1787129624
Practical Data Science Cookbook Second Edition
by: Prabhanjan Tattar – Bhushan Purushottam Joshi
ISBN-10: 1787129624
ISBN-13: 9781787129627
Edition 版次: 2nd Revised edition
Publication Date 出版日期: 2017-07-06
Print Length 页数: 458
Book Description
By finelybook
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
By finelybook
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
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
