Topics
● Introduction to Data Science & Data Analytics
 ○ What is Data Science? Roles & Applications
 ○ Data Analytics vs Business Analytics
 ○ Data Science Lifecycle

● Python Programming Basics
 ○ Variables, Data Types, Loops, Conditionals
 ○ Functions and Modules
 ○ Python Libraries for Data Analysis: NumPy, Pandas

● Mathematics & Statistics for Data Science
 ○ Descriptive Statistics: Mean, Median, Mode, Standard Deviation
 ○ Probability Basics
 ○ Correlation and Covariance

Hands-On Labs
● Python programming exercises
● Data cleaning using Pandas
● Basic statistical calculations on sample datasets

Mini Project
Analyze a dataset (e.g., sales or marketing data) and generate summary statistics

Topics
● Data Wrangling & Preprocessing
 ○ Handling Missing Values, Duplicates, and Outliers
 ○ Data Transformation & Normalization
 ○ Feature Engineering Basics

● Data Visualization
 ○ Matplotlib & Seaborn (Python)
 ○ Tableau & Power BI Basics
 ○ Plot types: Line, Bar, Histogram, Heatmap, Scatter

● SQL for Data Analysis
 ○ Basics: SELECT, WHERE, GROUP BY, JOINs
 ○ Advanced Queries: Subqueries, Window Functions

Hands-On Labs
● Visualize datasets using Python libraries and Tableau/Power BI
● Write SQL queries to extract insights from databases

Mini Project
Build an interactive dashboard for a business dataset using Power BI/Tableau

Topics
● Introduction to Machine Learning
 ○ Supervised vs Unsupervised Learning
 ○ Regression, Classification, and Clustering

● Supervised Learning
 ○ Linear Regression, Logistic Regression
 ○ Decision Trees, Random Forest, K-Nearest Neighbors
 ○ Model evaluation: Accuracy, Precision, Recall, F1 Score

● Unsupervised Learning
 ○ K-Means Clustering
 ○ Hierarchical Clustering
 ○ Dimensionality Reduction (PCA)

Hands-On Labs
● Build and evaluate predictive models using Python (scikit-learn)
● Implement clustering on sample datasets

Mini Project
Predict sales or customer behavior using a supervised ML model