Practical Data Science with R shows helpful statistical techniques for everyday business situations and ways for using the R programming language. Without plenty of academic theory or advanced mathematics, this R language is associated with tools which give simple ways to tackle with day-to-day data science tasks .
In this book, you will learn the statistical analysis techniques to explain examples which are based mostly on decision support, business intelligence, and marketing. This is the book for you if you are a data scientist, want to be a data scientist, or want to work with data scientists.
This is a good “what next” book for analysts and programmers wanting to know more about machine learning and data wrangling. Concept of this book is to present data science from a pragmatic, practice-oriented viewpoint.
What you will learn
• How to work as a data scientist. Learn how important listening, collaboration, honest presentation, and iteration are to and what we do.
• The key significance of the book is loading data, collecting requirements, validating models, examining data, deploying models to production, building models and documenting.
• This provides over 10 significant examples of datasets and demonstrates the concepts which are discussed with fully worked exercises using standard R methods.
• It will demonstrate all the preparatory steps necessary for any real-world project. Every result and almost every graph in the book is given as a fully worked example.
• It is scrupulously correct on statistics, but presents topics in the context and order a practitioner worries about them.
This book focuses primarily on R, but also uses several other domain-specific languages (DSLs) and even touches on languages such as the UNIX shell and C, also illustrate the process by which programmers approach a problem and implement the solution in different ways. This book has 3 parts, with each part having a general theme.
Part I contains case studies that involve reading and transforming raw data, manipulating and visualizing them, and then using statistical techniques to try to solve a problem or understand relationships between variables.
Part II focuses on using simulation to understand stochastic processes for their own sake and also explore how to use simulation to model interesting situations.
Part III explores different data technologies. These include databases, visualization with KML, and scraping data from Web pages with HTTP requests and text processing.
The scope of this book is wide, covering three main topics:
• Applications of R to specific disciplines
• For the study of topics of the statistical methodology by Using R
• The development of R also including building packages, programming, and graphics
What you will learn
• Non-standard data formats (robot logs, email messages)
• Text processing and regular expressions
• Newer/less-traditional technologies (Web scraping, Web services, JSON, XML, HTML, KML and Google Earth™)
• Statistical methods (classification trees, k-nearest neighbors, naïve Bayes)
• Visualization and exploratory data analysis; • relational databases and SQL
• Implementing algorithms
• Large data and efficiency
• Software design, development, and testing
• Using and interfacing to other languages such as the UNIX shell, C, and Python.
This book aims to teach you how to begin performing the data science tasks by taking advantage of R's powerful ecosystem of packages. R is the most widely used programming language and when used with data science, it can be a great combination to solve the problems involved with varied data sets in the real world. For statistical simulation to the users, it will provide a methodological and computational framework. This book is for them who want to learn about the computer-intense Monte-Carlo methods, the advanced features of R and computational tools for statistical simulation. Good knowledge of R programming is assumed/required.
You will learn five different simulation techniques in-depth using real-world case studies which are as follows-
1. Monte Carlo; 2. Discrete Event Simulation;
3. System Dynamics; 4 .Agent-Based Modeling;
It teaches the essential and fundamental concepts in statistical modeling and simulation. For explaining the statistical computing methods, it takes a practical and hands-on approach and gives advice on the usage of these methods. It provides computational tools to help you in solving common problems in statistical simulation and computer-intense methods. This book helps in uncovering the large-scale patterns of complex systems where interdependencies and variation are critical.
What You Will Learn
· Advanced R features to extract insights from your data and to simulate data
· How simulation project can be plan and structure to aid in the presentation of results and also in the decision-making process.
· To simulate distributions, data sets, and populations is done by seeing random number simulation
· For solving scientific and real-world problems by using design statistical solutions with R
· High-performance computing and advanced data manipulation
· Comprehensive coverage of several R statistical packages like simPop, boot, VIM, and many more.