Python Basics (Variables, Loops, Functions)

Data Types & Operations

List, Tuple, Dictionary, Set

Libraries: NumPy, Pandas

Writing Clean and Reusable Code

Reading/Writing Data (CSV, Excel, JSON)

DataFrames & Series

Indexing, Filtering, Sorting

Handling Missing Values

Data Aggregation & Grouping

Matplotlib & Seaborn Basics

Creating Charts: Line, Bar, Pie, Histogram, Heatmap

Data Storytelling Techniques

Plotly for Interactive Visualizations

Descriptive Statistics

Probability Theory

Distributions: Normal, Binomial, Poisson

Hypothesis Testing & Confidence Intervals

Correlation vs Causation

Outlier Detection & Handling

Feature Engineering

Data Transformation

Detecting Multicollinearity

Real Dataset Analysis (e.g., Titanic, Sales, Healthcare)

Introduction to ML Algorithms

Linear Regression & Logistic Regression

Decision Trees & Random Forests

Model Evaluation (MAE, RMSE, Accuracy, Confusion Matrix)

Cross Validation

Clustering (K-Means, Hierarchical)

Dimensionality Reduction (PCA)

Association Rules (Apriori)

Anomaly Detection