1. Python setup (Anaconda, Jupyter Notebook)

2. Python basics: variables, loops, functions, data types

3. Libraries: NumPy, Pandas
   Practical : Write Python scripts to handle and manipulate sample datasets

1. Data sources: CSV, Excel, APIs, web scraping

2. Handling missing values, duplicates, outliers

3. Feature selection and encoding categorical data
   Practical : Clean a real-world dataset (e.g., Titanic, Sales, or Student dataset)

1. Visualization with Matplotlib, Seaborn, Plotly

2. Charts: histograms, scatter plots, bar charts, box plots

3. Insights extraction and reporting
   Practical : Visualize patterns in a dataset (e.g., sales trends or student performance)

1. Descriptive statistics: mean, median, mode, variance, standard deviation

2. Probability basics, distributions, correlation, hypothesis testing
   Practical : Analyze a dataset and calculate statistical measures in Python

1. Supervised vs Unsupervised Learning

2. Regression and Classification basics

3. Evaluation metrics: accuracy, precision, recall, R² score
   Practical : Build a simple regression/classification model (e.g., predict house prices)

1. Decision Trees, Random Forest, KNN, SVM

2. Clustering: K-Means, Hierarchical

3. Dimensionality reduction: PCA
   Practical : Apply ML algorithms to a real dataset and evaluate performance

1. Scikit-learn for ML

2. Statsmodels for statistical analysis

3. Optional: Light introduction to TensorFlow/Keras for AI/ML integration
   Practical : Train a machine learning model using Scikit-learn on a dataset

   Mini-project ideas:

1. Customer segmentation
2. Sales forecasting
3. Stock market prediction
4. Predictive analytics on student performance
   Practical : Students choose a dataset and create a mini-project

1. Deploy ML model using Streamlit, Flask, or Jupyter Notebooks

2. Present insights, visualizations, and predictive models

3. Create a portfolio of projects for internships or job applications
   Final Project : Complete and deploy a working Data Science application