Data Science with Python
A comprehensive BSc course covering Python fundamentals, data manipulation, statistics, machine learning, deep learning, and deployment. Finance-focused examples throughout.
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48 lessons
904 charts
1517 slides
960 quiz questions
Curriculum
Module 1: Python Fundamentals
6 lessons
L1Python Setup
L2Data Structures
L3Control Flow
L4Functions
L5DataFrames Introduction
L6Selection Filtering
Module 2: Data Manipulation
6 lessons
L7Missing Data
L8Basic Operations
L9GroupBy Operations
L10Merging Joining
L11NumPy Basics
L12Time Series
Module 3: Statistics & Visualization
8 lessons
L13Descriptive Statistics
L14Distributions
L15Hypothesis Testing
L16Correlation
L17Matplotlib Basics
L18Seaborn Plots
L19Multi Panel Figures
L20Data Storytelling
Module 4: ML: Regression
4 lessons
Module 5: ML: Classification
4 lessons
Module 6: ML: Unsupervised
4 lessons
Module 7: Deep Learning
4 lessons
L33Perceptron
L34MLP Activations
L35Backpropagation
L36Overfitting Prevention
Module 8: NLP & Text
4 lessons
L37Text Preprocessing
L38BOW TFIDF
L39Word Embeddings
L40Sentiment Analysis
Module 9: Deployment
4 lessons
L41Model Serialization
L42FastAPI
L43Streamlit Dashboards
L44Cloud Deployment
Module 10: Capstone & Ethics
4 lessons
L45Project Work 1
L46Project Work 2
L47ML Ethics
L48Final Presentations
Resources
Quick Downloads
Module Overviews
Module 1: Python Fundamentals
Module 2: Data Manipulation
Module 3: Statistics & Visualization
Module 4: ML: Regression
Module 5: ML: Classification
Module 6: Unsupervised Learning
Module 7: Deep Learning
Module 8: NLP & Text
Module 9: Deployment
Module 10: Capstone & Ethics
Colab Notebooks
Module Summaries
Python Fundamentals (L01-L06)
Data Manipulation (L07-L12)
Statistics & Visualization (L13-L20)
ML: Regression (L21-L24)
ML: Classification (L25-L28)
ML: Unsupervised (L29-L32)
Deep Learning (L33-L36)
NLP & Text (L37-L40)
Deployment (L41-L44)
Capstone & Ethics (L45-L48)
Quizzes
Test your knowledge with practice quizzes for each lesson.
EXAM
Exam Practice - Conceptual Questions
Start Practice
Questions with detailed solutions from all 48 lessons
Reading Companions
Pre-class preparation and post-class review materials with vocabulary, examples, and self-tests.
Charts Gallery
Visualizations illustrating key concepts across all modules.
Explore visualizations across all 10 modules covering Python, Statistics, ML, Deep Learning, NLP, and Deployment.
Teaching Materials
Handouts, worksheets, and exercises for deeper study and class activities.
Regression Handout
Classification Handout
Supervised Learning Handout
Unsupervised Learning Handout
Deep Learning Handout
Day 7 Deployment Handout
6-Day Intensive: Day 5 (Unsupervised + Reinforcement)
6-Day Intensive: Day 6 (Embeddings + Frontier)
Additional Lectures
Supplementary lecture materials covering advanced topics beyond the core curriculum.
Foundations and AI in Finance
Introduction to AI
AI in Finance: From Prediction to Production
Supervised Learning: Concepts and Overviews
PILOTSL Mini-Lecture: The Generalization Paradox
Supervised Learning: The Big Idea
Supervised Learning: Complete Overview
Supervised Learning: From Linear Regression to Gradient Boosting
SL: One Dataset, Eight Methods
Supervised Learning: Methods and Algorithms
Comprehensive Regression
Comprehensive Classification
Support Vector Machines
SVM: A Simple Lecture with All Formulas
Naive Bayes: A Simple Lecture with All Formulas
XGBoost: A Simple, Complete Lecture
Unsupervised Learning
PILOTUL Mini-Lecture: The Validation Paradox
Unsupervised Learning: The Big Idea
Unsupervised Learning: Complete Overview
Unsupervised Learning: From K-Means to Gaussian Mixtures
Deep Learning
Deep Learning: The Big Idea
Deep Learning: Complete Overview
Day 6: Neural Networks (Full Lecture)
Frontier and Deployment
Day 7: From Notebook to Production (MLOps)
NEWML & Innovation 6-Day Intensive: The Frontier (Transformers, LLMs, RAG, Agents)
NEWDeep Reinforcement Learning in Finance: Agents, Q-Learning, Policy Gradients, and Limits
LLM Overview
Day 6: Review
Advanced Topics
Standalone deep-dive decks on advanced machine-learning topics, beyond the core curriculum.