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[dropcap]S[/dropcap]elf-service Business Intelligence is the buzzword everyone's talking about today. It gives modern business users the ability to find unique insights from their data without any hassle. Amidst a myriad of BI tools and platforms out there in the market, Microsoft’s Power BI has emerged as a powerful, all-encompassing BI solution - empowering users to tailor and manage Business Intelligence to suit their unique needs and scenarios. [author title="Brett Powell"]A Microsoft Power BI partner, and the founder and owner of Frontline Analytics LLC., a BI and analytics research and consulting firm. Brett has contributed to the design and development of Microsoft BI stack and Power BI solutions of diverse scale and complexity across the retail, manufacturing, financial, and services industries. He regularly blogs about the latest happenings in Microsoft BI and Power BI features at Insight Quest. He is also an organizer of the Boston BI User Group.[/author]   In this two part interview Brett talks about his new book, Microsoft Power BI Cookbook, and shares his insights and expertise in the area of BI and data analytics with a partciular focus on Power BI. In part one of the interview, Brett shared his views on topics ranging from what it takes to be successful in the field of BI & data analytics to why he thinks Microsoft is going to lead the way in shaping the future of the BI landscape. Today in part two, he shares his expertise with us on the unique features that differentiate Power BI from other tools and platforms in the BI space. Key Takeaways Ease of deployment across multiple platforms, efficient data-driven insights, ease of use and support for a data-driven corporate culture is what defines an ideal Business Intelligence solution for enterprises. Power BI leads in self-service BI because it’s the first Software as a Service (SaaS) platform to offer ‘End User BI’ in which anyone, not just a business analyst, can leverage powerful tools to obtain greater value from data. Microsoft Power BI has been identified as a leader in Gartner’s Magic Quadrant for BI and Analytics platforms, and provides a visually rich and easy to access interface that modern business users require. You can isolate report authoring from dataset development in Power BI, or quickly scale up or down a Power BI dataset as per your needs. Power BI is much more than just a tool for reports and dashboards. With a thorough understanding of the query and analytical engines of Power BI, users can customize more powerful and sustainable BI solutions. Part Two: Interview Excerpts - Power BI from a Worm’s Eye View How long have you been a Microsoft Power BI user? How have you been using Power BI on a day-to-day basis? What other tools do you generally end up using alongside Power BI for your work? I’ve been using Power BI from the beginning when it was merely an add-in for Excel 2010. Back then, there was no cloud service and Microsoft BI was significantly tethered to SharePoint but the fundamentals of the Tabular data modelling engine and programming language of DAX was available in Excel to build personal and team solutions. On a day-to-day basis I regularly work with Power BI datasets – that is, the analytical data models inside of Power BI Desktop files. I also work with Power BI report authoring and visualization features and with various data sources for Power BI such as SQL Server. From Learning to Mastering Power BI For someone just starting out using Power BI, what would your recommended learning plan be? For existing users, what does the road to mastering Microsoft Power BI look like? When you’re just starting out I’d recommend learning the essentials of the Power BI architecture and how the components (Power BI service, Power BI Desktop, On-Premises Data Gateway, Power BI Mobile, etc) work together. A sound knowledge on the differences between datasets, reports, and dashboards is essential and an understanding of app workspaces and apps is strongly recommended as this is the future of Power BI content management and distribution. In terms of a learning path you should consider what your role will be on Power BI projects – will you be administering Power BI, creating reports and dashboards, or building and managing datasets? Each of these roles has their own skills, technologies and processes to learn. For example, if you’re going to be designing datasets, a solid understanding of the DAX language and filter context is essential and knowledge of M queries and data access is very important as well. The road to mastering Power BI, in my view, involves a deep understanding of both the M and DAX languages in addition to knowledge of Power BI’s content management, delivery, and administration processes and features. You need to be able to contribute to the full lifecycle of Power BI projects and help guide the adoption of Power BI across an organization. The most difficult or ‘tricky’ aspect of Power BI is thinking of M and DAX functions and patterns in the context of DirectQuery and Import mode datasets. For example, certain code or design patterns which are perfectly appropriate for import models are not suitable for DirectQuery models. A deep understanding of the tradeoffs and use cases for DirectQuery versus default Import (in-memory) mode and the ability to design datasets accordingly is a top characteristic of a Power BI master. 5+ interesting things (you probably didn’t know) about Power BI What are some things that users may not have known about Power BI or what it could do? Can readers look forward to learning to do some of them from your upcoming book: Microsoft Power BI Cookbook? The great majority of learning tutorials and documentation on Power BI involves the graphical interfaces that help you get started with Power BI. Likewise, when most people think of Power BI they almost exclusively think of data visualizations in reports and dashboards – they don’t think of the data layer. While these features are great and professional Power BI developers can take advantage of them, the more powerful and sustainable Power BI solutions require some level of customization and can only be delivered via knowledge of the query and analytical engines of Power BI. Readers of the Power BI Cookbook can look forward to a broad mix of relatively simple to implement tips on usability such as providing an intuitive Fields list for users to more complex yet powerful examples of data transformations, embedded analytics, and dynamic filter behaviours such as with Row-level security models. Each chapter contains granular details on core Power BI features but also highlights synergies available by integrating features within a solution such as taking advantage of an M query expression, a SQL statement, or a DAX metric in the context of a report or dashboard. What are the 3 most striking features that make you love to work with Power BI? What are 3 aspects you would like improved? The most striking feature for me is the ability to isolate report authoring from dataset development. With Power BI you can easily implement a change to a dataset such as a new metric and many report authors can then leverage that change in their visualizations and dashboards as their reports are connected to the published version of the dataset in the Power BI service. A second striking feature is the ‘Query Folding’ of the M query engine. I can write or enhance an M query such that a SQL statement is generated to take advantage of the data source system’s query processing resources. A third striking feature is the ability to quickly scale up or down a Power BI dataset via the dedicated hardware available with Power BI Premium. With Power BI Premium, free users (users without a Pro License) are now able to access Power BI reports and dashboards. The three aspects I’d like to see improved include the following: Currently we don’t have IntelliSense and other common development features when writing M queries. Currently we don’t have display folders for Power BI datasets thus we have to work around this with larger, more complex datasets to maintain a simple user interface. Currently we don’t have Perspectives, a feature of SSAS, that would allow us to define a view of a Power BI dataset such that users don’t see other parts of a data model not relevant to their needs. Is the latest Microsoft Power BI update a significant improvement over the previous version? Any specific new features you’d like to highlight? Absolutely. The September update included a Drillthrough feature that, if configured correctly, enables users to quickly access the crucial details associated with values on their reports such as an individual vendor or a product. Additionally, there was a significant update to Report Themes which provides organizations with more control to define standard, consistent report formatting. Drillthrough is so important that an example of this feature was added to the Power BI Cookbook. Additionally, Power BI usage reporting including the identity of the individual user accessing Power BI content was recently released and this too was included in the Power BI Cookbook. Finally, I believe the new Ribbon Chart will be used extensively as a superior alternative to stacked column charts. Can you tell us a little about the new 'time storyteller custom visual' feature in Power BI? The Timeline Storyteller custom visual was developed by the Storytelling with Data group within Microsoft Research. Though it’s available for inclusion in Power BI reports via the Office Store like other custom visuals, it’s more like a storytelling design environment than a single visual given its extensive configuration options for timeline representations, scales, layouts, filtering and annotations. Like the inherent advantages of geospatial visuals, the linking of Visio diagrams with related Power BI datasets can intuitively call out bottlenecks and otherwise difficult-to-detect relationships within processes. 7 reasons to choose Power BI for building enterprise BI solutions Where does Power BI fall within Microsoft's mission to empower every person and every organization on the planet to achieve more of 1. Bringing people together 2. Living smarter 3. Friction free creativity 4. Fluid mobility? Power BI Desktop is available for free and is enhanced each month with features that empower the user to do more and which remove technical obstacles. Similarly, with no knowledge whatsoever of the underlying technology or solution, a business user can access a Power BI app on their phone or PC and easily view and interact with data relevant to their role. Importantly for business analysts and information workers, Power BI acknowledges the scarcity of BI and analytics resources (ie data scientists, BI developers) and thus provides both graphical interfaces as well as full programming capabilities right into Power BI Desktop. This makes it feasible and often painless to quickly create a working, valuable solution with relatively little experience with the product. We can expect Power BI to support 10GB (and then larger) datasets soon as well as improve its ‘data storytelling’ capabilities with a feature called Bookmarks. In effect, Bookmarks will allow Power BI reports to become like PowerPoint presentations with animation. Organizations will also have greater control over how they utilize the v-Cores they purchase as part of Power BI Premium. This will make scaling Power BI deployments easier and more flexible. I’m personally most interested in the incremental refresh feature identified on the Power BI Premium Roadmap. Currently an entire Power BI dataset (in import mode) is refreshed and this is a primary barrier to deploying larger Power BI datasets. Additionally (though not exclusively by any means), the ability to ‘write’ from Power BI to source applications is also a highly anticipated feature on the Power BI Roadmap. How does your book, Microsoft Power BI Cookbook, prepare its readers to be industry ready? What are the key takeaways for readers from this book? Power BI is built with proven, industry leading BI technologies and architectures such as in-memory, columnar compressed data stores and functional query and analytical programming languages. Readers of the Power BI Cookbook will likely be able to quickly deliver fresh solutions or propose ideas for enhancements to existing Power BI projects. Additionally, particularly for BI developers, the skills and techniques demonstrated in the Power BI Cookbook will generally be applicable across the Microsoft BI stack such as in SQL Server Analysis Services Tabular projects and the Power BI Report Server. A primary takeaway from this book is that Power BI is much more than a report authoring or visualization tool. The data transformation and modelling capabilities of Power BI, particularly combined with Power BI Premium capacity and licensing considerations, are robust and scalable. Readers will quickly learn that though certain Power BI features are available in Excel and though Excel can be an important part of Power BI solutions from a BI consumption standpoint, there are massive advantages of Power BI relative to Excel. Therefore, almost all PowerPivot and Power Query for Excel content can and should be migrated to Power BI Desktop. An additional takeaway is the breadth of project types and scenarios that Power BI can support. You can design a corporate BI solution with a Power BI dataset to support hundreds of users across multiple teams but you can also build a tightly focused solution such as monitoring system resources or documenting the contents of a dataset. If you enjoyed this interview, check out Brett’s latest book, Microsoft Power BI Cookbook. Also, read part one of the interview here to see how and where Power BI fits into the BI landscape and what it takes to stay successful in this industry.

