nach der Bestellung gedruckt Neuware - Printed after ordering - 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 CONTENTS1. Database Management System2. Online Transaction Processing and Data Warehouse3. Business Intelligence and its deeper dynamics4. Introduction to Data Visualization5. Advanced Data Visualization6. Introduction to Big Data and Hadoop7. Application of Big Data Real Use Cases8. Application of Big Data9. Introduction to Machine Learning10. Advanced Concepts to Machine Learning11. Application of Machine Learning.

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Bibliographic Details

Title: Data Analytics : Principles, Tools, and Practices: A Complete Guide for Advanced Data Analytics Using the Latest Trends, Tools, and Technologies: Principles, Tools, and Practices:
Publisher: BPB Publications
Publication Date: 2022
Language: English
ISBN 10: 9388511956
ISBN 13: 9789388511957
Binding: Taschenbuch
Condition: Neu
Dimensions: 235 millimeters width by 191 millimeters height by 23 millimeters depth
Weight: 780 grams

About this title

Synopsis

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.

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

"About this title" may belong to another edition of this title.

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