Module 1: Python Introduction (8 lectures)

• Lecture 1: What is Python & Why Use It?

• Lecture 2: Installing Python, Anaconda & Jupyter Notebooks

• Lecture 3: Python IDEs & Google Colab

• Lecture 4: Syntax, Indentation & Comments

• Lecture 5: Variables, Data Types & Type Conversion

• Lecture 6: Basic Operators & Expressions

• Lecture 7: String & List Manipulations

• Lecture 8: File I/O & Exception Handling

Module 2: Functions (6 lectures)

• Defining Functions & Docstrings

• Positional vs. Keyword Arguments

• Default Params & *args/**kwargs

• Return Values & Multiple Returns

• Lambda Functions & map/filter

• Writing & Importing Your Own Modules

Module 3: Mathematical Operations (6 lectures)

• Numeric Types & Typecasting

• Math Module Functions

• Random Number Generation

• List Comprehensions for Calculations

• datetime Basics

• Working with decimal & fractions

Module 4: Data Manipulation (6 lectures)

• pandas Series & DataFrame Basics

• Importing CSV, Excel & JSON

• Indexing, Slicing & Filtering

• Handling Missing Data & Duplicates

• groupby & Aggregations

• Merging, Joining & Concatenation

Module 5: Visualization (6 lectures)

• Matplotlib: Line, Bar & Scatter Plots

• Customizing Titles, Labels & Legends

• Seaborn: Distributions & Categorical Plots

• Plot Aesthetics & Styles

• Saving Figures & Interactive Charts

• Intro to Plotly (Bonus)

Module 6: Capstone Project (1 lecture + deliverable)

• End-to-end mini-analysis: ingest → clean → explore → visualize

• Submission & GitHub portfolio tips

Module 1: Excel Essentials (8 lectures)

• Interface & Ribbon Tour

• Cell References & Data Entry

• Basic Formulas (SUM, AVERAGE, IF)

• Relative vs Absolute References

• Named Ranges

• Quick Analysis Tools

• Keyboard Shortcuts

• Workbook Management

Module 2: Data Cleanup & Prep (6 lectures)

• Text Functions (LEFT, RIGHT, MID)

• Flash Fill & Text to Columns

• Remove Duplicates & Data Validation

• Find & Replace Tricks

• Error Checking & Trace Dependents

• Basic Power Query Intro

Module 3: PivotTables & Slicers (6 lectures)

• Creating PivotTables

• Grouping & Drill-Down

• Calculated Fields & Items

• Inserting Slicers & Timelines

• Pivot Charts

• Refresh & Connection Options

Module 4: Power Query Basics (6 lectures)

• Getting & Transforming Data

• M-Language Fundamentals

• Merging & Appending Queries

• Pivot & Unpivot

• Parameterized Queries

• Loading to Data Model

Module 5: Charts & Dashboards (6 lectures)

• Chart Types & When to Use Them

• Customizing Chart Elements

• Sparklines & Conditional Formatting

• Building Interactive Dashboards

• Form Controls & Slicers

• Publishing to SharePoint/OneDrive

Module 6: Excel Dashboard Project (1 lecture + deliverable)

• Design & build a dynamic sales dashboard

• Share and present your workbook

Module 1: Descriptive Statistics (8 lectures)

• Lecture 1: Measures of Central Tendency (Mean/Median/Mode)

• Lecture 2: Measures of Dispersion (Range/Variance/Std Dev)

• Lecture 3: Data Distributions & Histograms

• Lecture 4: Boxplots & Outliers

Module 2: Probability & Distributions (6 lectures)

• Basic Probability Concepts

• Bayes’ Theorem & Applications

• Discrete Distributions: Binomial & Poisson

• Continuous Distributions: Normal & Exponential

• Sampling Distributions

• Central Limit Theorem

Module 3: Inferential Statistics (6 lectures)

• Point & Interval Estimation

• Confidence Intervals for Means & Proportions

• Margin of Error & Sample Size

• Introduction to Regression Analysis

• Assumptions & Model Diagnostics

• ANOVA Basics

Module 4: Hypothesis Testing & A/B Tests (6 lectures)

