Data Science Training Program

Class 1: Getting Started with Python

• Install Python; set up Jupyter, Colab, and Kaggle
• Learn basic syntax: variables, data types, loops, conditionals, and error handling
• Intro to GitHub version control

Class 2: Python Data Structures

• Work with lists, tuples, dictionaries, and sets
• Write functions (including lambda expressions) and do file I/O (CSV, text)
• Practice exercises on manipulating lists and dictionaries
  

Class 1: NumPy & Pandas

• Create and manipulate NumPy arrays: slicing, vectorization, broadcasting
• Use Pandas for merging, cleaning, handling missing data, and descriptive stats
• Reference: The NumPy Array: A Structure for Efficient Numerical Computation (van der Walt et al., 2011)

Class 2: Data Plotting & Simple Transformations

• Plot data using Matplotlib and Seaborn (line, bar, scatter, histograms)
• Apply basic feature transformations: scaling (Standard/MinMax) and encoding (one hot, label)
• Project  DataViz Explorer: Clean and visualize a dataset

Class 1: Feature Engineering

• Learn why transforming raw data is important
• Apply techniques: log transform, binning, polynomial features (with simple math)
• Encode categorical data (one hot, label, target encoding)
• Project Feature Mastery: Implement and compare feature transformations

Class 2: Building an ML Pipeline

• Overview of supervised vs. unsupervised learning
• Steps in the ML workflow: preprocessing, training, validation, testing
• Data splitting methods and cross validation rationale
• Performance Metrics:
               Classification: Accuracy, Precision, Recall, F1, ROC AUC, confusion matrix
               Regression: MSE, RMSE, MAE, R Square, adjusted R Square
• Project  ML Basics Pipeline: Build a simple pipeline on a toy dataset and evaluate

Class 1: Linear Regression

• Derive the least squares solution and MSE cost function
• Explain gradient descent: derivatives, update rules, and learning rate
• Project  Linear Predictor: Code linear regression from scratch and compare with scikit learn; evaluate with MSE, RMSE, R Square
• Reference: Learning Representations by Back-Propagating Errors (Rumelhart et al., 1986)

Class 2: Logistic Regression

• Understand the sigmoid function and binary cross entropy loss with math details
• Project Binary Classifier: Implement logistic regression (from scratch and via scikit learn); evaluate with confusion matrices, accuracy, precision, and recall