Get valuable insights from your data by building data analysis systems from scratch with R.

About This Book

• A handy guide to take your understanding of data analysis with R to the next level
• Real-world projects that focus on problems in finance, network analysis, social media, and more
• From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R

Who This Book Is For

If you are looking for a book that takes you all the way through the practical application of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis is all you need to get started with this book.

What You Will Learn

• Build end-to-end predictive analytics systems in R
• Build an experimental design to gather your own data and conduct analysis
• Build a recommender system from scratch using different approaches
• Use and leverage RShiny to build reactive programming applications
• Build systems for varied domains including market research, network analysis, social media analysis, and more
• Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively
• Communicate modeling results using Shiny Dashboards
• Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling

In Detail

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it's one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle.

You'll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You'll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You'll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes.

With the help of these real-world projects, you'll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.

By the end of this book, you'll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.

Style and approach

This book takes a unique, learn-as-you-do approach, as you build on your understanding of data analysis progressively with each project. This book is designed in a way that implementing each project will empower you with a unique skill set, and enable you to implement the next project more confidently.

Osoby które kupowały "R Data Analysis Projects", wybierały także:

• Databricks. Kurs video. Wst 148,98 zÅ‚, (59,59 zÅ‚ -60%)
• Apache NiFi. Kurs video. Automatyzacja przep 148,98 zÅ‚, (59,59 zÅ‚ -60%)
• Web scraping. Kurs video. Zautomatyzowane pozyskiwanie danych z sieci 139,00 zÅ‚, (55,60 zÅ‚ -60%)
• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zÅ‚, (67,05 zÅ‚ -55%)
• Excel 2013. Kurs video. Poziom drugi. Przetwarzanie i analiza danych 79,00 zÅ‚, (35,55 zÅ‚ -55%)

Spis treści

R Data Analysis Projects. Build end to end analytics systems to get deeper insights from your data eBook -- spis treści

• 1. Building a Recommender System –I – A step by step approach to build Association Rule Mining
• 2. Building a Recommender System II- Fuzzy Logic induced Content Based Recommendation
• 3. Building a Recommender System III – Collaborative Filtering based recommendation systems
• 4. Taming time series data – Time Series analysis using Recurrent Neural Networks
• 5. Text Sentiment Classification using Kernel Density Estimates
• 6. Record Linkage - Stochastic and Machine Learning Approaches
• 7. Streaming data Clustering analysis in R
• 8. Analyze and understand networks using R