More Information
Learn
  • Describe and visualize the behavior of data and relationships between data
  • Gain a thorough understanding of statistical reasoning and sampling
  • Handle missing data gracefully using multiple imputation
  • Create diverse types of bar charts using the default R functions
  • Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on
  • Understand relationships between market factors and their impact on your portfolio
  • Harness the power of R to build machine learning algorithms with real-world data science applications
  • Learn specialized machine learning techniques for text mining, big data, and more
About

The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module!

This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility.

The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework.

With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs.

The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions.

Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on.

Features
  • Load, wrangle, and analyze your data using the world's most powerful statistical programming language
  • Build and customize publication-quality visualizations of powerful and stunning R graphs
  • Develop key skills and techniques with R to create and customize data mining algorithms
  • Use R to optimize your trading strategy and build up your own risk management system
  • Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
Page Count 1783
Course Length 53 hours 29 minutes
ISBN 9781786460486
Date Of Publication 23 Jun 2016

Authors

Tony Fischetti

Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory.

Tony enjoys writing and contributing to open source software, blogging at onthelambda, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples.

The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.

Brett Lantz

Brett Lantz (@DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.

Jaynal Abedin

Jaynal Abedin is currently doing research as a PhD student at Unit for Biomedical Data Analytics (BDA) of INSIGHT at the National University of Ireland Galway. His research work is focused on the sports science and sports medicine area in a targeted project with ORRECO --an Irish startup company that provides evidence-based advice to individual athletes through biomarker and GPS data. Before joining INSIGHT as a PhD student he was leading a team of statisticians at an international public health research organization (icddr,b). His primary role there was to develop internal statistical capabilities for researchers who come from various disciplines. He was involved in designing and delivering statistical training to the researchers. He has a bachelors and masters degree in statistics, and he has written two books in R programming: Data Manipulation with R and R Graphs Cookbook (Second Edition) with Packt. His current research interests are predictive modeling to predict probable injury of an athlete and scoring extremeness of multivariate data to get an early signal of an anomaly. Moreover, he has an excellent reputation as a freelance R programmer and statistician in an online platform such as upwork.

Hrishi V. Mittal

Hrishi V. Mittal has been working with R for a few years in different capacities. He was introduced to the exciting world of data analysis with R when he was working as a senior air quality scientist at King's College, London, where he used R extensively to analyze large amounts of air pollution and traffic data for London's Mayor's Air Quality Strategy. He has experience in various other programming languages but prefers R for data analysis and visualization. He is also actively involved in various R mailing lists, forums, and the development of some R packages.

Bater Makhabel

Bater Makhabel (LinkedIn: BATERMJ and GitHub: BATERMJ) is a system architect who lives across Beijing, Shanghai, and Urumqi in China. He received his master's and bachelor's degrees in computer science and technology from Tsinghua University between the years 1995 and 2002. He has extensive experience in machine learning, data mining, natural language processing (NLP), distributed systems, embedded systems, the web, mobile, algorithms, and applied mathematics and statistics. He has worked for clients such as CA Technologies, META4ALL, and EDA (a subcompany of DFR). He also has experience in setting up start-ups in China.

_x000D_

Bater has been balancing a life of creativity between the edge of computer sciences and human cultures. For the past 12 years, he has gained experience in various culture creations by applying various cutting-edge computer technologies, one being a human-machine interface that is used to communicate with computer systems in the Kazakh language. He has previously collaborated with other writers in his fields too, but Learning Data Mining with R is his first official effort.

Edina Berlinger

Edina Berlinger has a PhD in economics from the Corvinus University of Budapest. She is an associate professor, teaching corporate finance, investments, and financial risk management. She is the head of the Finance department of the university, and is also the chair of the finance subcommittee of the Hungarian Academy of Sciences. Her expertise covers loan systems, risk management, and more recently, network analysis. She has led several research projects in student loan design, liquidity management, heterogeneous agent models, and systemic risk.

Ferenc Illés

Ferenc Illés has an MSc degree in mathematics from Eötvös Loránd University. A few years after graduation, he started studying actuarial and financial mathematics, and he is about to pursue his PhD from Corvinus University of Budapest. In recent years, he has worked in the banking industry. Currently, he is developing statistical models with R. His interest lies in large networks and computational complexity.

Milán Badics

Milán Badics has a master's degree in finance from the Corvinus University of Budapest. Now, he is a PhD student and a member of the PADS PhD scholarship program. He teaches financial econometrics, and his main research topics are time series forecasting with data-mining methods, financial signal processing, and numerical sensitivity analysis on interest rate models. He won the competition of the X. Kochmeister-prize organized by the Hungarian Stock Exchange in May 2014.

Ádám Banai

Ádám Banai has received his MSc degree in investment analysis and risk management from Corvinus University of Budapest. He joined the Financial Stability department of the Magyar Nemzeti Bank (MNB, the central bank of Hungary) in 2008. Since 2013, he is the head of the Applied Research and Stress Testing department at the Financial System Analysis Directorate (MNB). He is also a PhD student at the Corvinus University of Budapest since 2011. His main research fields are solvency stress-testing, funding liquidity risk, and systemic risk.

Gergely Daróczi

Gergely Daróczi is a former assistant professor of statistics and an enthusiastic R user and package developer. He is the founder and CTO of an R-based reporting web application at http://rapporter.net and a PhD candidate in sociology. He is currently working as the lead R developer/research data scientist at https://www.card.com/ in Los Angeles.

