Introduction to Data Science. A Python Approach to Concepts, Techniques and Applications

✍ Scribed by Laura Igual, Santi Segu

• Publisher: Springer
• Year: 2017
• Tongue: English
• Leaves: 227
• Series: Undergraduate Topics in Computer Science
• Category: Library

✦ Synopsis

This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book supports understanding through hands-on experience of solving data science problems using Python describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming reviews a range of applications of data science, including recommender systems and sentiment analysis of text data provides supplementary code resources and data at an associated website.

✦ Table of Contents

Front Matter....Pages i-xiv
Introduction to Data Science....Pages 1-4
Toolboxes for Data Scientists....Pages 5-28
Descriptive Statistics....Pages 29-50
Statistical Inference....Pages 51-65
Supervised Learning....Pages 67-96
Regression Analysis....Pages 97-114
Unsupervised Learning....Pages 115-139
Network Analysis....Pages 141-164
Recommender Systems....Pages 165-179
Statistical Natural Language Processing for Sentiment Analysis....Pages 181-197
Parallel Computing....Pages 199-215
Back Matter....Pages 217-218