Module 1: Introduction to Data Science
→ Overview of data science lifecycle & industry applications
→ Setting up your data science environment (Python, Jupyter, RStudio)
→ Understanding structured & unstructured data
Module 2: Data Wrangling & Preprocessing
→ Cleaning & transforming datasets using Pandas & NumPy
→ Handling missing values, outliers & categorical data
→ Exploratory Data Analysis (EDA) for pattern recognition
Module 3: Machine Learning Fundamentals
→ Introduction to Supervised & Unsupervised Learning
→ Building models using Linear Regression, Decision Trees & Clustering
→ Model evaluation using precision, recall & accuracy metrics
Module 4: Data Visualization & Business Intelligence
→ Creating dashboards with Tableau, Power BI & Seaborn
→ Interactive charts, heatmaps & trend analysis
→ Real-world project: Visualizing sales & customer datay
Module 5: Statistical Inference & Predictive Modeling
→ Probability & hypothesis testing for decision-making
→ Understanding p-values, confidence intervals & distributions
→ Predicting trends using time series analysis & forecasting
Module 6: Hands-on Project & Real-World Applications
→ Sentiment analysis with Natural Language Processing (NLP)
→ Customer segmentation using K-Means clustering
→ Fraud detection with anomaly detection algorithms