Besides maintaining around half a dozen R packages, mainly dealing with reporting, Gergely has coauthored the books Introduction to R for Quantitative Finance and Mastering R for Quantitative Finance (both by Packt Publishing) by providing and reviewing the R source code. He has contributed to a number of scientific journal articles, mainly in social sciences but in medical sciences as well.

Barbara Dömötör

Barbara Dömötör is an assistant professor of the department of Finance at Corvinus University of Budapest. Before starting her PhD studies in 2008, she worked for several multinational banks. She wrote her doctoral thesis about corporate hedging. She lectures on corporate finance, financial risk management, and investment analysis. Her main research areas are financial markets, financial risk management, and corporate hedging.

Gergely Gabler

Gergely Gabler is the head of the Business Model Analysis department at the banking supervisory division of National Bank of Hungary (MNB) since 2014. Before this, he used to lead the Macroeconomic Research department at Erste Bank Hungary after being an equity analyst since 2008. He graduated from the Corvinus University of Budapest in 2009 with an MSc degree in financial mathematics. He has been a guest lecturer at Corvinus University of Budapest since 2010, and he also gives lectures in MCC College for advanced studies. He is about to finish the CFA program in 2015 to become a charterholder.

Dániel Havran

Dániel Havran is a postdoctoral research fellow at Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences. He also holds a part-time assistant professor position at the Corvinus University of Budapest, where he teaches corporate finance (BA, PhD) and credit risk management (MSc). He obtained his PhD in economics at Corvinus University of Budapest in 2011.

Péter Juhász

Péter Juhász holds a PhD degree in business administration from the Corvinus University of Budapest and is also a CFA charterholder. As an associate professor, he teaches corporate finance, business valuation, VBA programming in Excel, and communication skills. His research field covers the valuation of intangible assets, business performance analysis and modeling, and financial issues in public procurement and sports management. He is the author of several articles, chapters, and books mainly on the financial performance of Hungarian firms. Besides, he also regularly acts as a consultant for SMEs and is a senior trainer for EY Business Academy in the EMEA region.

István Margitai

István Margitai is an analyst in the ALM team of a major banking group in the CEE region. He mainly deals with methodological issues, product modeling, and internal transfer pricing topics. He started his career with asset-liability management in Hungary in 2009. He gained experience in strategic liquidity management and liquidity planning. He majored in investments and risk management at Corvinus University of Budapest. His research interest is the microeconomics of banking, market microstructure, and the liquidity of order-driven markets.

Balázs Márkus

Balázs Márkus has been working with financial derivatives for over 10 years. He has been trading many different kinds of derivatives, from carbon swaps to options on T-bond futures. He was the head of the Foreign Exchange Derivative Desk at Raiffesien Bank in Budapest. He is a member of the advisory board at Pallas Athéné Domus Scientiae Foundation, and is a part-time analyst at the National Bank of Hungary and the managing director of Nitokris Ltd, a small proprietary trading and consulting company. He is currently working on his PhD about the role of dynamic hedging at the Corvinus University of Budapest, where he is affiliated as a teaching assistant.

Péter Medvegyev

Péter Medvegyev has an MSc degree in economics from the Marx Károly University Budapest. After completing his graduation in 1977, he started working as a consultant in the Hungarian Management Development Center. He got his PhD in Economics in 1985. He has been working for the Mathematics department of the Corvinus University Budapest since 1993. His teaching experience at Corvinus University includes stochastic processes, mathematical finance, and several other subjects in mathematics.

Julia Molnár

Julia Molnár is a PhD candidate at the Department of Finance, Corvinus University of Budapest. Her main research interests include financial network, systemic risk, and financial technology innovations in retail banking. She has been working at McKinsey & Company since 2011, where she is involved in several digital and innovation studies in the area of banking.

Balázs Árpád Szűcs

Balázs Árpád Szűcs is a PhD candidate in finance at the Corvinus University of Budapest. He works as a research assistant at the Department of Finance at the same university. He holds a master's degree in investment analysis and risk management. His research interests include optimal execution, market microstructure, and forecasting intraday volume.

Ágnes Tuza

Ágnes Tuza holds an applied economics degree from Corvinus University of Budapest and is an incoming student of HEC Paris in International Finance. Her work experience covers structured products' valuation for Morgan Stanley as well as management consulting for The Boston Consulting Group. She is an active forex trader and shoots a monthly spot for Gazdaság TV on an investment idea where she frequently uses technical analysis, a theme she has been interested in since the age of 15. She has been working as a teaching assistant at Corvinus in various finance-related subjects.

Tamás Vadász

Tamás Vadász has an MSc degree in economics from the Corvinus University of Budapest. After graduation, he was working as a consultant in the financial services industry. Currently, he is pursuing his PhD in finance, and his main research interests are financial economics and risk management in banking. His teaching experience at Corvinus University includes financial econometrics, investments, and corporate finance.

Kata Váradi

Kata Váradi is an assistant professor at the Department of Finance, Corvinus University of Budapest since 2013. Kata graduated in finance in 2009 from Corvinus University of Budapest and was awarded a PhD degree in 2012 for her thesis on the analysis of the market liquidity risk on the Hungarian stock market. Her research areas are market liquidity, fixed income securities, and networks in healthcare systems. Besides doing research, she is active in teaching as well. She mainly teaches corporate finance, investments, valuation, and multinational financial management.

Ágnes Vidovics-Dancs

Ágnes Vidovics-Dancs is a PhD candidate and an assistant professor at the Department of Finance, Corvinus University of Budapest. Previously, she worked as a junior risk manager in the Hungarian Government Debt Management Agency. Her main research areas are government debt management (in general) and sovereign crises and defaults (in particular). She is a CEFA and CIIA diploma holder.