The Beginner's Guide to Data Science

This book discusses the principles and practical applications of data science, addressing key topics including data wrangling, statistics, machine learning, data visualization, natural language processing and time series analysis. Detailed investigations of techniques used in the implementation of recommendation engines and the proper selection of metrics for distance-based analysis are also covered.

Utilizing numerous comprehensive code examples, figures, and tables to help clarify and illuminate essential data science topics, the authors provide an extensive treatment and analysis of real-world questions, focusing especially on the task of determining and assessing answers to these questions as expeditiously and precisely as possible. This book addresses the challenges related to uncovering the actionable insights in “big data,” leveraging database and data collection tools such as web scraping and text identification.

This book is organized as 11 chapters, structured as independent treatments of the following crucial data science topics:

Data gathering and acquisition techniques including data creation
Managing, transforming, and organizing data to ultimately package the information into an accessible format ready for analysis
Fundamentals of descriptive statistics intended to summarize and aggregate data into a few concise but meaningful measurements
Inferential statistics that allow us to infer (or generalize) trends about the larger population based only on the sample portion collected and recorded