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A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

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To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

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Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled?

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title?

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team.

Can I download the code files for Early Access titles?

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date?

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready?

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access?

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered?

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content?

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access?

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.

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Amazon verified reviews

Dror Oct 01, 2023

Statistics is a fundamental discipline concerned with the collection, organization, analysis, interpretation, and presentation of data. While Python—an extremely popular general-purpose programming language—has become the programming language of choice for computation in most science and engineering disciplines, most (software-oriented) statistics books still teach statistics using the more special-purpose R language.This unique and highly practical book provides a gentle introduction to statistics and to using the Python programming language for building statistical models. It begins with a clear and useful introduction to statistics, including sampling, data distributions, hypothesis testing, and parametric and non-parametric statistical tests. It then progresses to describe in detail how to build statistical models using Python for a variety of problems, including for regression, classification, time-series, and survival analysis. The descriptions are clear and concise, and gradually present additional common and helpful Python packages for performing statistical analysis. The accompanying GitHub repository includes practical and detailed code examples, and is very helpful in reinforcing the materials and concepts presented in the book.I highly recommend this book to anyone interested in learning statistics and how to use Python for building statistical models. It requires no more than basic knowledge of the Python programming language, and will be ideal for data scientists, analysts, and industry professionals who are taking their first steps in the world of statistics or want to expand their knowledge in this area.Highly recommended!

Amazon Verified review

JRVV Oct 19, 2023

The book provides a broad primer on statistical modeling using Python. This book can also serve as a starting point to those who eventually want to go into machine learning. Recommended.

Amazon Customer Oct 02, 2023

This book is exceptionally crafted, serving as a comprehensive review of fundamental statistical knowledge, complemented with practical Python codes. Unlike other market options, which either focus solely on theory or coding, lacking depth in theoretical insight, this book seamlessly bridges theory to application. While many statistical texts predominantly utilize the R language, this book's emphasis on Python is a refreshing change. It not only rejuvenates and reinforces my existing knowledge but also significantly advances my understanding of Statistics and Machine Learning. It stands out as a balanced and insightful resource for both theoretical comprehension and practical application in the field.

Steven Fernandes Oct 09, 2023

The authors offer a compelling dive into making informed decisions under uncertainty, equipping readers with practical skills, such as hypothesis testing and data analysis. They thoughtfully elucidate the distinctions between regression and classification models and provide a hands-on approach to building models using Python's statsmodels. The text also insightfully explores time-series data analysis, forecasting, and survival analysis, adeptly linking theory with real-world applications. This book emerges as an invaluable guide for both beginners and seasoned practitioners, intertwining robust theoretical constructs with practical applicability in data analysis and model-building, rendering it a must-read in the field of data science.

Ratan Nov 23, 2023

Authors have done a good job in maintaining the comprehensiveness of the book. They have maintained adequate amount of mathematics what is needed. I particularly loved the way they have presented Hypothesis testing for models which is often missing in many places. They have nicely covered both parametric and non parametric testing.The other part I liked was somewhat less visited topic survival analysis. Overall I found this book an excellent read ! Definitely recommend it.

Building Statistical Models in Python: Develop useful models for regression, classification, time series, and survival analysis

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