Security is an important aspect for organizations and securing the devices that contain confidential data--personal or professional, is absolutely essential. Microsoft Enterprise Mobility + Security, an intelligent mobility management and security platform, offers a suite of services that helps in securing employee devices; thus, protecting and securing the organization. Our recent chat with Nate Chamberlain, a Business Analyst at DH Pace, Kansas, helped us understand more about the Microsoft 365 Enterprise Mobility + Security suite. Nate is also a Microsoft MVP in Office apps and services. His recently published book Microsoft 365 Mobility and Security – Exam Guide MS-101, helps users to plan, deploy, and manage Microsoft Office 365 services and gain the skills required to pass the MS-101 exam. In this interview, Nate also shares his favorite services from the suite, the importance of using Shadow IT in a controlled manner while ensuring security Cloud Apps, how the M365 Certified Enterprise Administrator Expert certification has helped him give a career boost, and much more. On the Microsoft Enterprise Mobility and Security suite Microsoft Enterprise Mobility and Security suite help professionals secure devices used within the enterprise and also helps in identifying breaches before they cause any major damage. The suite provides two offerings, Enterprise Mobility + Security E3 and E5. Talking about the popular Microsoft solutions in the suite, Nate said, “Azure Active Directory, Intune, and the Microsoft 365 Security & Compliance Center are big players in the overall EM+S suite. Taking the time to get to know each of them has the potential to significantly enhance your organization’s security.” Nate said one of his favorite features from the Microsoft 365 Enterprise Mobility + Security suite is, “the ability to be extremely granular in building conditional access policies. That, paired with the ability to utilize AI and zero-day security information in policies and practices, continually impresses me. It’ll be interesting to see where Endpoint Manager takes us.” On the topic of what features he would like to add in the suite in the future, he said, “the biggest improvement I would hope for currently is licensing simplification, and making sure admins are able to secure their organization and its users without breaking the budget.” On the new Microsoft Endpoint Manager and using ‘Shadow IT’ for Cloud App security Last month, Microsoft announced its new Endpoint Manager, a convergence of two of its popular tools, System Center Configuration Manager (ConfigMgr) and Microsoft Intune. Both ConfigMgr and Intune offer integrated cloud-powered management tools, and unique co-management options to provision, deploy, manage, and secure endpoints and applications across an organization. The Endpoint Manager offers end-to-end management solutions without the need for worrying too much about the complexity involved during migration, thus helping customers in a smooth cloud transition. According to Nate, “Microsoft Endpoint Manager takes a lot of the licensing guesswork out of building a secure solution for your organization.”  In addition to Intune and ConfigMgr, Microsoft Endpoint Manager includes the Device Management Admin Center (DMAC) and Desktop Analytics. Nate further adds that Microsoft Endpoint Manager includes nearly everything discussed in his exam prep book, Microsoft 365 Mobility and Security – Exam Guide MS-101--including Intune and ConfigMgr. Shadow IT and cloud app security In his book, Nate has written about controlling the use of ‘Shadow IT’ for Cloud App security. Shadow IT, also known as Stealth IT, is built and used without the knowledge of the IT or security group within the organization. We asked Nate for what processes is Shadow IT built-in the organizations. We also asked why Shadow IT is a threat and how organizations can minimize its usage. Clearing the clouds on Shadow IT, Nate explains, “Shadow IT is often a consequence of being too restrictive without providing alternative means of productivity and collaboration solutions. And sometimes, even if you provide alternatives or company-licensed tools, it’s the lack of ongoing education that failed to spread awareness and competency that led users to more familiar, comfortable means of accomplishing goals. When users need to accomplish something, they’ll find a way with or without the organization’s assistance.” He further adds, “It’s IT administrators’ responsibility to make sure productivity and collaboration solutions are provisioned and configured for secure, appropriate usage, and that education is provided to get users on board.” A few key takeaways from Nate’s book, an MS-101 exam guide, and his recommendations for further Microsoft 365 certifications According to Microsoft’s official website, the “Exam MS-101: Microsoft 365 Mobility and Security”, the skills measured in the exam includes, implementing modern device services, implementing Microsoft 365 security and threat management, and managing Microsoft 365 governance and compliance. These skills would help companies sieve through all the candidates among others who don’t know much about the suite. Talking about the key takeaways from his book, Nate says, “I hope readers find the content to be challenging, but accessible. The best takeaway I could hope for is that readers retain information that not only helps them in the exam but in their jobs. The whole point of taking exams and obtaining certifications is to demonstrate proficiency, knowledge, and skill. Ultimately, it’s practising those skills in the real world that matter - not the score on the exam. But the exam is absolutely a first step toward building confidence and career growth.” We also asked him what other certifications he would recommend next, to which Nate said, “Once readers pass MS-101, they should aim for passing MS-100 if they haven’t already. After that, they’re just one prerequisite certification away from becoming a Microsoft 365 Enterprise Administrator Expert.” On Nate’s journey as a Microsoft SharePoint Systems Engineer and beyond Nate worked as a SharePoint Systems Engineer at LMH Health, Kansas, and is currently a Business Analyst at DH Pace in Olathe, KS. He is also an M365 Certified Enterprise Administrator Expert. He shared why certifications are important for career growth. He says, “My certification certainly looks great on my resume to potential employers and I like to think it’s part of what made me competitive in pursuing my current role. Certifications are verified proof of skill and competency. It alleviates some risk a company would otherwise assume in hiring someone for highly technical work like we find in our industry.” He also shared about his journey and how learning SharePoint transformed his role. “My journey has been one of self-teaching, fueled by inspiring tech solutions coming out of Microsoft. I was once tasked to learn what I can about SharePoint, at the University of Kansas, and it turned into a SharePoint-specific role there. That opened doors for me which brought me to LMH Health and ultimately DH Pace.” He continues, “Somewhere along the way, I started sharing what I was learning via my blog, NateChamberlain.com, and by speaking at conferences around the country regularly. I also started a SharePoint user group, LSPUG, in Lawrence, KS. For these reasons and perhaps others, I was awarded the Microsoft MVP for Office Apps and Services.” Certifications are indeed verified proof of skill and competency. So go ahead and check out Nate’s book, Microsoft 365 Mobility and Security – Exam Guide MS-101, to get up to speed with planning, deploying, and managing Microsoft Office 365 services and gain the skills you need to pass the MS-101 exam. With this book, you’ll explore everything from mobile device management and compliance, through to data governance and auditing. By the end of this book, you’ll have learned to work with Microsoft 365 services and covered the concepts and techniques you need to know to pass the MS-101 exam. Written in a succinct way, you’ll explore chapter-wise self-assessment questions, exam tips and mock exams with answers. Microsoft technology evangelist Matthew Weston on how Microsoft PowerApps is democratizing app development [Interview] How PyTorch is bridging the gap between research and production at Facebook: PyTorch team at F8 conference SOLIDWORKS specialist Tayseer Almattar takes us into the world of 3D modeling using SOLIDWORKS 2020 [Interview]

Netflix, the global poster child for streamed TV and the use of Big Data to inform the programs they develop, has shown steady customer growth for several years now. Recently, the company revealed that it would be shutting down the user reviews which have been so prominent in their media catalogue interface for so long. In the background, media and telco are merging. AT&T, the telco which undertook the biggest deal in history recently, acquired Time and wants HBO to become like Netflix. Telia, a Finnish telecommunications company bought Bonnier Broadcasting in late July 2018. The video content landscape has changed a great deal in the last decade. Everyone in the entertainment game wants to move beyond broadcast TV and to use data to develop content their users will love and which will give their customer base more variety. This means they can look to data to charge higher subscription rates per user, experiment with tiered subscriptions, decide to localize global content, globalize local content and more. These changes raise two key questions. First, are we heading for a world in which AI and ML based algorithms drive what we watch on TV? And second, are the days of human recommendation being quietly replaced by machine recommendations over which the user has no control? [caption id="attachment_21726" align="aligncenter" width="1392"] As you know, Netflix is acquiring customers fast.[/caption] Source: Statista To get an insider’s view on the answer to those questions, I sat down with Matt Jones of OVO Mobile, one of Australia’s fastest growing telecommunications companies. OVO offer their customers a unique point of difference – streaming video sports content, included in a phone plan. OVO has bought the rights to a number of niche sports in Australia which weren’t previously available and now offer free OTA (Over the Air) digital content for fans of ‘unusual’ sports like Drag Racing or Gymnastics. OTA content is anything delivered to a user’s phone over a wireless network. In OVO’s case, the data used to transport the video content they provide to their users is free. That means customers don’t have to worry about paying more for mobile data so they can watch it – a key concern for users. OVO Mobile and Netflix are in very similar businesses – and Matt has a unique point of view about how Artificial Intelligence and Machine Learning will impact the world of telco and media. Key takeaways What’s changed our media consumption habits: the ubiquitous mobile internet, the always on and connected younger generation, better mobile hardware, improved network performance and capabilities, need for control over content choices. Digitization allows new features –some of which that people have proven to love - binge watching, screening out advert breaks and time shifting. The key to understanding the value of ML and AI is not in understanding the statistical or technical models that are used to enable it, it’s the way AI is used to improve the customer experience your digital customers are having with you. The use of AI in digital/app experience has changed in a way to personalize what users can see which old media could not offer. Content producers use the information they have on us, about the programs we watch, when we watch them and for how long we watch to Contribution of AI / ML towards the delivery of online media is endless in terms of personalisation, context awareness, notification management etc. Social acceptance of media delivered to users on mobile phones is what’s driving change A number of overlapping factors are driving changes in how we engage with content. Social acceptance of the internet and mobile access to it as a core part of life is one key enabler. From a technology perspective, things have changed too. Smartphones now have bigger, higher resolution screens than ever before – and they’re with us all the time. Jones believes this change is part of a cultural evolution in how we relate to technology. He says, “There has also been a generational shift which has taken place. Younger people are used to the small screen being the primary device. They’re all about control, seeking out their interests and consuming these, as opposed to previous generations which was used to mass content distribution from traditional channels like TV.” Other factors include network performance and capability which has improved dramatically in recent years. Data speeds have grown exponentially from 3G networks – launched less than 15 years ago, which could support stuttered low resolution video to 4G and 4.5G enabled networks. These can now support live streaming of High Definition TV. Mobile data allowances in plans and offers from some phone companies to provide some content ‘data free’ (as OVO does with theirs) have also driven uptake. Finally, people want convenience and digital offers that in a way people have never experienced