Introduction to Data Mining and Analytics With Machine Learning in R and Python

Description

Introduction to Data Mining and Analytics With Machine Learning in R and Python provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation. With a dual focus on concepts and operations, this textbook comprises a complete how-to and is an excellent resource for anyone considering the field.

Case studies and hands-on activities incorporate real-world data sets and allow students the opportunity to exercise their new skills. Our Cloud Desktop integrates popular data mining tools, giving students a valuable familiarity with industry-standard applications.

After defining the concepts of data mining and machine learning, Introduction to Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use. The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world context through a practical application to the current issues of “big data” and of text and image data mining. The text concludes by describing an analyst’s steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace.

Table of Contents

1. Chapter 1 Data Mining and Analytics
2. Chapter 2 Machine Learning
3. Chapter 3 Databases
4. Chapter 4 Data Visualization
5. Chapter 5 Keep Excel in Your Toolkit
6. Chapter 6 SQL
7. Chapter 7 NoSQL Data Analytics
8. Chapter 8 Programming Data Analytics
9. Chapter 9 Data Preprocessing and Cleansing
10. Chapter 10 Clustering
11. Chapter 11 Classification
12. Chapter 12 Predictive Analytics
13. Chapter 13 Data Association
14. Chapter 14 Mining Text and Images
15. Chapter 15 Big Data Analytics
16. Chapter 16 Planning Data Projects

Author Biography

Dr. Kris Jamsa wrote his first computer program in Algol, using punched cards, while attending the United States Air Force Academy. Since then, he has spent his career wrangling data and programs that use it. Jamsa has a Ph.D. in Computer Science, a second Ph.D. in Education and Masters’ Degrees in Computer Science, Information Security, Project Management, Education, and Business. He is the author of 115 books on all aspects of programming and computing.

Kris lives with his wife, Debbie, on their ranch in Prescott, Arizona. When he is not in front of a computer screen, you can find him spending time with their horses, dogs, and grand kids.