The Ultimate Data analyst Bootcamp for Beginners

📑 Detailed Masterclass Curriculum

Phase 1: Microsoft Excel (The Foundation)

Before writing code, students must understand how data behaves, how to clean it, and how to analyze it visually.

• Data Cleaning: Removing duplicates, handling blanks, and using Text-to-Columns.

• Advanced Formulas: XLOOKUP(), INDEX(MATCH), IFS(), and COUNTIFS().

• Data Summarization: Building dynamic Pivot Tables and custom Calculated Fields.

• Intro to Automation: Using Power Query to import, transform, and merge messy datasets.

Phase 2: SQL (Database Management & Querying)

Excel has row limits; SQL does not. This phase teaches how to extract and analyze massive datasets directly from relational databases.

• The Basics: SELECT, FROM, WHERE, ORDER BY, and LIMIT.

• Aggregation: Using GROUP BY, HAVING, and aggregate functions (SUM, AVG, COUNT).

• Data Merging: Mastering INNER JOIN, LEFT JOIN, RIGHT JOIN, and UNION.

• Advanced Queries: Subqueries, Common Table Expressions (CTEs), and Window Functions (RANK(), ROW_NUMBER()).

Phase 3: Power BI (Business Intelligence & Visualization)

Transforming raw numbers into interactive, automated visual dashboards that executives can actually understand.

• Data Modeling: Connecting multiple data sources and building Star Schemas (Fact vs. Dimension tables).

• DAX (Data Analysis Expressions): Writing custom metrics using CALCULATE(), FILTER(), and Time Intelligence functions (e.g., Year-to-Date sales).

• Visual Storytelling: Choosing the right charts (bar, line, scatter, map) and designing professional dashboard layouts.

• Interactivity: Adding slicers, drill-throughs, and tooltips for deep-dive analysis.

Phase 4: Python (Advanced Analytics & Automation)

The industry standard for statistical analysis, predictive modeling, and heavy data transformation.

• Python Basics: Variables, loops, lists, and dictionaries.

• Pandas Library: The "Excel of Python." Reading CSVs, filtering dataframes, handling missing values, and grouping data.

• Matplotlib & Seaborn Libraries: Creating advanced, customizable data visualizations (heatmaps, distribution plots).

• Automation: Writing scripts to automate daily data extraction and reporting tasks.