This Online Data Analytics course is designed to help learners understand the complete data analytics lifecycle—from raw data collection to insight-driven decision-making. The program is suitable for beginners as well as early-career professionals who want to build strong analytical foundations and practical skills using industry-relevant tools such as Excel, SQL, Python, Power BI, and Tableau.

The course takes a structured, application-oriented approach, ensuring learners not only understand concepts but also learn how to apply them to real-world datasets and business scenarios. With hands-on projects, case studies, and practical assignments, students gain confidence in working with data and presenting insights effectively.

What Students Will Learn

By the end of this course, learners will be able to:

• Understand data types, data structures, and the data analytics process

• Collect, clean, and preprocess data for analysis

• Use MS Excel for data handling, formulas, pivot tables, and basic analysis

• Write SQL queries to extract and manipulate data from databases

• Perform Exploratory Data Analysis (EDA) using Python

• Apply descriptive and inferential statistics to analyze trends and patterns

• Understand probability concepts and hypothesis testing

• Create clear and meaningful data visualizations

• Build interactive dashboards using Power BI and Tableau

• Interpret analytical results for business decision-making

• Get an introduction to machine learning concepts such as:

  • Regression

  • Classification

  • Clustering

• Work on real-world datasets through practical assignments and case studies

Teaching Method & Course Structure

This course is delivered through online interactive sessions, focusing on concept clarity and hands-on learning. Each topic is explained step by step and supported with practical demonstrations.

Teaching approach includes:

• Live online classes with real-time interaction

• Tool-based learning using Excel, Python, SQL, Power BI, and Tableau

• Practical exercises after each module

• Case-study-based explanations for business relevance

• Hands-on projects to reinforce learning

• Regular doubt-clearing and revision sessions

• Assignments designed to simulate real analytics tasks

The learning pace is structured yet flexible, allowing learners to gradually build confidence as they progress through the modules.

Why Learn from This Course

This course emphasizes practical application over theory-heavy learning. The focus is on developing job-ready analytics skills that are relevant across industries such as business, finance, marketing, operations, and technology.

Tutor V R Manasi Manasan follows a structured teaching approach that connects analytical concepts with real-world use cases, helping learners understand why a technique is used and how it adds value in decision-making scenarios. The course avoids unnecessary complexity and focuses on clarity, application, and skill development.

Mode of Teaching

• Online classes

• Interactive and practice-oriented sessions

• Project-based learning support

Who Should Join This Course

• Students interested in starting a career in data analytics

• Working professionals looking to upskill in analytics tools

• Beginners seeking structured guidance in data analysis

• Learners interested in data visualization and dashboards

• Anyone aiming to understand data-driven decision-making

Benefits & Learning Outcomes

After completing the course, learners will gain:

• A clear understanding of the end-to-end data analytics workflow

• Strong hands-on experience with analytics tools

• Ability to analyze data and extract meaningful insights

• Confidence in building dashboards and visual reports

• Foundational knowledge of machine learning concepts

• Practical exposure through assignments and case studies

• Improved analytical thinking and problem-solving skills