DA-TOOLS.AW1

Data Analytics: Principles, Tools, and Practices

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

12 Interactive Lessons and 84 topics mapped to the official exam objectives

Beginner Self-paced · 1 year access

12Interactive Lessons

84Topics

01 / Skills you'll get

What you will be able to do

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!

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.

Course Highlights

12 Structured Lessons Comprehensive coverage of core course objectives
1 Year Full Access Self-paced learning accessible anytime on all devices

02 / Lessons & labs

See exactly what you will learn and practice

Download outline (PDF)

Lessons

12 Interactive Lessons · 84 topics

01 Preface

02 Database Management System 4 topics

Database and database management system
DB objects
Conclusion
Questions

03 Online Transaction Processing and Data Warehouse 8 topics

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

04 Business Intelligence and Its Deeper Dynamics 7 topics

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

05 Introducing Data Visualization 9 topics

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

06 Advanced Data Visualization 5 topics

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

07 Introduction to Big Data and Hadoop--Too Huge to Avoid 5 topics

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

08 NoSQL and MapReduce--Too Huge to Avoid 8 topics

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

09 Application of Big Data—Real Use Cases 12 topics

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 4 topics

Introduction
Need for machine learning
Supervised learning
Conclusion

11 Advanced Concepts to Machine Learning: Making Machine to Run the Show 9 topics

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 13 topics

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

03 / FAQs

Questions before you start

Who is this course for?

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.

Do I need prior experience in data analytics to enroll?

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.

Can I take this course if I’m not from a technical background?

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

What is the annual salary of a data analyst in the US?

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.

1 year of full access
Certificate of completion