Advanced Analytics in Power BI with R and Python... Ingesting, Transforming, Visualizing

Book

Brief

This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard to do, if not impossible, using native Power BI tools without Power BI Premium capacity. For example, you will learn to score Power BI data using custom data science models, including powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. If you are a BI developer, business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you to do that.

Introduction to R for Business

Brief

Book

Designed to provide Business students and professionals with a pragmatic introduction to R, RStudio, Git, GitHub, and GitKraken in the context of the Business Data Life Cycle. R is a powerful free open source statistical computing software that has emerged as the leading programming language for statistics and data analysis. It is well established in both academia and corporations for robustness, reliability and accuracy, but is still relatively new to the Caribbean region. R allows the business to combine statistics with computer science in order to extract new insights and new knowledge from the vast amount of data generated from the digital age. Based on real cases, the book allows you will sit at the computer and learn how to load, merge, clean, reshape, summarize, troubleshoot and transform data into mathematical functions, and produce ‘markdown’ reports using R. Eventually, the book positions you to immediately increase their usefulness to the organization, as well as to move on to more advanced courses such as Data Science Using R.

Hands-On Machine Learning with R

Book

Brief

This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results.

Practical Statistics for Data Scientists...Using R and Python

Book

Brief

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

Data Mining for Business Analytics...Concepts from R

Brief

Book

Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. This book is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Machine Learning and Artificial Intelligence with Just Enough R!

Brief

Book

Features Gets you quickly using R as a problem-solving tool Uses RStudio’s integrated development environment  Shows how to interface R with SQLite  Includes examples using R’s Rattle graphical user interface  Requires no prior knowledge of R, machine learning, or computer programming Offers over 50 scripts written in R, including several problem-solving templates that, with slight modification, can be used again and again  Covers the most popular machine learning techniques, including ensemble-based methods and logistic regression  Includes end-of-chapter exercises, many of which can be solved by modifying existing scripts  Includes datasets from several areas, including business, health and medicine, and science

Data Science for Marketing:...Improve your strategies...using Python and R

Brief

Book

Regardless of company size, the adoption of data science and machine learning for marketing is witnessing an exponential rise in the industry. With this book, you'll learn to implement data science techniques to identify the factors behind the successes and failures of marketing campaigns. With the help of this guide, you'll also be able to understand and predict customer behavior, and create more effectively targeted and personalized marketing strategies. You'll first get to grips with performing simple through to advanced tasks, such as extracting hidden insights from data and using them to make smart business decisions. The book will further guide you through understanding what drives sales and increases customer engagement for your products. As you explore further chapters, you'll not only learn how to gain insights into consumer behavior using exploratory analysis, but also discover the concept of A/B testing and implement it using Python and R.

Practical Time Series Analysis...with Statistics and Machine Learning

Brief

Book

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.

Machine Learning with R... Expert techniques for predictive modeling

Brief

Book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.  This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.