before. Digitization allows new features –some of which that people have proven to love - binge watching, screening out advert breaks and time shifting. What part can AI / machine learning play in the delivery of media online? Artificial Intelligence (AI) is already part of 85% of our online interactions. Gartner suggest, it will be part of every product in the future. The key to understanding the value of ML and AI is not in understanding the statistical or technical models that are used to enable it, it’s the way AI is used to improve the customer experience your digital customers are having with you. When you find a new band in Spotify, when YouTube recommends a funny video you’ll like, when Amazon show you other products that you might like to consider alongside the one you just put in to your basket, that’s AI working to improve your experience. “Over The Top content is exploding. Content owners are going direct to consumer and providing fantastic experiences for their users. What’s changing is the use of AI in digital / app experiences to personalize what users see in ways old media never could.” Says Matt. Matt’s video content recommendation app, for example, ‘learns’ not just what you like to watch but also the times you are most likely to watch it. It then prompts users with a short video to entice them to watch. And the analytics available show just how effective it is. Matt’s app can be up to 5 times more successful at encouraging customers to watch his content, than those who don’t use it. “The list of ways that AI / ML contributes to the delivery of media online is endless. Personalisation, context awareness, notification management …. Endless” By offering users recommendations on content they’ll love, producers can now engage more customers for longer. Content producers use the information they have on us, about the programs we watch, when we watch them and for how long we watch to: Personalise at volume: Apps used to deliver content can personalise what’s shown first to users, based on a number of variables known about them, including the sort of context awareness that can be relatively easy to find on mobile devices. Ultimately, every AI customer experience improvement (including the examples that follow) are all designed to automate the process of providing something special to each individual that they uniquely want. Automation means that can be done at scale, with every customer treated uniquely. Notification management: AI that tracks the success of notifications and acknowledges, critically, when they are not helpful to the user, can be employed to alert users only about things they want to know. These AI solutions provide updates to users based on their preferences and avoid the provision of irrelevant information. Content discovery & Re- engagement: AI and ML can be used in the provision of recommendations as to what users could watch, which expose customers to content they would not otherwise find, but which they are likely to value. Better / more relevant advertising: Advertising which targets a legitimately held, real, customer need is actually useful to viewers. Better analytics for AI can assist in targeting micro segments with ads which contain information customers will value. Lattice, is a Business Insights tool provider. Their ‘Lattice Engine’ product combined information held in multiple cloud based locations and uses AI to automatically assign customers to a segment which suits them. Those data are then provided to a customer’s eCommerce site and other channel interactions, and used to offer content which will help them convert better. Developing better segments: Raw data on real customers can be gathered from digital content systems to inform Above The Line marketing in the real, non digital world. Big data analytics can now be used with accurate segmentation for local area marketing and to tie together digital and retail customer experiences. McKinsey suggest that 36% of companies are actively pursuing strategies, driven from their Big Data reserves. They advise their clients that Big Data can be used to better understand and grow Customer Lifetime Values. In the future - Deep linking for calls-to-action: Some digital content is provided in a form such that viewers can find out more information about an item on screen. Providing a way to deep link from a video screen in to a shopping cart prepopulated with something just seen on screen is an exciting possibility for the future. Cutting steps out of the buying process to make it easier for eCommerce users to transact from within content apps to buying a product they’ve seen on the screen is likely to become a big business. Deep linking raises the value of the content shown to the degree it raise the sales of the products included. Bringing it all together Jones believes those that invest big in AI and machine learning, and of them, those who find a way to draw out insights and act upon them, will be the ultimate victors. “The big winners are going to be the people who connect a fan with content they love and use AI and ML to deliver the best possible experience. It’s about using all the information you have about your users and acting on them.” Said Jones. That commercial incentive is already driving behavior. AI and ML drive already provide personalized content recommendations. Progressive content companies, including Matt’s, are already working on building AI in to every facet of every Digital experience you have. As to whether AI is entirely replacing social media influence, I don’t think that’s the case. The research says people are still 4 times more likely to watch a video if it is recommended to them by a friend. Reviews have always been important to presales on the internet and that applies to TV shows, too. People want to know what real users felt when they used a product. If they can’t get reviews from Netflix, they will simply open a new tab and google for reviews in that while they are thinking of how to find something to watch on Netflix. About Matt Jones, Matt is an industry disruptor, launching the first of its kind Media and Telco brand OVO Mobile in 2015, Matt is the driving force behind convergence of new media & telco – by bringing together Telecommunications with Media Rights and digital broadcast for mass distribution. OVO is a new type of Telco, delivering content that fans are passionate about, streamed live on their mobile or tablet UNLIMITED & data free. OVO has secured exclusive 3 year+ digital broadcast and distribution rights for a range of content owners including Supercars, World Superbikes, 400 Thunder Drag Series, Audi Australia Racing & Gymnastics Australia – with a combined Australian audience estimated at over 7 Million. OVO is a multi-award winner, including winning the Money Magazine Best of the Best Award 2017 for high usage, as well as featuring on A Current Affair, Sunrise, The Today Show, Channel 7 News, Channel 9 News and multiple radio shows for their world-first kids’ mobile phone plan with built-in cyber security protection. As OVO CEO, Matt was nominated for Start-Up Executive of the Year at the CEO Magazine Awards 2017 and was awarded runner-up. The Award recognises the achievements of leaders and professionals, and the contributions they have made to their companies across industry-specific categories. Matt holds a Bachelor of Arts (BA) from the University of Tasmania and regularly speaks at Telco, Sports Marketing and Media forums and events. Matt has held executive leadership roles at leading Telecommunications brands including Telstra (Head of Strategy – Operations), Optus, Vodafone, AAPT, Telecom New Zealand as well as global Management Consulting firms including BearingPoint. Matt lives on the northern beaches of Sydney with his wife Mel and daughters Charlotte and Lucy. How to earn $1m per year? Hint: Learn machine learning We must change how we think about AI, urge AI founding fathers Alarming ways governments are using surveillance tech to watch you

Charlotte Jee is a journalist specialising in technology and politics, currently working as editor for Techworld. Charlotte has founded, hosted and moderated a range of events including The Techies awards and her own ‘Women in Tech Speak Up’ event series. In 2017, Charlotte set up Jeneo with the mission to give women and underrepresented people a voice at Tech events. Jeneo helps companies source great individuals to speak at their events, and encourage event organisers to source panellists from a range of backgrounds. We spoke to Charlotte in a live Twitter Q&A to find out a bit more about Jeneo and Charlotte’s experience as a woman in the tech sector. Packt: Why did you decide to set up Jeneo? CJ: Good question! Basically, blame manels (aka all-male panels). I got tired of going to tech events and not seeing even one woman speaking. It's symptomatic of a wider issue with diversity within tech. And I resolved to start to do something about it! Packt: What have been your most interesting findings from the research you've carried out into women at tech events? CJ: Interestingly, the ratio of women to men speakers varies hugely across different events. However, promisingly, I've found that those who have worked on this specific issue have successfully upped their number of women speakers. The general finding was that from the top 60 London tech events, women comprise about a quarter of the speakers. The full research hasn't concluded just yet – so stay tuned. Packt: How has the industry changed since you started in Tech? Do you think it is getting easier for women to get into the Tech world? CJ: To be honest, I don't think that much progress has been made since I started working in tech. However, in the last year with scandals at Uber, Google and VC sexual harassment, the industry is starting to finally really focus on making itself more welcoming for women. Packt: What do you think is the best part of being a woman in the tech industry? CJ: I think the answer is the same regardless of your gender – the tech industry is at the forefront of the latest innovations within society and it is constantly changing, so you never get bored. Packt: What do you think is the worst part of being a woman in the tech industry? CJ: To be clear, I believe the majority of workplaces within tech are perfectly welcoming to women – however for the persistent minority that aren't, women can face all sorts of discrimination, both subtle and unsubtle. Packt: What advice would you give to a woman considering a career in the tech industry? CJ: Go for it! I honestly can't think of a better industry to work in. You can pretty much guarantee you'll be able to find work no matter what, so long as you keep your skills up to date. Packt: In what ways – positive or negative ­– do you think our education system influences the number of women in tech? CJ: Good question. I actually think that it's in education that this problem starts – from the experts I've spoken to, it seems like our education system too often seems to actively discourage women from pursuing careers in tech. Packt: What has been your biggest success in your tech career so far? CJ: I was really honoured when my boss promoted me to TechWorld News editor in 2015. Recently, it'd have to be the Women in Tech Speak Up event for 300 people I organised (single-handedly) in August 2017. Also, creating this list of 348 women working in tech in the UK which in many ways kicked this all off. Packt: What advice would you give to help tech companies to help increase their gender diversity? CJ: Way too much for one tweet here. Get senior buy-in, look carefully at your hiring process, be clear on culture/working practices, collect and publish data, provide flexible working (men want this too!) – if you want to more detail please get in touch with me. Packt: What do you think the future looks like for women working in tech? CJ: I feel more optimistic now than I have at any point. I think there is a huge amount of desire within the industry to be more inclusive. However, it's translating that goodwill into action – that's what I'll be focusing on. Packt: Are there any tech companies doing awesome things to increase diversity in their business? CJ: There's some impressive work from Monzo at the moment, who are highlighting the need for a diverse and inclusive team that represents their user base. There are plenty of others doing good work too, including Amazon UK. Packt: Are there any particular women in tech who have inspired you and who should we be following on Twitter? CJ:How long do you have? So many: @emercoleman @kitterati @ChiOnwurah @NAUREENK @annkempster @cathywhite10 @carrie_loves_ @JeniT @lily_dart @annashipman @yoditstanton That is truly just for starters. And of course, I have to add, the original woman in tech who inspired me is my Mum, Jane Jee (@janeajee) – she's CEO of RegTech startup Kompli-Global Compliance (@kompliglobal). Plus, obviously everyone on this list. Packt: Do you have any tips or advice for women to get on panels or bag speaking slots at tech events? CJ: Start small if possible, build up your conference – don't be afraid to put yourself out there! My research has found speaker submissions overwhelmingly come from men, get proactive and contact events you'd like to speak at. Thanks for chatting with us, Charlotte! Find out more about Jeneo here and follow Charlotte on Twitter: @CharlotteJee.

