Phase 1
Phase 1 Topic 01 - Getting Started with Data Science Phase 1 Topic 02 - Bash and Git Phase 1 Topic 03 - Control Flow, Functions, and Statistics
Python
Coding Conventions
Phase 1 Topic 04 - Python Libraries: Numpy and Pandas Phase 1 Topic 05 - Data Cleaning in Pandas
Data Cleaning
Aggregation
Phase 1 Topic 06 - Data Visualization
Warmup
Data Visualization
Phase 1 Topic 07 - SQL and Relational Databases & Phase 1 Topic 08 - Other Database Structures
Module 1
Module 1 Section 01 - Getting Started with Data Science
Python
Coding Conventions
Module 1 Section 02 - Bash and Git
Bash Shell (Command Line Interface)
Git & GitHub
Activities
Extras for Using Git
Module 1 Section 03 - Control Flow, Functions, and Statistics
Control Flow
Functions
Statistics
Module 1 Section 04 - Python Libraries: NumPy and Pandas Module 1 Section 05 - Data Cleaning in Pandas
Pandas & Data
Data Exploration & Cleaning
Module 1 Section 06 - Data Visualization
Data Visualization Intro
Good & Bad Visualizations
Module 1 Section 07 - SQL and Relational Databases
Introduction to SQL
More SQL
Module 1 Section 08: Other Database structures Module 1 Section 09: JSON and APIs Module 1 Section 10: HTML, CSS, and Web Scraping Module 1 Project: Movie Analysis
Module 2
Module 2 Section 11 - Combinatorics and Probability
Conditional Probability
Combinatorics
Module 2 Section 12 - Statistical Distributions
Statistical Distributions
Module 2 Section 13 - Central Limit Theorem and Confidence Intervals
Central Limit Theorem
Confidence Intervals
Module 2 Section 14 - Hypothesis Testing
Experiment Design
Considerations
Statistical Tests
t-Tests
Module 2 Section 15 - Statistical Power & ANOVA
Parts of Hypothesis Tests
Welch's t-test & ANOVA
Module 2 Section 16 - A/B Testing Module 2 Section 17 - Bayesian Statistics Module 2 Section 18 - Introduction to Linear Regression Module 2 Section 19 - Multiple Linear Regression
Multiple Linear Regression
Module 2 Section 20 - Extensions to Linear Regression
Polynomial & Interacting Terms
Module 3
Module 3 Section 17 - Combinatorics Module 3 Section 18 - Statistical Distributions Module 3 Section 19 - Central Limit Theorem
Central Limit Theorem
Sampling Statistics
Confidence Intervals
Module 3 Section 20 - Hypothesis Testing
Intro to Experimental Design
P-Values & Null Hypothesis
Effect Sizes
T-Tests
Type 1 & Type 2 Errors
Module 3 Section 21 - Statistical Power & ANOVA
Statistical Power
Welch's T-Test
Multiple Comparisons & Goodhart's Law
ANOVA
Module 3 Section 22 - AB Testing Module 3 Section 23 - Bayesian Statistics
Bayes Theorem
Naive Bayes
Module 3 Section 24 - Resampling and Monte Carlo Simulation
Data Generation
Resampling
Monte Carlo
Module 4
Module 4 Section 25 - A Complete Data Science Project Using Multiple Regression Module 4 Section 26 - Linear Algebra
Linear Algebra Intro
Math with Tensors
Solving With Linear Algebra
Module 4 Section 27 - Calculus, Cost Function, and Gradient Descent Derivatives
- derivatives.ipynb
Gradient Descent
Gradient Descent Walkthrough
Module 4 Section 28 - Extensions to Linear Models
Improving Linear Regression (Interactions & Polynomial)
Regularization
Bias & Variance
Module 4 Section 29 - Introduction to Logistic Regression
Logistic Regression Intro
Logistic Regression
Evaluation Metrics (Confusion Matrices)
Evaluation Curves (ROC & AUC)
Module 4 Section 30 - In-depth Logistic Regression Module 4 Section 31 - Working with Time Series Data
Time Series Intro
Time Series Visualization
Time Series Trends
Module 4 Section 32 - Time Series Modeling
Time Series Models Intro
ARMA Model
Module 5
Module 5 Section 33 - K Nearest Neighbors
Distance Metrics
K Nearest Neighbors
Module 5 Section 34 - Decision Trees Module 5 Section 35 - Ensemble Methods
Ensemble Methods (Bagging, Random Forest, Adaboost, Gradient Boosting)
Module 5 Section 36 - Support Vector Machines
Support Vector Machine Intro
Kernel Trick
Module 5 Section 37 - Principal Component Analysis
Dimensionality
Principal Component Analysis
Module 5 Section 38 - Clustering
K-means
Hierarchical Clustering
DBSCAN
Module 5 Section 39 - Building a Machine Learning Pipeline Module 5 Section 40 - Big Data in PySpark
Big Data Introduction
Distributed Computing
MapReduce
Module 5 Section 41 - Recommendation Systems
Recommendation Systems
Neighbor Memory Based Collab Filtering
Matrix Factorization
Module 6
Module 6 Section 42 - Graph Theory Module 6 Section 43 - Foundations of Natural Language Processing Module 6 Section 44 - Introduction to Deep Learning Module 6 Section 45 - Multi-Layer Perceptrons Module 6 Section 46 - Tuning Neural Networks Moduel Section 49 - Deep NLP - Word Embeddings