Data Analytics
Learn how to collect, clean, analyze, and visualize data using industry-standard tools like Excel, SQL, Python, and BI platforms.
Duration
3Â Months
Level
Beginner to Advanced
Format
Online
Projects
hands-on projects and a capstone project
Curriculum
10 Modules • 6+ Hands on projects
1. Introduction to Data Analytics & Business Thinking
What is Data Analytics and how it drives business decisions
Types of analytics: Descriptive, Diagnostic, Predictive, Prescriptive
Data analytics lifecycle and real-world use cases
Introduction to data-driven decision making
Tools overview: Excel, SQL, Python, BI tools
2. Data Fundamentals & Excel for Analytics
Data types, structures, and formats
Data collection and data quality fundamentals
Excel basics for analytics
Formulas, functions, and conditional logic
Sorting, filtering, and basic data cleaning
3. Advanced Excel & Data Analysis Techniques
Pivot tables and pivot charts
Lookup functions (VLOOKUP, XLOOKUP, INDEX-MATCH)
Data validation and error handling
Basic statistical analysis in Excel
Creating dashboards in Excel
4. SQL for Data Analytics
Introduction to databases and relational concepts
Writing SQL queries (SELECT, WHERE, ORDER BY)
Filtering, sorting, and aggregations
Joins and subqueries
Real-world data querying scenarios
5. Python for Data Analysis
Python basics for data analytics
Working with NumPy and Pandas
Data loading, cleaning, and transformation
Exploratory Data Analysis (EDA)
Handling missing and inconsistent data
6. Statistics for Data Analytics
Descriptive statistics (mean, median, variance)
Probability and distributions
Correlation and regression basics
Hypothesis testing and confidence intervals
Interpreting statistical results for business decisions
7. Data Visualization & Storytelling
Principles of effective data visualization
Charts, graphs, and dashboards best practices
Introduction to BI tools (Power BI / Tableau)
Creating interactive dashboards
Data storytelling for stakeholders
8. Advanced Analytics & Predictive Techniques
Introduction to predictive analytics
Regression models and forecasting
Time series analysis basics
Clustering and segmentation concepts
Model evaluation and interpretation
9. Real-World Projects & Case Studies
Industry-based datasets (marketing, finance, operations)
End-to-end data analysis workflow
Problem framing and KPI identification
Insight generation and recommendations
Presentation of findings
10. Capstone Project & Career Readiness
Capstone project using real-world datasets
Data cleaning, analysis, visualization, and insights
Project documentation and storytelling
Resume & portfolio guidance for data analytics roles
Interview preparation and career roadmap
11. Assessments and Certifications
Assessments
- Quizzes after each module
- Mid-term project evaluation
- Final project evaluation
Certifications
- Complete online exams
- Obtain course completion certificates