Analytics is slowly changing the face of the sports industry as we know it. Data-driven insights are being used to improve the team and individual performance, and to get that all-important edge over the competition. But what exactly is sports analytics? And how is it being used? What better way to get answers to these questions than asking an expert himself! [author title="Gaurav Sundararaman"]A Senior Stats Analyst at ESPN currently based in Bangalore, India. With over 10 years of experience in the field of analytics, Gaurav worked as a Research Analyst and a consultant in the initial phase of his career. He then ventured into sports analytics in 2012, and played a major role in the Analytics division of SportsMechanics India Pvt. Ltd. where he was the Analytics Consultant for the T20 World Cup winning West Indies team in 2016.[/author]   In this interview, Gaurav takes us through the current landscape of sports analytics, and talks about how analytics is empowering better decision-making in sports. Key Takeaways Sports analytics pertains to finding actionable, useful insights from sports data which teams can use to gain competitive advantage over the opposition Instincts backed by data make on and off-field decisions more powerful and accurate Rise of IoT and wearable technology has boosted sports analytics. With more data available for analysis, insights can be unique and very helpful Analytics is being used in sports right from improving player performance to optimizing ticket prices and understanding fan sentiments Knowledge of  tools for data collection, analysis and visualization such as R, Python and Tableau is essential for a sports analyst Thorough understanding of the sport, up to date skillset and strong communication with players and management are equally important factors to perform efficient analytics Adoption of analytics within sports has been slow, but steady. More and more teams are now realizing the benefits of sports analytics and are adopting an analytics-based strategy Complete Interview Analytics today is finding widespread applications in almost every industry today - how has the sports industry changed over the years? What role is analytics playing in this transformation? The sports industry has been relatively late in adopting analytics. That said, the use of analytics in sports has also varied geographically. In the west, analytics plays a big role in helping teams, as well as individual athletes, take up decisions. Better infrastructure and a quick adoption of the latest trends in technology is an important factor here. Also, investment in sports starts from a very young age in the west, which also makes a huge difference.  In contrast, many countries in Asia are still lagging behind when it comes to adopting analytics, and still leverage on traditional techniques to solve problems. A combination of analytics with traditional knowledge from experience would go a long way in helping teams, players and businesses succeed. Previously the sports industry was a very close community. Now with the advent of analytics, the industry has managed to expand its horizon. We witness more non-sportsmen playing a major part in the decision making. They understand the dynamics of the sports business and how to use data-driven insights to influence the same. Many major teams across different sports such as Football (Soccer), Cricket, American Football, Basketball and more have realized the value of data and analytics. How are they using it? What advantages does analytics offer to them? One thing I firmly believe is that analytics can’t replace skills or can’t guarantee wins. What it can do is ensure there is logic towards certain plans and decisions. Instincts backed by data make the decisions more powerful. I always tell the coaches or players – Go with your gut and instincts as Plan A. If it does not work out your fall back could be Plan B based on trends and patterns derived from data. It turns out to be a win-win for both. Analytics offers a neutral perspective which sometimes players or coaches may not realize. Each sport has a unique way of applying analytics to make decisions and obviously, as analysts, we need to understand the context and map the relevant data. As far as using the analytics is concerned, the goals are pretty straightforward - be the best, beat the opponents and aim for sustained success. Analytics helps you achieve each of these objectives. The rise of IoT and wearable technology over the last few years has been incredible. How has it affected sports, and sports analytics, in particular? It is great to see that many companies are investing in such technologies. It is important to identify where wearables and IoT can be used in sport and where it can cause maximum impact. These devices allow in-game monitoring of players, their performance, and their current physical state. Also, I believe more than on-field, these technologies would be very useful in engaging fans as well. Data derived from these devices could be used in broadcasting as well as providing a good experience for fans in the stadiums. This will encourage more and more people to watch games in stadiums and not in the comfort of their homes. We have already seen a beginning with a few stadiums around the world leveraging technology (IoT). The Mercedes Benz stadium (home of Atlanta Falcons) has a high tech stadium powered by IBM. Sacramento is building a state-of-the-art facility for the Sacramento Kings. This is just the start, and it will only get better with time. How does one become a sports analyst? Are there any particular courses/certifications that one needs to complete in order to become one? Can you share with us your journey in sports analytics? To be honest there are no professional courses yet in India to become an Analyst. There are a couple of colleges which have just started offering Sports Analytics as a course in their Post-Graduation Program. However, there are a few companies (Sports Mechanics and Kadamba Technologies in Chennai) that offer jobs that can enable you to become a Sports Analyst if you are really good.  If you are a freelancer then my advice would be to ensure you brand yourself well and showcase your knowledge through social media platforms and get a breakthrough via contacts. Post my MBA, Sports Mechanics (a leader in this space), a company based in Chennai were looking for someone to work to start their data practice. I was just lucky to be at the right place at the right time. I worked for 4 years there and was able to learn a lot about the industry and what works and what does not. Being a small company, I was lucky to don multiple hats and work on different projects across the value chain. I moved and joined the lovely team Of ESPNCricinfo where I work for their stats team. What are the tools and frameworks that you use for your day to day tasks? How do they make your work easier? There are no specific tools or frameworks. It depends on the enterprise you are working for. Usually, they are proprietary tools of the company. Most of these tools are used either to collect, mine or visualize data. Interpreting the information and presenting it in a manner in which users understand is important and that is where certain applications or frameworks are used. However to be ready for the future it would be good to be skilled on tools that support data collection, analysis and visualization namely R, Python and Tableau, to name a few. Do sports analysts have to interact with players and the coaching staff directly? How do you communicate your insights and findings with the relevant stakeholders? Yes, they have to interact with players and management directly. If not, the impact will be minimal. Communicating insights is very important in this industry. Too much analysis could lead to paralysis. We need to identify what exactly each player or coach is looking for, based on their game and try to provide them the information in a crisp manner which helps them make decisions on and off the field. For each stakeholder the magnitude of the information provided is different. For the coach and management, the insights can be in detail while for the players we need to keep it short and to the point. The insights you generate must not only be limited to enhancing the performance of a team on the field but much more than that. Could you give us some examples? Insights can vary. For the management, it could deal with how to maximise the revenue or save some money in an auction. For coaches, it could help them know about his team’s as well as the opposition’s strengths and weaknesses from a different perspective. For captains, data could help in identifying some key strategies on the field. For example, in Cricket, it could help the captain determine which bowler to bring on to which opposition batsmen, or where to place the fielders. Off the field, one area where analytics could play a big role would be in grassroots development and tracking of an athlete from an early age to ensure he is prepared for the biggest stage. Monitoring performance, improving physical attributes by following a specific regimen, assessing injury record and designing specific training programs, etc. are some ways in which this could be done. What are some of the other challenges that you face in your day to day work? Growth in this industry can be slow sometimes. You need to be very patient, work hard and ensure you follow the sport very closely. There are not many analytical obstacles as such, but understanding the requirements and what exactly the data needs are can be quite a challenge. Despite all the buzz, there are quite a few sports teams and organizations who are still reluctant to adopt an analytics-based strategy – why do you think is that the case? What needs to change? The reason for the slow adoption could be the lack of successful case studies and the awareness. In most sports when so many decisions are taken on the field sometimes the players' ability and skill seems far more superior to anything else. As more instances of successful execution of data-based trends come up, we are likely to see more teams adopting the data-based strategy. Like I mentioned earlier, analytics needs to be used to make the coach and captain take the most logical and informed decisions. Decision-makers need to be aware of the way it is used and how much impact it can cause.  This awareness is vital towards increasing the adoption of analytics in sports. Where do you see sports analytics in the next 5-10 years? Today in sports many decisions are taken on gut feeling, and I believe there should be a balance. That is where analytics can help. In sports like Cricket, only around 30% of the data is used and there is more emphasis given to video. Meanwhile, if we look at Soccer or Basketball, the usage of data and video analytics is close to 60-70% of its potential. Through awareness and trying out new plans based on data, we can increase usage of analytics in cricket to 60-70 % in the next few years. Despite the current shortcomings, It is fair to say that there is a progressive and positive change at the grassroots level across the world. Data-based coaching and access to technology are slowly being made available to teams as well as budding sportsmen/women. Another positive is that the investment in the sports industry is growing steadily. I am confident that in a couple of years, we will see more job opportunities in sports. Maybe in five years, the entire ecosystem would be more structured and professional. We would witness analytics playing a much bigger role in helping stakeholders make informed decisions, as data-based insights become even more crucial. Lastly, what advice do you have for aspiring sports analysts? My only advice would be - Be passionate, build a strong network of people around you, and constantly be on the lookout for opportunities. Also, it is important to keep updating your skill-set in terms of the tools and techniques needed to perform efficient and faster analytics. Newer and better tools keep coming up very quickly, which make your work easier and faster. Be on the lookout for such tools! One also needs to identify their own niche based on their strengths and try to build on that. The industry is on the cusp of growth and as budding analysts, we need to be prepared to take off when the industry matures. Build your brand and talk to more people in the industry - figure out what you want to do to keep yourself in the best position to grow with the industry.

SAP, the market-leading enterprise software, recently became the first European technology company to create an AI ethics advisory panel where they announced seven guiding principles for AI development. These guidelines revolve around recognizing AI’s significant impact on people and society. Also, last week, at the Microsoft Ignite conference, SAP, in collaboration with Microsoft and Adobe announced the Open Data Initiative. This initiative aims to help companies to better govern their data and support privacy and security initiatives. For SAP, this initiative will further bring advancements to its SAP C/4HANA and S/4HANA platforms. All of these actions emphasize SAP’s focus on transforming itself into a responsible data use company. We recently interviewed Rehan Zaidi, a senior SAP ABAP consultant. Rehan became one of the youngest authors on SAP worldwide when he was published in the prestigious SAP Professional Journal in the year 2001. He has written a number of books, and over 20 articles and professional papers for the SAP Professional Journal USA and HR Expert USA, part of the prestigious sapexperts.com library. Following are some of his views on the SAP community and products and how the SAP suite can benefit people including budding professionals, developers, and business professionals. Key takeaways SAP HANA was introduced to accelerate jobs 200 times faster while maintaining the efficiency. The introduction of SAP Leonardo brought in the next wave of AI, machine learning, and blockchain services via the SAP cloud platform and other standalone projects. Experienced ABAP developers should look forward to getting certified in one of the newest technologies such as HANA, and Fiori. SAP ERP Central Component (SAP ECC) is the on-premises version of SAP, and it is usually implemented in medium and large-sized companies. For smaller companies, SAP offers its Business One ERP platform. SAP Fiori is a line of SAP apps meant to address criticisms of SAP's user experience and UI complexity. Q.1. SAP is one of the most widely used ERP tools. How has it evolved over the past few years from the traditional on-premise model to keep up with the cloud-based trends? Yes. Let me cover the main points. SAP started in 1973 as a company and the first product SAP R/98 was launched. In 1979, SAP launched the R/2 design. It had most of the typical processes such as accounting, manufacturing processes, supply chain logistics, and human resources. Then came R/3  that brought the more efficient three-tier (Application server -  Database and the presentation (GUI)) architecture, with more new modules and functionalities added. It was a smart system fully configurable by functional consultants. This was further enhanced with Netweaver capability that brought the integration of the internet and SOA capability.  SAP introduced the ECC 5 and subsequently the ECC 6 Release. Mobility was later added that lets mobile applications running on devices to access the business processes in SAP and execute them. Both display and updation of SAP data was possible. HANA system was then introduced. It is very fast and efficient - allows you to do 200 times faster jobs than before Cloud systems then became available that let customers connect to SAP Cloud Platform via their on-premise systems and then get access to services such as Mobile Service for app protection, Mobile Service for SAP Fiori, among others. SAP Leonardo was finally introduced, as a way of bringing in next-gen AI, machine learning and blockchain services via standalone projects and the SAP cloud platform. Q.2. Being a Senior ABAP Programming Analyst, how does your typical day look like? Ahh. Well, a typical day! No two days are the same for us. Each morning we find ourselves confronting a problem whose solution is to be devised. A different problem every day- followed by a unique solution. We spend hours and hours finding issues in custom developed programs. We learn about making custom programs run faster. We get requirements of a wide variety of users. They may be in the Human Resource, Materials Management, Sales and Distribution or Finance, and so on. This requirement may be pertaining to an entirely new report or a dialog program having a set of screens. We even do Fiori ( using Javascript based library) applications that may be accessible from the PC or a mobile device. I even get requirements of teaching junior or trainee SAP developers on a wide variety of technologies. Q.3. Can you tell us about the learning curve for SAP? There are different job profiles related to SAP which range from executives to consultants and managers. How do each of them learn or update themselves on SAP? Yes, this is a very important question. A simple answer to this question is that “there is no end to learning and at any stage, learning is never enough,” no matter to which field within SAP you belong to. Things are constantly changing. The more you read and the more you work, you feel that there is a lot to be done. You need to constantly update yourself and learn about new technologies. There is plenty of material available on the internet. I usually refer to the Official SAP website for newer courses available. They even tell you for which background (managers, developers) the courses are relevant to. I also go to open.sap.com for new courses. Whether they are consultants (functional and technical), or managers, all of them need to keep themselves up-to-date. They must take new courses and learn about innovation in their technology. For example, HR must now study and try to learn about Successfactors. Even integration of SAP HANA with other software might be an interesting topic of today. There are Fiori and HANA related courses for Basis consultants and the corresponding tracks for developers. Some knowhow of newer technologies is also important for managers and executives, since your decisions may need to be adapted based on the underlying technologies running in your systems. You should know the pros and cons of all technologies in order to make the correct move for your business. Q.4. Many believe an SAP certification improves their chances of getting jobs at competitive salaries. How important are certifications? Which SAP certifications should a buddying developer look forward to obtain? When I did my Certification in October 2000, I used to think that Certifications are not important. But now I have realized, yes, it makes a difference.  