Data Analytics: Principles, Tools, and Practices: A Complete Guide for Advanced Data Analytics Using the Latest Trends, Tools

A Complete Data Analytics Guide for Learners and Professionals.

Key Features
● Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database.
● Dive into Machine Learning, its tools, and applications.
● Coverage of applications of Big Data, Data Analysis, and Business Intelligence.

Description
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.

What you will learn
● Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database.
● Learn to manage data warehousing with real time transaction processing.
● Explore various machine learning techniques that apply to data analytics.
● Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry.
● Acquaint yourself with Big Data tools and statistical techniques for machine learning.

Who this book is for
IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book.