Practical Data Science Cookbook, 2nd Edition
- Length: 458 pages
- Edition: 2nd Revised edition
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2017-07-06
- ISBN-10: 1787129624
- ISBN-13: 9781787129627
- Sales Rank: #1125287 (See Top 100 Books)
Key Features
- Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
- Get beyond the theory and implement real-world projects in data science using R and Python
- Easy-to-follow recipes will help you understand and implement the numerical computing concepts
Book Description
As an increasing amount of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data have a competitive advantage over companies that don’t, and this drives a higher demand for knowledgeable and competent data professionals.
Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis-R and Python.
What you will learn
- Get to know the installation procedure and environment required for R and Python on various platforms
- Implement data science concepts such as acquisition, munging, and analysis through R and Python
- Analyze and produce reports on data
- Perform some text mining
- Build a predictive model and an exploratory model
- Build various tree-based methods and Build random forest
Table of Contents
Chapter 1. Preparing Your Data Science Environment
Chapter 2. Driving Visual Analysis with Automobile Data with R
Chapter 3. Creating Application-Oriented Analyses Using Tax Data and Python
Chapter 4. Modeling Stock Market Data
Chapter 5. Visually Exploring Employment Data
Chapter 6. Driving Visual Analyses with Automobile Data
Chapter 7. Working with Social Graphs
Chapter 8. Recommending Movies at Scale (Python)
Chapter 9. Harvesting and Geolocating Twitter Data (Python)
Chapter 10. Forecasting New Zealand Overseas Visitors
Chapter 11. German Credit Data Analysis