Well, certifications are definitely a plus point. They enhance your CV and allow you to have an edge over those who are not certified.  I found some jobs adverts that specifically mention that certification will be required or will be advantageous. However, they are only useful when you have at least 4 years of experience. For a fresh graduate, a certification might not be very useful. A useful SAP consultant/developer is a combination of solid base/foundation of knowledge along with a touch of experience. I suggest all my juniors to go for Certifications in order to strengthen concepts, which include: C_C4C30_1711 - SAP Certified Development Associate – SAP Hybris Cloud for Customer C_CP_11 - SAP Certified Development Associate - SAP Cloud Platform C_FIORDEV_20 - SAP Certified Development Associate - SAP Fiori Application Developer C_HANADEV_13 - SAP Certified Development Associate - SAP HANA C_SMPNHB_30 - SAP Certified Development Associate - SAP Mobile Platform Application Development (SMP 3.0) C_TAW12_750 - SAP Certified Development Associate - ABAP with SAP NetWeaver 7.50 E_HANAAW_12 - SAP Certified Development Specialist - ABAP for SAP HANA For experienced ABAP developers, I suggest getting certified on the newest technologies such as HANA, and Fiori. They may help you get a project quicker and/or at a better rate than others. Q.5. The present buzz is around AI, machine learning, IoT, Big data, and many other emerging technologies. SAP Leonardo works on making it easy to create frameworks for harnessing the latest tech. What are your thoughts on SAP Leonardo? Leonardo is SAP’s response to an AI platform. It should be an important part of SAP’s offerings, mostly built on the SAP cloud platform. SAP has relaunched Leonardo as a digital innovation system. As I understand it, Leonardo allows customers to take advantage of artificial intelligence (AI), machine learning, advanced analytics and blockchain on their company’s data. SAP gives customers an efficient way of using these technologies to solve business issues. It allows you to build a system which, in conjunction with machine learning, searches for results that can be combined with SAP transactions. The benefit with SAP Leonardo is that all the company’s data is available right in the SAP system. Using Leonardo, you have access to all human resources data and any other module data residing in the ERP system. Any company from any industry can make use of Leonardo; it works equally well for retailers, food and beverage companies and medical industries, for organizations working in retail, manufacturing and automotive. An approach that works for one company in a given industry can be applied to other companies in that industry. Suppose a company operates sensors. They can link the sensor data with the data in their SAP systems and even link that with other data, and they can then use the Leonardo capabilities to solve problems or optimize performance. When a problem for one company in an industry is solved, a similar solution may be applied to the entire industry. Yes, in my opinion, Leonardo has a bright future and should be successful. For more information about Leonardo success stories, I encourage readers to check out SAP Leonardo Internet of Things Portfolio & Success Stories. Q. 6. You are currently writing a book on ABAP Objects and Design Patterns expected to be published by the end of 2018. What was your motivation behind writing it? Can you tell us more about ABAP objects? What should readers expect from this book? ABAP and ABAP Objects has gone tremendous changes since some time both on the features (and capability) as well as the syntax. It is the most unsung topic of today. It has been there for quite long but most developers are not aware of it or are not comfortable enough to use them in their day to day work. ABAP is a vast community with developers working in a variety of functional areas. The concepts covered in the book will be generic, allowing the learner to apply them to his or her particular area. This book will cover ABAP objects (the object-oriented extension of the SAP language ABAP) in the latest release of SAP NetWeaver 7.5 and explain the newest advancements. It will start with the programming of objects in general and the basics of ABAP language the developer needs to know to get started. The book will cover the most important topics needed on everyday support jobs and for succeeding in projects. The book will be goal-directed, not a collection of theoretical topics. It won’t just touch on the surface of ABAP objects, but will go in depth from building the basic foundation (e.g., classes and objects created locally and globally) to the intermediary areas (e.g., ALV programming, method chaining, polymorphism, simple and nested interfaces), and then finally into the advanced topics (e.g., shared memory, persistent Objects). The best practices for making better programs via ABAP objects will be shown at the end. No long stories, no boring theory, only pure technical concepts followed by simple examples using coding pertaining to football players. Everything will be presented in a clear, interesting manner, and readers will learn tips and tricks they can apply immediately. Learners, students, new SAP programmers and SAP developers with some experience can use this as an alternative to expensive training books. The book will also save reader’s time searching the internet for help writing new programs. Knowing ABAP objects is key for ABAP developers these days to move forward. Starting from simple ALV reporting requirements, or defining and catching exceptional situations that may occur in a program or even the enhancement technology of BAdIs that lets you enhance standard SAP applications require sound ABAP Objects understanding. In addition, Web Dynpro application development, the Business Object Processing Framework, and even OData service creation to expose data that can be used by Fiori apps all demand solid knowledge of ABAP objects. How to perform predictive forecasting in SAP Analytics Cloud Popular Data sources and models in SAP Analytics Cloud Understanding Text Search and Hierarchies in SAP HANA  

IBM’s SPSS Modeler provides a powerful, versatile workbench that allows you to build efficient and accurate predictive models in no time. What else separates IBM SPSS Modeler from other enterprise analytics tools out there today? To know just that, we talk to arguably two of the most popular members of the SPSS community. [box type="shadow" align="" class="" width=""] Keith McCormick Keith is a career-long practitioner of predictive analytics and data science, has been engaged in statistical modeling, data mining, and mentoring others in this area for more than 20 years. He is also a consultant, an established author, and a speaker. Although his consulting work is not restricted to any one tool, his writing and speaking have made him particularly well known in the IBM SPSS Statistics and IBM SPSS Modeler communities. Jesus Salcedo Jesus is an independent statistical consultant and has been using SPSS products for over 20 years. With a Ph.D., in Psychometrics from Fordham University, he is a former SPSS Curriculum Team Lead and Senior Education Specialist, and has developed numerous SPSS learning courses and trained thousands of users.[/box] In this interview with Packt, Keith and Jesus give us more insights on the Modeler as a tool, the different functionalities it offers, and how to get the most out of it for all your data mining and analytics needs. Key Interview Takeaways IBM SPSS Modeler is easy to get started with but can be a tricky tool to master Knowing your business, your dataset and what algorithms you are going to apply are some key factors to consider before building your analytics solution with SPSS Modeler SPSS Modeler’s scripting language is Python, and the tool has support for running R code IBM SPSS Modeler Essentials helps you effectively learn data mining and analytics, with a focus on working with data than on coding Full Interview Predictive Analytics has garnered a lot of attention of late, and adopting an analytics-based strategy has become the norm for many businesses. Why do you think this is the case?   Jesus: I think this is happening because everyone wants to make better-informed decisions.  Additionally, predictive analytics brings the added benefit of discovering new relationships that you were previously not aware of. Keith: That’s true, but it’s even more exciting when the models are deployed and are potentially driving automated decisions. With over 40 years of combined experience in this field, you are master consultants and trainers, with an unrivaled expertise when it comes to using the IBM SPSS products. Please share with us the story of your journey in this field. Our readers would also love to know how your day-to-day schedule looks like.   Jesus: When I was in college, I had no idea what I wanted to be. I took courses in many areas, however I avoided statistics because I thought it would be a waste of time, after all, what else is there to learn other than calculating a mean and plugging it into fancy formulas (as a kid I loved baseball, so I was very familiar with how to calculate various baseball statistics). Anyway, I took my first statistics course (where I learned SPSS) since it was a requirement, and I loved it. Soon after I became a teaching assistant for more advanced statistics courses and I eventually earned my Ph.D. in Psychometrics, all the while doing statistical consulting on the side. After graduate school, my first job was as an education consultant for SPSS (where I met Keith). I worked at SPSS (and later IBM) for seven years, at first focusing on training customers on statistics and data-mining, and then later on developing course materials for our trainings. In 2013 Keith invited me to join him as an IBM partner, so we both trained customers and developed a lot of new and exciting material in both book and video formats. Currently, I work as an independent statistical and data-mining consultant and my daily projects range from analyzing data for customers, training customers so they can analyze their own data, or creating books and videos on statistics and data mining. Keith: Our careers have lots of similarities. My current day to day is similar too. Lately, about 1/3rd of my year is lecturing and curriculum development for organizations like TDWI (Transforming Data with Intelligence), The Modeling Agency, and UC Irvine Extension. The majority of my work is in predictive analytics consulting. I especially enjoy projects where I’m brought in early and can help with strategy and planning. Then, the coach and mentor take over a team until they are self-sufficient. Sometimes building the team is even more exciting than the first project because I know that they will be able to do many more projects in the future. There is a plethora of predictive analytics tools used today - for desktop and enterprises. IBM SPSS Modeler is one such tool. What advantages does SPSS Modeler have over the others, in your opinion? Keith: One of our good friends who co-authored the IBM SPSS Modeler Cookbook made an interesting comment about this at a conference. He is unique in that he has done one-day seminars using several different software tools. As you know, it is difficult to present data mining in just one day. He said that only with Modeler he is able to spend some time on each of the CRISP-DM phases of a case study in a day. I think he feels this way because it’s among the easiest options to use. We agree. While powerful, and while it takes a whole career to master everything, it is easy to get started. Are there any prerequisites for using SPSS Modeler? How steep is the learning curve in order to start using the tool effectively? Keith: Well, the first thing I want to mention is that there are no prerequisites for our PACKT video IBM SPSS Modeler Essentials. In that, we assume that you are starting from scratch. For the tool in general, there aren’t any specific requisites as such, however knowing your data, and what insights you are looking for always helps. Jesus: Once you are back at the office, in order to be successful on a data mining project or efficiently utilize the tool, you’ll need to know your business, your data, and the modeling algorithm you are using. Keith: The other question that we get all the time is how much statistics and machine learning do you have to know. Our advice is to start with one or maybe two algorithms and learn them well. Try to stick to algorithms that you know. In our PACKT course, we mostly focus on just Decision Trees, which one of the easiest to learn. What do you think are the 3 key takeaways from your course - IBM SPSS Modeler Essentials? The 3 key takeaways from this course, we feel are: Start slow. Don’t pressure yourself to learn everything all at once. There are dozens of “nodes” in Modeler. We introduce the most important ones so start there. Be brilliant in the basics. Get comfortable with the software environment. We recommend the bests ways to organize your work. Don’t rush to Modeling. Remember the Cross Industry Standard Process for Data Mining (CRISP-DM), which we cover in the video. Use it to make sure that you proceed systematically and don’t skip critical steps. IBM recently announced that SPSS Modeler would be available freely for educational usage. How can one make the most of this opportunity? Jesus: A large portion of the work that we have done over the past few years has been to train people on how to analyze data. Professors are in a unique position to expose more students to data mining since we teach only those students whose work requires this type of training, whereas professors can expose a much larger group of people to data mining. IBM offers several programs that support professors, students, and faculty; for more information visit: https://www-01.ibm.com/software/analytics/spss/academic/ Keith: When seeking out a university class, whether it be classroom or online, ask them if they use Modeler or if they allow you to complete your homework assignments in Modeler. We recognize that R based classes are very popular now, but you potentially won’t learn as much about Data Mining. Sometimes too much of the class is spent on coding so you learn R, but learn less about analytics. You want to spend most of the class time actively working with data and producing results. With the rise of open source languages such as R and Python and their applications in predictive analytics, how do you foresee enterprise tools like SPSS Modeler competing with them? Keith: Perhaps surprisingly, we don’t think Modeler does compete with R or Python. A lot of folks don’t know that Python is Modeler’s scripting language. Now, that is an advanced feature, and we don’t cover it in the Essentials video, but learning Python actually increases your knowledge of Modeler. And Modeler supports running R code right in a Modeler stream by using the R nodes. So Modeler power users (or future power users) should keep learning R on their to-do list. If you prefer not to use code, you can produce powerful results without learning either by just using Modeler straight out of the box. So, it really is all up to you. If this interview has sparked your interest in learning more about IBM SPSS Modeler, make sure you check out our video course IBM SPSS Modeler Essentials right away!

