Course Resources
Your comprehensive guide to learning materials and references
Week 1: Foundations of Data Science
Getting Started with Data
This week introduces fundamental concepts in data science, including data types, basic statistics, and essential Python tools for data manipulation and analysis.
📚 Core Materials
Required Reading
Essential foundations covering data types, variables, and descriptive statistics. Provides theoretical foundation for understanding how data is structured and analyzed.
Python for Statistics
Perfect introduction to Python for statistics students. Covers probability, descriptive statistics, and statistical inference using Python with real datasets.
🎓 UCSB Access: Library Database → Search “O’Reilly” → Login with NetID → Search “Think Stats”
Supplementary Reading
Deep dive into pandas operations including describe(), groupby(), and essential aggregation functions for real-world datasets.
🎓 UCSB Access: Library Database → Search “O’Reilly” → Login with NetID → Search “Python for Data Analysis”
Library Access
Access thousands of technology and programming books including Python, statistics, and data science titles. Essential for supplementary reading and advanced topics.
💻 Interactive Tools & Practice
Hands-on Workshop
Comprehensive hands-on workshop covering Python data types, pandas DataFrame structures, and input/output operations with downloadable datasets.
API Documentation
Official documentation for descriptive statistics in pandas. Essential reference for understanding central tendency, dispersion, and shape analysis.
Statistical Functions
Complete reference for NumPy’s statistical toolkit including mean(), median(), std(), percentile(), and advanced statistical measures.
Advanced Reference
Comprehensive statistical analysis toolkit covering probability distributions, hypothesis testing, and advanced descriptive statistics for research-grade analysis.
🎯 Learning Objectives & Study Plan
Mastery Checklist
Week 1 Learning Goals
After completing this week, you should be able to:
Study Schedule
Recommended Learning Path
Days 1-2: Core Reading (OpenIntro Chapters 1-2)
Day 3: Python for Statistics (Think Stats)
Day 4: Supplementary Reading (Python for Data Analysis)
Day 5: Interactive tutorials and hands-on practice
Day 6: Get familiar with python documentation
Day 7: Review and concept integration
🔍 Additional Resources
Extended Learning
Supplementary Materials by Topic
Python Basics: Python.org Tutorial • Codecademy Python • Python for Everybody
Data Science Foundations: Kaggle Learn • DataCamp Intro to Python • Coursera Python for Data Science
Statistical Computing: Think Python • Automate the Boring Stuff • Real Python Tutorials
Week 2: Introduction to Probability
Understanding Uncertainty Through Statistics
This week introduces fundamental concepts in probability theory, including sample spaces, events, conditional probability, independence, and Bayes’ theorem. You’ll learn to quantify uncertainty and make informed decisions with incomplete information.
📚 Core Materials
Required Reading
Essential foundations covering probability definitions, sample spaces, events, conditional probability, and independence.
Supplementary Reading
Interactive guide to set theory operations with Venn diagrams and visual explanations of probability concepts.
Video Lectures
High-quality video lectures covering probability axioms, sample spaces, and basic probability rules with problem sets.
Quick Reference
Comprehensive reference sheet covering all major probability formulas including conditional probability and independence.
💻 Interactive Tools & Practice
Visualizations
Interactive visual introduction with animations for conditional probability, Bayes’ theorem, and independence.
Simulations
Interactive probability simulation tool with tree diagrams, conditional probability, and Bayes’ theorem calculators.
Practice Problems
Comprehensive practice problems covering basic probability, conditional probability, and independence with instant feedback.
Python Code
Gallery of statistical visualizations including probability distributions, Venn diagrams, and tree diagrams.
🎯 Learning Objectives & Study Plan
Mastery Checklist
Week 2 Learning Goals
After completing this week, you should be able to:
Study Schedule
Recommended Learning Path
Days 1-2: Core Reading (OpenIntro Chapter 3)
Day 3: Interactive tutorials and visualizations
Day 4: Video lectures and supplementary readings
Day 5: Practice problems and exercises
Day 6: Review and concept integration
Day 7: Assessment preparation
🔍 Additional Resources
Extended Learning
Supplementary Materials by Learning Style
Visual Learners: Khan Academy Videos • Treena Notes • Math is Fun Stats
Practical Applications: Medical Diagnosis Examples • Real-world Problems
Advanced Study: MIT 6.041 Full Course • Probability Fallacies Guide
Week 3: Conditional Probability, Counting & Discrete Random Variables
Advanced Probability & Discrete Distributions
This week deepens your understanding of conditional probability and Bayes’ theorem, introduces counting principles (permutations and combinations), and explores discrete random variables including their probability mass functions, expected values, and common distributions.
📚 Core Materials
Required Reading
Essential coverage of conditional probability, Bayes’ theorem, counting principles, and discrete random variables. Includes probability mass functions and expected values.
Bayes’ Theorem
Interactive visualization of Bayes’ theorem with medical testing examples, false positives/negatives, and real-world applications. Essential for understanding conditional probability.
Combinatorics
Comprehensive coverage of counting principles, permutations, combinations, and their applications to probability with step-by-step examples and practice problems.
Discrete Distributions
Interactive exploration of discrete random variables, probability mass functions, and common distributions (Bernoulli, Binomial, Geometric, Poisson) with parameter adjustments.
💻 Interactive Tools & Practice
Bayes Calculator
Step-by-step Bayes’ theorem calculator with medical testing examples, tree diagrams, and visual representations of prior and posterior probabilities.
Combinatorics Tool
Online calculator for permutations, combinations, and factorial calculations with explanations and step-by-step solutions for complex counting problems.
Python Documentation
Complete reference for discrete probability distributions in Python including Bernoulli, Binomial, Geometric, and Poisson with PMF, CDF, and random generation functions.
Interactive Simulations
Interactive probability distribution notes and overview for discrete random variables.
🎯 Learning Objectives & Study Plan
Mastery Checklist
Week 3 Learning Goals
After completing this week, you should be able to:
Study Schedule
Recommended Learning Path
Days 1-2: Conditional probability and Bayes’ theorem review
Day 3: Counting principles: permutations and combinations
Day 4: Introduction to discrete random variables and PMFs
Day 5: Expected values, variance, and common distributions
Day 6: Python implementation and interactive practice
Day 7: Real-world applications and problem integration
🔍 Additional Resources
Extended Learning
Supplementary Materials by Topic
Bayes’ Theorem Applications: Medical Diagnosis Examples • Spam Filtering Kaggle Python Example • Spam Filtering • Legal Evidence
Combinatorics: Art of Problem Solving • Brilliant Combinatorics • Pascal’s Triangle
Discrete Distributions: Wolfram MathWorld • NIST Engineering Statistics • Real-world Examples
Python Programming: NumPy Random Documentation • Matplotlib Statistical Plots • Jupyter Notebooks Examples
Advanced Topics: Information Theory • Markov Chains • Maximum Likelihood Estimation
Week 4: Statistical Methods & Testing
Hypothesis Testing Fundamentals
Week 4 will cover hypothesis testing, and two sample t-Tests. Materials will be posted by week 4.