📍 Module 1: Introduction to Data Science

• What is Data Science?

• Roles: Data Scientist vs Analyst vs Engineer

• Overview of Data Science Lifecycle

• Applications Across Industries

📍 Module 2: Python for Data Science

• Python Basics (Variables, Data Types, Operators)

• Control Flow (Loops, Conditions)

• Functions, Modules & Packages

• Working with Files

• List Comprehensions and Lambda Functions

📍 Module 3: Working with Libraries

• NumPy: Arrays, Indexing, Broadcasting

• Pandas: DataFrames, Series, Data Cleaning

• Matplotlib & Seaborn: Data Visualization

• Working with CSV, Excel, and JSON files

• Handling Missing Data & Duplicates

📍 Module 4: Data Wrangling & EDA

• Data Aggregation and Grouping

• Sorting, Filtering, and Merging Datasets

• Descriptive Statistics & Summary Measures

• Exploratory Data Analysis (EDA) Techniques

• Correlation Matrix & Heatmaps

📍 Module 5: Data Visualization Techniques

• Line, Bar, Pie, Histogram, Box Plots

• Advanced Plots: Pairplot, Violin, Distribution

• Plot Customization (Labels, Titles, Legends)

• Dashboards using Plotly (Optional)

📍 Module 6: Statistics for Data Science

• Probability, Mean, Median, Mode, Standard Deviation

• Hypothesis Testing

• Confidence Intervals

• Normal Distribution & Z-score

• t-Test, Chi-Square Test

📍 Module 7: Machine Learning with Scikit-Learn

• Supervised vs Unsupervised Learning

• Train/Test Split & Cross-Validation

• Linear & Logistic Regression

• Decision Trees and Random Forest

• KNN, Naive Bayes, SVM

• Clustering: K-Means, Hierarchical Clustering

• Model Evaluation Metrics (Accuracy, Confusion Matrix, Precision, Recall, F1 Score)

📍 Module 8: Advanced Concepts

• Feature Engineering & Selection

• Dimensionality Reduction: PCA

• Time Series Forecasting Basics

• Text Data Processing (NLP Basics)

• Introduction to Deep Learning (Optional with Keras/TensorFlow)

📍 Module 9: Working on Real Projects

• Predictive Analysis (e.g., House Price Prediction)

• Customer Segmentation

• Sales Forecasting

• Fraud Detection

• Real-time EDA & Dashboard Creation

📍 Module 10: Deployment & Interview Prep

• Exporting Models with Pickle/Joblib

• Model Deployment Concepts (Flask, Streamlit)

• Building a Data Science Portfolio (GitHub)

• Resume Preparation

• Data Science Interview Q&A Practice