Sebastian Raschka is a machine learning expert. He is currently a researcher at Michigan State University, where he is working on computational biology. But he is also the author of Python Machine Learning, the most popular book ever published by Packt. It's a book that has helped to define the field, breaking it out of the purely theoretical and showing readers how machine learning algorithms can be applied to everyday problems. Python Machine Learning was published in 2015, but Sebastian is back with a brand new edition, updated and improved for 2017, working alongside his colleague Vahid Mirjalili. We were lucky enough to catch Sebastian in between his research and working on the new edition to ask him a few questions about what's new in the second edition of Python Machine Learning, and to get his assessment of what the key challenges and opportunities in data science are today. What's the most interesting takeaway from your book? Sebastian Raschka: In my opinion, the key take away from my book is that machine learning can be useful in almost every problem domain. I cover a lot of different subfields of machine learning in my book: classification, regression analysis, clustering, feature extraction, dimensionality reduction, and so forth. By providing hands-on examples for each one of those topics, my hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications. Also, by using well-developed and maintained open source software, makes machine learning very accessible to a broad audience of experienced programmers as well as people who are new to programming. And introducing the basic mathematics behind machine learning, we can appreciate machine learning being more than just black box algorithms, giving readers an intuition of the capabilities but also limitations of machine learning, and how to apply those algorithms wisely. What's new in the second edition? SR: As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. No worries, we didn't remove it! But it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its open source release by Google in November 2015. Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focussing on deep learning applications: A more detailed overview of the TensorFlow mechanics, an introduction to convolutional neural networks for image classification, and an introduction to recurrent neural networks for natural language processing. Of course, and in a similar vein as the rest of the book, these new chapters do not only provide readers with practical instructions and examples but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works. What do you think is the most exciting trend in data science and machine learning? SR: One interesting trend in data science and machine learning is the development of libraries that make machine learning even more accessible. Popular examples include TPOT and AutoML/auto-sklearn. Or, in other words, libraries that further automate the building of machine learning pipelines. While such tools do not aim to replace experts in the field, they may be able to make machine learning even more accessible to an even broader audience of non-programmers. However, being to interpret the outcomes of predictive modeling tasks and being to evaluate the results appropriately will always require a certain amount of knowledge. Thus, I see those tools not as replacements but rather as assistants for data scientists, to automate tedious tasks such as hyperparameter tuning. Another interesting trend is the continued development of novel deep learning architectures and the large progress in deep learning research overall. We've seen many interesting ideas from generative adversarial neural networks (GANs), densely connected neural networks (DenseNets), and  ladder networks. Large profress has been made in this field thanks to those new ideas and the continued improvements of deep learning libraries (and our computing infrastructure) that accelerate the implementation of research ideas and the development of these technologies in industrial applications. How has the industry changed since you first started working? SR: Over the years, I have noticed that more and more companies embrace open source, i.e., by sharing parts of their tool chain in GitHub, which is great. Also, data science and open source related conferences keep growing, which means more and more people are not only getting interested in data science but also consider working together, for example, as open source contributors in their free time, which is nice. Another thing I noticed is that as deep learning becomes more and more popular, there seems to be an urge to apply deep learning to problems even if it doesn't necessarily make sense -- i.e., the urge to use deep learning just for the sake of using deep learning. Overall, the positive thing is that people get excited about new and creative approaches to problem-solving, which can drive the field forward. Also, I noticed that more and more people from other domains become more familiar with the techniques used in statistical modeling (thanks to "data science") and machine learning. This is nice, since good communication in collaborations and teams is important, and a given, common knowledge about the basics makes this communication indeed a bit easier. What advice would you give to someone who wants to become a data scientist? SR: I recommend starting with a practical, introductory book or course to get a brief overview of the field and the different techniques that exist. A selection of concrete examples would be beneficial for understanding the big picture and what data science and machine learning is capable of. Next, I would start a passion project while trying to apply the newly learned techniques from statistics and machine learning to address and answer interesting questions related to this project. While working on an exciting project, I think the practitioner will naturally become motivated to read through the more advanced material and improve their skill. What are the biggest misunderstandings and misconceptions people have about machine learning today? Well, there's this whole debate on AI turning evil. As far as I can tell, the fear mongering is mostly driven by journalists who don't work in the field and are apparently looking for catchy headlines. Anyway, let me not iterate over this topic as readers can find plenty of information (from both viewpoints) in the news and all over the internet. To say it with one of the earlier comments, Andrew Ng's famous quote: “I don’t work on preventing AI from turning evil for the same reason that I don’t work on combating overpopulation on the planet Mars." What's so great about Python? Why do you think it's used in data science and beyond? SR: It is hard to tell which came first: Python becoming a popular language so that many people developed all the great open-source libraries for scientific computing, data science, and machine learning or Python becoming so popular due to the availability of these open-source libraries. One thing is obvious though: Python is a very versatile language that is easy to learn and easy to use. While most algorithms for scientific computing are not implemented in pure Python, Python is an excellent language for interacting with very efficient implementations in Fortran, C/C++, and other languages under the hood. This, calling code from computationally efficient low-level languages but also providing users with a very natural and intuitive programming interface, is probably one of the big reasons behind Python's rise to popularity as a lingua franca in the data science and machine learning community. What tools, frameworks and libraries do you think people should be paying attention to? There are many interesting libraries being developed for Python. As a data scientist or machine learning practitioner, I'd especially want to highlight the well-maintained tools from Python core scientific stack: -       NumPy and SciPy as efficient libraries for working with data arrays and scientific computing -       Pandas to read in and manipulate data in a convenient data frame format -       matplotlib for data visualization (and seaborn for additional plotting capabilities and more specialized plots) -       scikit-learn for general machine learning There are many, many more libraries that I find useful in my project. For example, Dask is an excellent library for working with data frames that are too large to fit into memory and to parallelize computations across multiple processors. Or take TensorFlow, Keras, and PyTorch, which are all excellent libraries for implementing deep learning models. What does the future look like for Python? In my opinion, Python's future looks very bright! For example, Python has just been ranked as top 1 programming language by IEEE Spectrum as of July 2017. While I mainly speak of Python from the data science/machine learning perspective, I heard from many people in other domains that they appreciate Python as a versatile language and its rich ecosystem of libraries. Of course, Python may not be the best tool for every problem, it is very well regarded as a "productive" language for programmers who want to "get things done." Also, while the availability of plenty of libraries is one of the strengths of Python, I must also highlight that most packages that have been developed are still being exceptionally well maintained, and new features and improvements to the core data science and machine learning libraries are being added on a daily basis. For instance, the NumPy project, which has been around since 2006, just received a $645,000 grant to further support its continued developed as a core library for scientific computing in Python. At this point, I also want to thank all the developers of Python and its open source libraries that have made Python to what it is today. It's an immensely useful tool to me, and as Python user, I also hope you will consider getting involved in open source -- every contribution is useful and appreciated, small documentation fixes, bug fixes in the code, new features, or entirely new libraries. Again, and with big thanks to the awesome community around it,  I think Python's future looks very bright.

IntroductionIn today’s digital landscape, scalability isn’t just a buzzword—it’s a crucial determinant of success. As the complexity and user base of applications grow, so do the challenges in designing systems that can efficiently handle massive loads. This ongoing challenge of scalability was a key inspiration for my recent book, “System Design Guide for Software Professionals: Build scalable solutions – from fundamental concepts to cracking top tech company interviews” The Scalability Crisis Consider a scenario where a startup’s web application goes viral, resulting in a massive influx of users. This should be a cause for celebration, but instead, it becomes a nightmare as the application starts to slow down significantly. According to a 2024 report by Ably, nearly 85% of companies that experience sudden user growth face significant performance issues due to scalability challenges. The root cause often lies in early design decisions, where the rush to market overshadows the need to build for scale. The building Blocks Approach Over the years, I've found that the "building blocks" approach to system design is crucial for building scalable systems. This method leverages established patterns and components to improve scalability. Here are some of the key building blocks discussed in my book: Distributed Caching: A report from Ahex shows that implementing distributed caching systems like Redis or Memcached can reduce database load by up to 60%, significantly speeding up read operations. Load Balancing: Modern load balancers are more than just traffic directors; they are intelligent systems that optimize resource utilization. A 2024 NGINX report revealed that effective load balancing can improve server efficiency by 40%, enhancing performance during peak loads. Database Sharding: As data grows, a single database becomes a bottleneck. Sharding allows horizontal scaling, and companies that implemented it have seen up to a 5x increase in database throughput, as noted in a Google Cloud study. Message Queues: Asynchronous processing with message queues like Kafka or RabbitMQ can decouple system components and manage traffic spikes. A Gartner report found that this can lead to a 30% reduction in latency during peak usage times. Content Delivery Networks (CDNs): For global applications, CDNs are essential. According to Cloudflare, CDNs can reduce load times by 50-70% for users across different regions, significantly improving user experience. Real-World Application: Scaling a Hypothetical E-commerce Platform Consider an e-commerce platform initially designed as a monolithic application with a single database. This setup worked well for the first 100,000 users, but performance issues began to surface as the user base grew to a million. Approach: Microservices Architecture: Decomposing the monolith into microservices allows independent scaling of each component. Amazon famously adopted this approach, enabling it to handle billions of requests daily. Distributed Caching: Implementing a distributed cache reduced database queries by 70%, as seen in an Akamai case study. Database Sharding: Sharding the database improved query performance by 80%, according to data from MongoDB. Message Queues: Using message queues for resource-intensive tasks led to a 25% reduction in system load, as per RabbitMQ's benchmarks. CDN Deployment: Deploying a global CDN reduced page load times from 3.5 seconds to under 1 second, similar to the optimizations reported by Shopify. Example Metrics: Before optimization: The average page load time was 3.5 seconds, with 30% of requests exceeding 5 seconds during peak hours. After optimization: Reduced to 800ms, with 99% of requests completing under 2 seconds, even during Black Friday. Database query volume: Reduced by 65% through effective caching strategies. Infrastructure costs: Reduced by 40% while handling 5x more daily active users. The AI/ML Twist: Scaling GenAI Infrastructure Scaling infrastructure for Generative AI (GenAI) presents unique challenges. For instance, consider a startup offering a GenAI service for content creation. Initially, 10 high-end GPUs served 1,000 daily users, processing about 1 million tokens daily. However, rapid growth led to the processing of 500 million tokens per day for 100,000 users. Challenges: GPU Scaling: GPU scaling requires managing expensive, specialized hardware. A BCG report notes that effective GPU utilization can save companies up to 50% in infrastructure costs. Token Economy: The varying token loads in GenAI apps pose significant challenges. Stanford University says token loads can vary dramatically, complicating resource prediction. Cost Management: Cloud GPU instances can cost over $10,000/month. AWS reports that optimized GPU management strategies can reduce costs by 30%. Latency Expectations: Users expect near-instant responses. A study by OpenAI found that sub-second latencies are critical for real-time applications. Solutions: Dynamic GPU Allocation: Implementing dynamic GPU allocation can reduce idle times and costs, as observed by Google Cloud. Request Batching: Grouping user requests can improve GPU throughput by 20%, according to Azure AI. Model Optimization: Techniques like quantization and pruning can reduce model size by 70% and increase inference speed by 50%, as highlighted in MIT’s research. Tiered Service Levels: Offering different response time guarantees can optimize resource allocation, as shown by Microsoft Azure. Distributed Inference: Splitting models across GPUs or using CPU inference can reduce GPU load by 40%, based on Google AI's findings. Example Metrics: Cost per 1000 tokens: Reduced from $0.05 to $0.015 through optimized GPU management. p99 Latency: Improved from 5 seconds to 1.2 seconds. Infrastructure scaling: Handled 1 billion daily tokens with only a 20x increase in costs, compared to the 100x increase projected by traditional scaling methods. Beyond Technology: The Human Factor While technology is critical, fostering a culture of scalability is equally important. A Harvard Business Review article emphasized that companies prioritizing scalable culture from the start are 50% more likely to sustain growth without operational roadblocks. Strategies: Encourage developers to consider scalability from the outset. Invest in monitoring and observability tools to detect issues early. Regularly conduct load tests and capacity planning. Adopt a DevOps culture to break down silos between development and operations. The Road Ahead As we move forward, innovations in edge computing, serverless architectures, and large-scale machine learning will continue to push the boundaries of scalability. However, the foundational principles of scalable system design—modularity, redundancy, and efficient resource utilization—remain vital. By mastering these principles, you can build systems that grow and adapt to an ever-changing digital landscape, whether you’re scaling a web application or pioneering generative AI technologies. Remember, scalability is not a destination but a journey, and having the right building blocks makes all the difference. 

