Syllabus - Data Analytics
Data Analytics empowers organizations to make data-driven decisions by extracting meaningful insights from raw data. This comprehensive syllabus equips learners with analytical tools, techniques, and real-world applications across industries.
Session 1 – Introduction to Data Analytics
- What is Data Analytics?
- Types of Data Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
- Importance and Applications of Analytics
- Overview of Analytics Tools (Excel, SQL, Python, Power BI)
- Analytics Lifecycle and Framework
- Data Collection and Understanding
Session 2 – Excel and SQL for Data Analysis
- Data Cleaning and Formatting in Excel
- Pivot Tables and Charts
- Advanced Excel Functions for Analysis
- SQL Basics: SELECT, WHERE, GROUP BY, ORDER BY
- Joins, Subqueries, and Aggregations
- Hands-on SQL Querying with Sample Datasets
Session 3 – Data Visualization with Power BI
- Getting Started with Power BI
- Data Import and Transformation
- Building Dashboards and Reports
- Using DAX for Calculations
- Visualizing Trends and KPIs
- Publishing and Sharing Reports
Session 4 – Python for Data Analytics
- Python Basics for Analysts
- Pandas for Data Manipulation
- NumPy for Numerical Operations
- Data Cleaning and Transformation
- Visualizations using Matplotlib and Seaborn
- Real-world Data Analysis Projects
Session 5 – Statistical Techniques for Analysis
- Descriptive Statistics (Mean, Median, Mode)
- Correlation and Causation
- Data Distributions and Visualization
- Hypothesis Testing & Confidence Intervals
- Trend, Forecasting, and Time Series Basics
- Outlier Detection and Data Imputation
Session 6 – Projects and Business Applications
- Building End-to-End Data Analytics Projects
- Domain-Specific Use Cases (Sales, Marketing, HR, Finance)
- Storytelling with Data
- Creating Dashboards for Stakeholders
- Data-Driven Decision Making
- Capstone Project and Presentation