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Data Analytics: Principles, Tools, and Practices

Upskill, reskill, and let data analytics become your new superpower. No cape required.

(DA-TOOLS.AW1) / ISBN : 978-1-64459-665-4
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About This Course

Prepare to master data analytics fundamentals with this interactive training program. 

Through bite-sized lessons and hands-on examples, you’ll explore various data analytics domains, from database management and data visualization to Big Data tools and machine learning (ML) techniques. The online data analytics training covers Apache Spark, Hadoop, NoSQL, and advanced visualization tools. 

So, gear up because a world of opportunities in data analytics awaits you!

Skills You’ll Get

  • Data analytics fundaments including DBMS, RDBMS, NoSQL, and DocumentDB.
  • Manage data warehousing and real-time transaction processing.
  • Use Big Data tools like Apache Spark, Apache Hive, MapReduce, and Hadoop Distributed File System (HDFS).
  • Apply ML techniques for predictive analytics and data analysis.
  • Visualize data using graphs, charts, and tools like Tableau and Highcharts.
  • Design and implement dimensional modeling for data warehouses.
  • Understand ETL processes and tools for data integration.
  • Explore advanced data visualization trends and create interactive dashboards.
  • Work with structured and unstructured data, including Data Lakes.
  • Apply Big Data and ML in real-world industries like healthcare, finance, retail, and media.
  • Build deep learning architectures like YOLO and Natural Language Processing (NLP).
  • Gain hands-on experience with statistical techniques and programming MapReduce jobs.
  • Develop business intelligence strategies to improve data quality and decision-making.
  • Understand the ethical responsibilities and applications of AI, ML, and Big Data analytics in various sectors. 
Download Course Outline

1

Preface

2

Database Management System

  • Database and database management system
  • DB objects
  • Conclusion
  • Questions
3

Online Transaction Processing and Data Warehouse

  • Introduction
  • Online transaction processing
  • Need of data warehouse
  • Dimensional modeling used in DW design
  • Types of schemas
  • ETL and other tools sets available in market
  • Conclusion
  • Questions
4

Business Intelligence and Its Deeper Dynamics

  • Business intelligence
  • Data quality: a real challenge
  • Structured Versus Unstructured
  • Data Lake
  • Modern business intelligence system
  • Conclusion
  • Questions
5

Introducing Data Visualization

  • Presenting data visualization
  • Ring chart
  • Stacked area chart
  • Visualization dashboards
  • Introduction to reporting tools
  • Tableau
  • High charts
  • Conclusion
  • Questions
6

Advanced Data Visualization

  • Types of advanced data visualization
  • Data visualization trends
  • Introducing data visualization tools
  • Conclusion
  • Questions
7

Introduction to Big Data and Hadoop--Too Huge to Avoid

  • Introduction and need of big data
  • Introducing Hadoop and HDFS system
  • The Hadoop distributed file system
  • Conclusion
  • Points to remember
8

NoSQL and MapReduce--Too Huge to Avoid

  • What is NoSQL?
  • Uses of NoSQL
  • Types of NoSQL database
  • Programming MapReduce jobs
  • What is MapReduce?
  • How MapReduce works?
  • Conclusion
  • Points to remember
9

Application of Big Data—Real Use Cases

  • Big data applications in healthcare
  • What is big data in healthcare?
  • Why do we need big data analytics in healthcare?
  • Twelve big data applications in healthcare
  • List of 12 big data examples in healthcare
  • Applications of big data in finance
  • Big data applications in retail
  • Big data benefits for retail
  • Big data application in travel
  • Big data applications in media
  • Conclusion
  • Questions
10

Introducing Machine Learning - Making Machine to Run the Show

  • Introduction
  • Need for machine learning
  • Supervised learning
  • Conclusion
11

Advanced Concepts to Machine Learning: Making Machine to Run the Show

  • Predictive learning--predictive analytic and machine learning
  • Predictive analytics models
  • Deep learning
  • Deep learning architecture
  • Deep Learning Algorithms
  • YOLO--You Just Look Once
  • Natural Language Programming
  • Conclusion
  • Points to remember
12

Application of Machine Learning

  • Machine learning applications in healthcare
  • Machine learning applications in finance
  • Machine learning applications in retail
  • Key benefits of machine learning in retail
  • Wrapping up
  • Advantages of predictive analytics for machine learning in retail
  • Applications in Brick-and-Mortar retail
  • Machine learning applications in travel
  • ML, AI, and big data analytics in the travel and hospitality industry
  • Machine learning applications in media
  • The future of machine learning
  • Conclusion
  • Points to remember

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This course is ideal for IT graduates, data engineers, and entry-level professionals who want to build a strong foundation in data analytics. It’s also perfect for anyone looking to upskill or reskill in data science tools, Big Data, and ML. 

No prior experience is required. This course starts with the basics of data analytics and gradually progresses to advanced topics, making it suitable for beginners and those with some foundational knowledge. 

Yes! The course is designed to be beginner-friendly, with clear explanations and practical labs to help non-technical learners grasp the concepts. 

The average annual salary of a data analyst in the U.S. is around 75,000 to 126,000+.

Develop Data Skills

  Data doesn’t lie, and neither will your skills after mastering this course. Start your journey today. 

$239.99

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