These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book "Data Analytics: Principles, Tools, and Practices" can be considered a handbook or a guide for professionals who want to start their journey in the field of data science.

The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples.

After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science.

TABLE OF CONTENTS
1. Database Management System
2. Online Transaction Processing and Data Warehouse
3. Business Intelligence and its deeper dynamics
4. Introduction to Data Visualization
5. Advanced Data Visualization
6. Introduction to Big Data and Hadoop
7. Application of Big Data Real Use Cases
8. Application of Big Data
9. Introduction to Machine Learning
10. Advanced Concepts to Machine Learning
11. Application of Machine Learning