Curriculum
- 12 Sections
- 69 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Introduction to Data AnalyticsBy the end of this module, learners should understand: ✔ What data analytics is ✔ Various types of analytics ✔ The end-to-end data lifecycle ✔ Different roles in a data organization4
- MODULE 2 – Excel Foundations for Data AnalyticsLearners can now: ✔ Navigate Excel confidently ✔ Apply essential formulas for calculations ✔ Clean and prepare raw datasets effectively3
- MODULE 3 – Advanced Excel & DashboardsBy the end of Module 3, learners can: ✔ Build PivotTables and PivotCharts ✔ Use slicers to enhance interactivity ✔ Design a complete dashboard in Excel ✔ Apply advanced functions like VLOOKUP and INDEX/MATCH4
- MODULE 4 – SQL FoundationsLearners understand SQL basics, how databases work, and can run fundamental queries using SELECT, WHERE, ORDER BY, DISTINCT, and LIMIT.3
- MODULE 5 – Intermediate SQLLearners can now combine tables, summarize data, and perform advanced filtering using JOINs, GROUP BY, and HAVING.12
- MODULE 6 – Data Cleaning & PreprocessingLearners can identify data issues, clean inconsistent datasets, and apply preprocessing techniques.14
- MODULE 7 – Python Foundations for AnalyticsLearners understand Python basics and can load datasets using simple scripts.14
- MODULE 8 – Data Analysis with PandasLearners can manipulate DataFrames, merge datasets, and perform exploratory analysis.3
- MODULE 9 – Data VisualizationLearners can create meaningful visuals and communicate findings effectively.3
- MODULE 10 – Power BI FoundationsLearners can import, clean, and visualize data in Power BI.3
- MODULE 11 – DAX & Advanced Power BILearners can build advanced dashboards and write DAX measures to compute business KPIs.3
- MODULE 12 – Capstone ProjectLearners demonstrate their end-to-end analytics capabilities through a practical, real-world project.3
Lesson 1.4 Key Roles in Data Analytics
- Data Analyst
Focuses on cleaning data, creating dashboards, writing queries, and producing reports.
- Business Analyst
Acts as a bridge between business and data teams, defining requirements and interpreting insights.
- Data Scientist
Builds predictive models using machine learning, statistics, and advanced algorithms.
- Data Engineer
Designs pipelines and manages data infrastructure to ensure efficient data flow.