A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming
A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy–to–understand terms and does not require familiarity with statistics or programming. The authors noted experts in the field highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples.
Thought to be easily accessible to non–experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems. The learning resources offer:
- A guide to the reasoning behind data mining techniques
- A unique illustrative example that extends throughout all the chapters
- Exercises at the end of each chapter and larger projects at the end of parts II and III of the book
- Supplemented with PowerPoint slides available for instructors on a Wiley Book Companion Site
Together with these learning resources, the book can be used in a 13–week course guide, one chapter per course topic.
The book was written in a format that allows the understanding of the main data analytics concepts by non–mathematicians, non–statisticians and non–computer scientists interested in getting an introduction to data science. A General Introduction to Data Analytics is a basic guide to data analytics written in highly accessible terms.
Part I: Introductory Background
Chapter 1: What can we do with data?
Part II: Getting Insights from Data
Chapter 2: Descriptive statistics
Chapter 3: Descriptive Multivariate Analysis
Chapter 4: Data quality and pre–processing
Chapter 5: Clustering
Chapter 6: Frequent pattern mining
Chapter 7: Résumé and project on descriptive analytics
Part III: Predicting the Unknown
Chapter 8: Regression
Chapter 9: Classification
Chapter 10: Additional predictive methods
Chapter 11: Advanced predictive topics
Chapter 12: Résumé and Project on predictive analytics
Part IV: Popular Data Analytics Applications
Chapter 13: Applications for Text, Web and Social Media