Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other ProfessionalsUsing the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution.Drawing on his unsurpassed experie...

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in PythonToday, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple ...

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.Updated for R 2.14 and 2.15, this second edition includes...

Key FeaturesBook DescriptionUpdated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover...

Understand, evaluate, and visualize dataAbout This BookLearn basic steps of data analysis and how to use Python and its packagesA step-by-step guide to predictive modeling including tips, tricks, and best practicesEffectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visuali...

Because there is so much of a buzz around the R programming language in the data science and analytics world, in health-care and other domains, I wrote a book on R-programming for data analysis. The book aims to teach data analysis using R within a single day to anyone who already knows some programming in any other language. The book has sample code (which can be downloaded as a zip file from the Dropbox link provided in the text of the book) that uses the freely available, CMS-sourced Me...

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includ...

Leverage the power of Python to clean, scrape, analyze, and visualize your dataAbout This BookClean, format, and explore your data using the popular Python libraries and get valuable insights from itAnalyze big data sets; create attractive visualizations; manipulate and process various data types using NumPy, SciPy, and matplotlib; and morePacked with easy-to-follow examples to develop advanced computational skills for the analys...

Build Machine Learning models with a sound statistical understanding.Key FeaturesLearn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics.Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering.Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python.Book DescriptionCompl...

Solve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyondHarness the power of R to build flexible, effective, and transparent machine learning modelsLearn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett LantzBook DescriptionMachine learning, at i...

Gain practical insights by exploiting data in your business to build advanced predictive modeling applicationsKey FeaturesA step-by-step guide to predictive modeling including lots of tips, tricks, and best practicesLearn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and ClusteringMaster open source Python tools to build sophisticated predictive models...

The book will start with quick introductions to Python and its ecosystem libraries for data science such as JupyterLab, Numpy, Pandas, SciPy, Matplotlib, and Seaborn.This book will help in learning python data structures and essential concepts such as Functions, Lambdas, List comprehensions, Datetime objects, etc. required for data engineering. It also covers an in-depth understanding of Python data science packages where JupyterLab used as an IDE for writing, documenting, and executing th...