John Arundel is a DevOps consultant. Which means he helps businesses use software better. But when he's not supporting his clients, John is writing for Packt. John has written a number of books over the last few years, most recently Puppet 5 Beginner's Guide. Puppet is one of the most popular tools in the DevOp toolchain; it's a tool that gives administrators and architects significant control over their infrastructure. For that reason, it's a tool worth exploring - whatever field you're working in. It's likely to play a large part in the continued rise of DevOps throughout 2018. We spoke to John about Puppet, DevOps, and his new book - as well as his experience writing it. Packt: Your book is published now. How does it feel to be a published author? John Arundel: Pretty great! At one time I wrote technical manuals for Psion, the palmtop computer manufacturer. Thanks to a conservative house style, the kind of books I wrote said things like: “To access file operations, select the File menu”. Not exactly a page-turner. I’m very happy now to be able to publish a book which is written more or less exactly the way I want it, on a subject I find very interesting, and including a lot of jokes. What benefits did writing a book bring to your specialist area? JA: The funny thing is that despite being a Puppet user almost since the very beginning, I really don’t use many of its features. In fact, most of them have been added since I started using Puppet, and I don’t have a lot of time to experiment with new stuff, so writing the book was a great opportunity to delve into all the Puppet features I didn’t know about. I’m hoping that readers will also find out stuff they didn’t know and that will come in useful. If just one person is helped and inspired by this book... then I’m not giving refunds to the others. It’s done a lot to raise the profile of my consulting business; I was introduced to one potential client as “This is John. He literally wrote the book on Puppet”. I had to modestly point out that in fact, other, probably better books are available. Our authors usually have full-time jobs whilst writing for us. Was this the case for you and how did you approach managing your time? JA: As any freelancer knows, the job is more than full-time. I practically had to invent new physics to figure out a way of using my negative free time to write a book. I blocked out one day a week devoted to writing, and set myself a goal of a number of hours to achieve each month, which I mostly met. Because the book is so code-focused, I had to not only write about each technique I was describing, but also develop complete, working, reusable software in Puppet to implement it, and then test this on a virtual machine. Quite frequently I’d discover later that I’d been doing something wrong, or a behaviour in Puppet changed, and I’d have to go back and fix all the code. I’m sure there are still quite a few bugs, which I am going to pretend I’ve deliberately inserted to help the reader learn to debug and fix Puppet code: something they will, after all, spend a great deal of time doing. In all, what with researching, writing, coding, testing, fixing, editing, and complaining on Twitter, I spent about 200 hours on the book over 8 months. While writing your book, did you find that it overshadowed personal life in any way? How did you deal with this? JA: Not really. It could have, if I’d got into serious deadline trouble. Fortunately, I managed to keep up a continuous, manageable level of mild deadline trouble. I don’t think my friends or family noticed, except occasionally I’d say things at dinner like, “Could you pass the Puppet? I mean pepper.” Do you have any advice for other authors who may be interested in writing for Packt, but are still unsure? JA: Go for it! But make sure you have two hundred unallocated hours in your schedule. You’d be amazed how much time you can save by not watching TV, going out, putting on clothes, etc. Really, my advice would be to plan the book carefully - agreeing the outline in advance with your editor helps a lot. Henry Ford said that there are no big problems, just lots of little problems. Breaking down a book into chapters and sections and subsections and tackling them one by one makes it seem less daunting. And managing your time well helps avoid last-minute-essay syndrome. Do you have any tips for other authors, or tricks that you learnt whilst writing, that you'd like to share? JA: One good tip is, once you’ve written a chapter, let it lie fallow for a few weeks and then come back to it with a fresh eye. What you thought were immaculately-crafted sentences turn out to be pompous waffle. And what seemed clear and explicit now seems larded with techno-babble. I read somewhere that P.G. Wodehouse would stick each page of manuscript to the wall as he wrote it, somewhere around the skirting board level, and as he obsessively reworked and rewrote and polished the text he would gradually move it higher and higher up the wall until he judged it good enough - somewhere near the ceiling. Well, I’m not saying I’m P.G. Wodehouse — I’ll leave that for others to say — but it’s a useful way to think about the writing process. Rewrite, rewrite, rewrite! “What is written without effort,” Dr Johnson pointed out, “is in general read without pleasure.” Was there anything interesting that happened during the writing of the book? JA: Only in the sense that the Chinese use when they curse you to live in interesting times. Quite often I wrote myself to a standstill and just stared blankly at the laptop, hoping it would explain something complicated for me, or think up a useful and instructive example when I couldn’t. On one occasion I decided that the best thing to do with a certain long, difficult, and laboriously-constructed section was to delete it altogether, improving the book immeasurably as a result. The deleted scenes will be available on a forthcoming DVD, together with a ‘making of’ documentary which consists of me frowning at a screen for 200 hours and intermittently making tea. How did Packt’s Acquisition Editors help you - what kind of things did they help you with and how did they support you throughout the writing process? JA: The biggest help at the start was giving me structure by insisting on an outline, and then setting individual chapter deadlines to plan the writing time - then gently but persistently enforcing them. What was also very useful was to see sample chapters of other books, to get an idea of where I was supposed to be going, and getting very detailed feedback on the early chapters about exactly how to lay things out and how to make everything consistent. Beyond that, I was pleased and surprised by how little the editors interfered with what I was doing. By and large I was allowed to write my own book the way I wanted. No one suggested I write sentences like “To access file operations, select the File menu.” When I asked for help, I got it, and when I didn’t, I was left in peace and trusted to do the right thing. That’s a great way to write. What projects, if any, are you working on at the moment? Several people have asked what the next book’s going to be. I have said, only half-jokingly, that I might do one on Chef. I have a kind of a semi-formed idea about a book of system administration patterns and practices, based on my several decades worth of experience (read: mistakes). But just now I’m enjoying a break from writing, and I’m spending my negative free time reading other people’s books, playing Beethoven on my toy piano like Schroeder out of Peanuts, and learning to bake the perfect Cornish pasty. Ah! Excuse me, that was the oven timer. Thanks for taking the time to talk to us, John! You can find John's latest book, Puppet 5 Beginner's Guide, here.

What initially drew you to write your book for Packt Publishing? In 2009, I started writing technical articles on my personal blog. I would write about my field, Geographic Information Systems, or any other technical articles. Whenever a new technology emerged, a new product,or sometimes even mere tips or tricks,I would write an article about it. My blog became a well-known site in GIS, and that is when Packt approached me with a proposed title. I always wanted to write a book but I never expected that the opportunity would knock on my door. I thank Packt for giving me that opportunity. When you began writing, what were your main aims? My main aim was to write a book that readers in my domain could grab and benefit from. While working on a chapter, I would always imagine a reader picking up the book and reading that particular chapter and asked myself, what could I do better? And then I tried to make the chapter as simple as possible and leave nothing unexplained. What did you enjoy most and what was most rewarding about the experience of writing? Think about all the knowledge, information, ideas, and tips that you possess. You knew you had it in you somewhere but you didn’t know the joy and delight you would feel when this knowledge slipped through your fingertips into a physical medium. With each reading I would reread and polish the chapters;it seems there is always room for improvement in writing. Why, in your opinion, is ArcGIS exciting to discover, read, and write about? ArcGIS is not a new technology; it has been around for more than 14 years. It has become mature and polished during these years. It has expanded and started touching other bleeding-edge technologies like mobile, web, and the cloud. Everyday this technology is increasingly worth discovering and everyday it benefits areas like health, utilities, transportation, and so on. Why do you think interest in GIS is on the rise? If you read The Tipping Point,by Malcolm T. Gladwell, you will understand that the smartphone was actually a tipping point for the GIS technology. GIS was only used by enterprises and big companies who wanted to add the location dimension to their tabular data so it helped them better visualize and analyze their information. With smartphones and GPS, geographic location became more relevant. Pictures taken with smartphones are tagged with location information. Applications were developed to harness the power of GIS for routing, finding the best restaurants in an area, calculating shortest routes, finding information based on geo-fencing technology that sends you text messages when you pass by a shop, and so on. The popularity of GIS is rising and so is the interest in adapting this technology. What do you see on the horizon for GIS? High end processing servers are being sent to the cloud while we are carrying smaller and smaller gadgets. Networking is getting stronger every day with the LTE and 4G networks already setup in many countries. Storage has become no issue at all. The Web architecture is dominant so far and it is the most open and compatible platform that has ever existed. As long as we keep using devices, we will need geographic information systems. The data can be consumed and fetched swiftly from anywhere in the world from the smallest device. I believe this will evolve to an extent that everything valuable we own can be tagged with a location, so when we misplace something or lose it, we can always use GIS to locate it. Any tips for new authors? My role model author is Seth Godin; the first book I ever read was his. When I told him about my new book and asked him for any advice he might give me as a new author, he told me and I quote,″Congratulations, Hussein .This is thrilling to hear; my only advice is to keep writing!″ I took his advice and now I′m working on my second book with Packt. Another personal tip I can give to new authors is thatwriting needs focus, and I find music the best soul feeding source. While working on my first book,I discovered this site www.stereomood.com, which plays music that will help you write. Another thing is to use a clutter free word processor application that will blank the entire screen so you are only left with your words. I use WriteMonkey for Windows and Focus writer for Mac.