• Null & Alternative Hypotheses

• Type I & II Errors

• T-tests & Z-tests

• Chi-Square Tests

• Designing A/B Experiments

• Interpreting p-values & Results

Module 5: Stats in Tools (6 lectures)

• Excel Analysis Toolpak Overview

• Running Tests in Excel

• scipy.stats Functions in Python

Module 6: Statistics Mini-Project (1 lecture + deliverable)

• Conduct a hypothesis test on a real dataset

• Write a brief findings report

Module 1: SQL Basics (8 lectures)

• Lecture 1: Introduction to Relational Databases

• Lecture 2: SELECT, FROM, WHERE

• Lecture 3: Filtering & Sorting Data

• Lecture 4: DISTINCT, LIMIT, OFFSET

• Lecture 5: String & Date Functions

• Lecture 6: Numeric & Aggregate Functions

• Lecture 7: Aliasing & Comments

• Lecture 8: Practice Queries

Module 2: Aggregations & Grouping (6 lectures)

• GROUP BY Basics

• HAVING vs WHERE

• Rolling & Window Aggregates

• GROUPING SETS & CUBE (Intro)

• Nested Aggregations

• Performance Tips

Module 3: JOINS & Subqueries (6 lectures)

• Inner & Outer Joins

• Self-Joins

• Cross Joins & Cartesian Products

• Correlated Subqueries

• EXISTS vs IN

• JOIN Performance

Module 4: Database Design & Indexing (6 lectures)

• Entity-Relationship Modeling

• Normal Forms 1–3

• Primary & Foreign Keys

• Index Types & Creation

• Query Plans & EXPLAIN

• Backup & Restore Basics

Module 5: SQL Query Challenge (1 lecture + deliverable)

• Complex multi-table query on sample schema

• Write & optimize your solution

Module 1: Power BI Desktop & Data Sources (8 lectures)

• Installing & Navigating the Desktop

• Connecting to Excel, CSV & SQL Server

• Data Import vs DirectQuery

• Query Editor Basics

• Transformations (Clean & Shape)

• Merging & Appending Tables

• Parameters & Functions

• Data Refresh Settings

Module 2: Data Modeling & DAX (6 lectures)

• Relationships & Cardinality

• Star vs Snowflake Schemas

• Calculated Columns vs Measures

• Basic DAX Functions (SUMX, CALCULATE)

• Time Intelligence Functions

• Tabular Model Best Practices

Module 3: Building Reports & Visuals (6 lectures)

• Report Canvas Layout

• Native Visual Gallery

• Drill-through & Bookmarks

• Custom Visuals & Marketplace

• Tooltips & Report Themes

• Page Navigation

Module 4: Advanced Features (6 lectures)

• Row-Level Security

• Composite Models

• Aggregations & Incremental Refresh

• AI Visuals (Key Influencers, Decomposition Tree)

• Performance Analyzer

• Deployment Pipelines (Intro)

Module 5: Power BI Service & Sharing (6 lectures)

• Publishing Reports & Apps

• Workspaces & Roles

• Apps vs Dashboards

• Dataflows & Shared Datasets

• Power BI Mobile Overview

• Usage Metrics & Governance

Module 6: Capstone Dashboard (1 lecture + deliverable)

• Build a fully interactive executive dashboard

• Deploy & share with stakeholders

Module 1: ML Foundations & Familiarity (6 lectures)

• Lecture 1: What Is Machine Learning & When to Use It

• Lecture 2: The ML Pipeline: Data → Model → Evaluate

• Lecture 3: Supervised vs. Unsupervised Learning (High-Level)

• Lecture 4: Simple Regression Demo (Linear Regression Intuition)

• Lecture 5: Simple Classification Demo (Logistic Regression Intuition)

• Lecture 6: Overfitting vs. Underfitting & Train/Test Split

Module 2: Mini ML Exploration (1 lecture + deliverable)

• Build a tiny regression or classification model on a sample dataset

• Interpret results & share findings