School of Code is a company on a mission to help more people benefit from technology. It has created an online multiplayer platform that aims to make coding fun, simple and accessible to all. This platform has been used by over 120,000 people since its launch in December 2016, and School of Code recently won the ‘Transforming Lives’ award at the 2017 Education Awards. The company was founded by Chris Meah while he was completing his PhD in Computer Science at the University of Birmingham.  As headline sponsors, Packt founder and CEO Dave Maclean shares his thoughts on the programme. “The number and diversity of the applicants proves how many people in Birmingham are looking to learn key skills like HTML, CSS, Javascript and Node.JS. Packt is excited to sponsor School of Code’s Bootcamp participants to increase the population of skilled developers in the West Midlands, which will have an impact on the growth of innovative start-ups in this region.” We spoke to Sharon Kaur, who's been involved with a School of Code bootcamp about her experience and for her perspective on tech in 2018. Packt: Hi Sharon! Tell us a little about yourself. Sharon Kaur: My name is Sharon. I am a choreographer and dancer for international music groups. I am also an engineering and technology advocate and STEM Ambassador for the UK and India – my main aim is getting more young girls and ethnic minorities interested in and pursuing a career in science, technology and engineering. What were you doing before you enrolled for School of Code and what made you want to sign up? I previously studied my BEng honours and MSc degrees at University of Surrey, in general and medical engineering. I worked in the STEM education industry for a few years and then gained my teaching qualification in secondary school/sixth form Science in Birmingham. I recently started learning more about the technology industry after completing an online distance-learning course in cyber security. I was on Facebook one day in June and I saw an advert for the first ever School of Code Bootcamp, and I just decided to dive in and go for it! Do you think there is a diversity issue in the tech sector? Has it affected you in any way? I definitely think there is a major problem in the technology industry, in terms of diversity. There are far too many leadership and management positions taken up by upper/middle class, white men. There needs to be more outreach work done to attract more women and ethnic minority people into this sector, as well as continuing to work with them afterwards, to prevent them from leaving tech in the middle of their careers! This has not affected me in any direct way, but as a female from an engineering background, which is also a very male-dominated sector, I have experienced some gender discrimination and credit for work I produced being given to someone else. Why do you think making technology accessible to all is important? Technology opens up so many doors to some really exciting and life-fulfilling work. It really is the future of this planet, and in order to keep improving the progress of the global economy and human society, we need more and more advanced technology and methods, daily. This means that there is a dire need for a large number of highly competent employees working continuously in the tech sector. What do you think the future looks like for people working in the tech industry? Will larger companies strive to diversify their workforce, and, why should they? In my opinion, the future looks extremely exciting and progressive! Technology will only become more and more futuristic, and we could be looking at getting more into the sci-fi age, come the next few centuries, give or take. So, the people who will work in the tech sector will be highly sought after – lucky them! I would hope though, that large corporations will change their employee recruitment policies, in terms of a more diverse intake, if they truly want to reach the top of their games, with maximum efficiency and employee wellbeing. School of Code encourages the honing of soft skills through networking, team work and project management. Do you think these skills are vital for the future of the tech industry and attracting a new generation, shaking off the stereotype that all coders are solitary beings? Why? Yes, definitely – soft skills are just as important, if not slightly more, than the technical aptitude of an employee in the tech industry! With collaboration and a business acumen, we can bring the world of technology together and use it to make a better life for every human being on this planet. The technology industry needs to show its solidarity, not its divisiveness, in attracting the next generation of young techies, if it wants to maintain its global outreach. What advice would you give to someone who wanted to get into the tech sector but may be put off by the common preconception that it is made up of male white privilege? I would say go for it, dive in at the deep end and come out the other side the better person in the room! Have the courage to stand up for your beliefs and dreams, and don't ever let anyone tell you or make you feel like you don't deserve to be standing there with everyone else in the room – pick your battles wisely, become more industry – and people-savvy, choose your opportune moment to shine, and you'll see all the other techies begging you to work with them, not even for them! Find out more about School of Code.  Download some of the books the Bootcampers found useful during the course: Thinking in HTML Thinking in CSS Thinking in JS series  MEAN Web Development React and React Native Responsive Web Design

An unreliable versioning tool makes product development a herculean task. Creating and enforcing checks and controls for the introduction, scrutiny, approval, merging, and reversal of changes in your source code, are some effective methods to ensure a secure development environment. Git and GitHub offer constructs that enable teams to conduct version control and collaborative development in an effective manner.  When properly utilized, Git and GitHub promote agility and collaboration across a team, and in doing so, enable teams to focus and deliver on their mandates and goals. We recently interviewed Joseph Muli and Alex Magana, the authors of Introduction to Git and GitHub course. They discussed the various benefits of Git and GitHub while sharing some best practices and myths. Author Bio Joseph Muli loves programming, writing, teaching, gaming, and travelling. Currently, he works as a software engineer at Andela and Fathom, and specializes in DevOps and Site Reliability. Previously, he worked as a software engineer and technical mentor at Moringa School. You can follow him on LinkedIn and Twitter. Alex Magana loves programming, music, adventure, writing, reading, architecture, and is a gastronome at heart. Currently, he works as a software engineer with BBC News and Andela. Previously, he worked as a software engineer with SuperFluid Labs and Insync Solutions. You can follow him on LinkedIn or GitHub. Key Takeaways Securing your source code with version control is effective only when you do it the right way. Understanding the best practices used in version control can make it easier for you to get the most out of Git and GitHub. GitHub is loaded with an elaborate UI. It’ll immensely help your development process to learn how to navigate the GitHub UI and install the octo tree. GitHub is a powerful tool that is equipped with useful features. Exploring the Feature Branch Workflow and other forking features, such as submodules and rebasing, will enable you to make optimum use of the many features of GitHub. The more elaborate the tools, the more time they can consume if you don’t know your way through them. Master the commands for debugging and maintaining a repository, to speed up your software development process. Keep your code updated with the latest changes using CircleCI or TravisCI, the continuous integration tools from GitHub. The struggle isn’t over unless the code is successfully released to production. With GitHub’s release management features, you can learn to complete hiccup-free software releases. Full Interview Why is Git important? What problem is it solving? Git, like all other version control tools, exists to solve for one problem, change. This has been a recurring issue, especially when coordinating work on teams, both locally and distributed, that specifically being an advantage of Git through hubs such as GitHub, BitBucket and Gitlab. The tool was created by Linus Torvalds in 2005 to aid in development and contribution on the Linux Kernel. However, this doesn’t necessarily limit Git to code any product or project that requires or exhibits characteristics such as having multiple contributors, requiring release management and versioning stands to have an improved workflow through Git. This also puts into perspective that there is no standard, it’s advisable to use what best suits your product(s). What other similar solutions or tools are out there? Why is Git better? As mentioned earlier, other tools do exist to aid in version control. There are a lot of factors to consider when choosing a version control system for your organizations, depending on product needs and workflows. Some organizations have in-house versioning tools because it suits their development. Some organizations, for reasons such as privacy and security or support, may look for an integration with third-party and in-house tools. Git primarily exists to provide for a faster and distributed version system, that is not tied to a central repository, hub or project. It is highly scalable and portable. Other VC tools include Apache SubVersion, Mercurial and Concurrent Versions System (CVS). How can Git help developers? Can you list some specific examples (real or imagined) of how it can solve a problem? A simple way to define Git’s indispensability is enabling fast, persistent and accessible storage. This implies that changes to code throughout a product’s life cycle can be viewed and updated on demand, each with simple and compact commands to enable the process. Developers can track changes from multiple contributors, blame introduced bugs and revert where necessary. Git enables multiple workflows that align to practices such as Agile e.g. feature branch workflows and others including forking workflows for distributed contribution, i.e. to open source projects. What are some best tips for using Git and GitHub? These are some of the best practices you should keep in mind while learning or using Git and GitHub. Document everything Utilize the README.MD and wikis Keep simple and concise naming conventions Adopt naming prefixes Correspond a PR and Branch to a ticket or task. Organize and track tasks using issues. Use atomic commits [box type="shadow" align="" class="" width=""]Editor’s note: To explore these tips further, read the authors’ post ‘7 tips for using Git and GitHub the right way’.[/box] What are the myths surrounding Git and GitHub? Just as every solution or tool has its own positives and negatives, Git is also surrounded by myths one should be aware of. Some of which are: Git is GitHub Backups are equivalent to version control Git is only suitable for teams To effectively use Git, you need to learn every command to work [box type="shadow" align="" class="" width=""]Editor’s note: To explore these tips further, read the authors’ post ‘4 myths about Git and GitHub you should know about’.  [/box] GitHub’s new integration for Jira Software Cloud aims to provide teams a seamless project management experience GitLab raises $100 million, Alphabet backs it to surpass Microsoft’s GitHub GitHub introduces ‘Experiments’, a platform to share live demos of their research projects  

Christoph is the CTO of GESIM, a Swiss start-up company, where he is responsible for their simulation software and web interface built with Angular and D3. He is a passionate, self-taught, software developer and web-enthusiast with more than 7 years’ experience in designing and implementing customer-oriented web-based IT solutions. Curious about new technologies and interested in innovation Christoph immediately started using AngularJS and D3 with the first versions. We caught up with him to get his insights into writing with Packt. Why did you decide to write with Packt, what convinced you? Initially, I wasn’t sure about taking on such a big project. However after doing some research and discussing Packt’s reputation with my University colleagues, I was sure I wanted to go ahead. I was also really passionate about the topic, Angular is one of my favourite tools for frontend JavaScript. As a first-time Packt author, what type of support did you receive to develop your content effectively? I started off working independently, researching papers, developing code for the project and reading other books on similar topics, and I got some great initial feedback from my University colleagues. As the project progressed with Packt, I received a lot of valuable feedback from the technical reviewers and the process really provided a lot of valuable and constructive insights. What were your main aims when you began writing with us, and how did Packt in particular match those aims? I was aiming to help other people get started with an awesome front-end technology stack (Angular and D3). I love to look closely at topics that interest me, and enjoy exploring all the angles, both practical and theoretical, and helping others understand it. My book experience was great and Packt allowed me to explore all the theory and practical concepts that the target reader will find really interesting. What was the most rewarding part of the writing experience? The most rewarding part of writing is getting constructive, critical feedback – particularly readers who leave comments about the book as well as the comments from my reviewers. It was a pleasure to have such skilled, motivated and experienced reviewers on-board who helped me develop the concepts of the book. And of course, holding your own book in your hands after 6 months of hard work is a fantastic feeling. What do you see as the next big thing in your field, and what developments are you excited about? The next big thing will be Angular 2.0 and Typescript 1.5; and this will have a big impact on the JavaScript world. Combining – for example – new Typescript features such as annotations with D3js, opening up a whole new world of writing visualizations using annotations for transitions or styling – which will make the code much cleaner. Do you have any advice for new authors? Proper planning is the key, it will take time to write, draw graphics and develop your code at the same time. Don't cut a chapter because you think you don't have time to write it as you wanted – find the time! And get feedback as soon as possible. Experienced authors and users can give very good tips, advice and critique. You can connect with Christoph here: Github: https://github.com/chaosmail Twitter: https://twitter.com/ChrisiKrnr LinkedIn: https://ch.linkedin.com/in/christophkoerner Blog: http://chaosmail.github.io/ Click here to find out more about Christoph’s book Data Visualization with D3 and AngularJS

Heather Mahalik is currently Principle Forensic Scientist and Program Manager at Oceans Edge, Inc, and the course lead for the SANS mobile device and advanced smartphone forensics courses. With over 11 years' experience in digital forensics, she currently focuses on mobile device investigations, forensic course development and instruction, and research on smartphone forensics. As a prolific forensics professional, Heather brought a great deal of expertise and knowledge to Packt, helping to make Practical Mobile Forensics a great success, and we caught up with her to get some thoughts on her experiences as an author. Why did you decide to write with Packt, what convinced you? Packt approached me with the idea and introduced me to the other authors, who ended up being co-authors of the book. I was lucky to be sought out and not have to seek a publisher. As a first-time Packt author, what type of support did you receive to develop your content effectively? Packt provided our team an editor and others to support our efforts on the book. Our Acquisition Editor was fantastic and always responded immediately. I never felt that any question was unanswered or that I didn’t have the support I needed. They were also very flexible with us submitting chapters out of order to allow the normal flow of writing. What were your main aims when you decided to write a book, and how did Packt, in particular, match those aims? I wanted to release a book quickly on mobile forensics that emphasized the use of open source tools. Packt allowed us to progress quickly, update as needed and get the book out. What was the most rewarding part of the writing experience? Working with my co-authors. Seeing their perspectives on each topic was eye opening. What do you see as the next big thing in your field, and what developments are you excited about? Handling smartphone security – device security, encryption, and application obfuscation. Do you have any advice for new authors? Stay positive, write a little bit every day and hang in there. Follow Heather on Twitter (@HeatherMahalik) or take a look at her blog. If you have been inspired to write, get in touch with our experienced team who are always open to discussing and designing potential book ideas with talented people. Click here for more details.