Phase 1 Topic 01 - Getting Started with Data Science

• Data Science
  • DataScienceIntro.ipynb

Recordings

Phase 1 Topic 02 - Bash and Git

• Bash
  • command_line_basics.ipynb
• Git & GitHub
  • git_intro.ipynb
  • github.ipynb
  • Git Tools

Recordings

Phase 1 Topic 03 - Control Flow, Functions, and Statistics

• Python
  • core_python.ipynb
• Coding Conventions
  • coding_best_practices.ipynb

Recordings

Phase 1 Topic 04 - Python Libraries: Numpy and Pandas

• NumPy
  • intro_to_numpy.ipynb
  • numpy_intro_activity.ipynb
• Pandas
  • from_numpy_to_pandas.ipynb

Recordings

Phase 1 Topic 05 - Data Cleaning in Pandas

• Data Cleaning
  • manipulating_data.ipynb
  • exploring_data.ipynb
  • data_cleaning_with_pandas_overview.ipynb
• Aggregation
  • aggregation.ipynb

Recordings

Phase 1 Topic 06 - Data Visualization

• Warmup
  • visualization_warmup.ipynb
• Data Visualization
  • motivation.ipynb
  • how_to_use_visualizations.ipynb
  • good_visualizations.ipynb
  • down_with_pie_chart.ipynb

Recordings

Phase 1 Topic 07 - SQL and Relational Databases & Phase 1 Topic 08 - Other Database Structures

• SQL
  • intro_to_sql.ipynb
  • using_sql.ipynb
  • sql_lesson.ipynb
  • sql_exercises.ipynb
  • joins.ipynb
  • advanced_topics.ipynb
• SQLite
  • sqlalchemy.ipynb
  • sqlite.ipynb
• NoSQL
  • nosql_intro.ipynb
  • mongodb.ipynb

Recordings

Module 1 Section 01 - Getting Started with Data Science

• Python
  • core_python.ipynb
• Coding Conventions
  • coding_best_practices.ipynb

Recordings

Module 1 Section 02 - Bash and Git

• Bash Shell (Command Line Interface)
  • command_line_basics.ipynb
• Git & GitHub
  • git_intro.ipynb
  • github.ipynb
  • git_collaboration.ipynb
  • git_advanced.ipynb
• Activities
  • git_collaboration_activity.ipynb
• Extras for Using Git
  • Git/Tools/

Recordings

Module 1 Section 03 - Control Flow, Functions, and Statistics

• Control Flow
  • core_python.ipynb
• Functions
  • functions.ipynb
• Statistics
  • summary_statistics.ipynb
  • Correlation & Correlation linear_regressions_and_simple_relationships.ipynb

Recordings

Module 1 Section 04 - Python Libraries: NumPy and Pandas

• NumPy
  • intro_to_numpy.ipynb
  • (OPTIONAL EXTRA) math_with_tensors.ipynb
  • Activity: numpy_intro_activity.ipynb
• Pandas
  • from_numpy_to_pandas.ipynb

Recordings

Module 1 Section 05 - Data Cleaning in Pandas

• Pandas & Data
  • from_numpy_to_pandas.ipynb
  • manipulating_data.ipynb
  • aggregation.ipynb
• Data Exploration & Cleaning
  • data_cleaning_with_pandas_overview.ipynb
  • exploring_data.ipynb

Recordings

Module 1 Section 06 - Data Visualization

• Data Visualization Intro
  • motivation.ipynb
  • how_to_use_visualizations.ipynb
• Good & Bad Visualizations
  • good_visualizations.ipynb
  • down_with_pie_chart.ipynb

Recordings

Module 1 Section 07 - SQL and Relational Databases

• Introduction to SQL
  • sql_lesson.ipynb
  • intro_to_sql.ipynb
  • sql_exercises.ipynb
• More SQL
  • using_sql.ipynb
  • joins.ipynb
  • advanced_topics.ipynb

Recordings

Module 1 Section 08: Other Database structures

Recordings

Module 1 Section 09: JSON and APIs

• JSON
  • json_and_xml_intro.ipynb
• APIs
  • apis.ipynb
  • lifx_example.ipynb

Recordings

Module 1 Section 10: HTML, CSS, and Web Scraping

• HTML & CSS
  • html_css_intro.ipynb
• Web Scraping
  • web_scraping.ipynb
  • web_scraping_beautiful_soup_activity_00.ipynb {IN PROGRESS}

Recordings

Module 1 Project: Movie Analysis

• Project Details
  • mod1_project_notes-pt_012120.ipynb
• Advice
  • general_advice.ipynb

Module 2 Section 11 - Combinatorics and Probability

• Conditional Probability
  • probability_and_notation.ipynb
  • conditional_probability.ipynb
• Combinatorics
  • combinatorics.ipynb

Recordings

Module 2 Section 12 - Statistical Distributions

• Statistical Distributions
  • statistical_distributions_intro.ipynb
  • statistical_distributions.ipynb
  • more_statistical_distributions.ipynb

Recordings

Module 2 Section 13 - Central Limit Theorem and Confidence Intervals

• Central Limit Theorem
  • sampling.ipynb
  • central_limit_theorem.ipynb
• Confidence Intervals
  • confidence_intervals.ipynb

Recordings

Module 2 Section 14 - Hypothesis Testing

• Experiment Design
  • experiment_design_intro.ipynb
  • hypothesis_testing_intro.ipynb
• Considerations
  • warnings.ipynb
  • multiple_comparisons.ipynb
• Statistical Tests
  • statistical_tests.ipynb
  • types_of_errors.ipynb
• t-Tests
  • t_distributions.ipynb
  • t_tests.ipynb

Recordings

Module 2 Section 15 - Statistical Power & ANOVA

• Parts of Hypothesis Tests
  • types_of_errors.ipynb
  • statistical_power.ipynb
  • effect_size.ipynb
• Welch's t-test & ANOVA
  • welchs_t_test.ipynb
  • multiple_comparisons.ipynb
  • anova.ipynb

Recordings

Module 2 Section 16 - A/B Testing

• A/B Testing
  • ab_testing.ipynb
  • ab_test_walkthrough.ipynb

Recordings

Module 2 Section 17 - Bayesian Statistics

• Bayes' Theorem
  • bayes_theorem.ipynb

Recordings

Module 2 Section 18 - Introduction to Linear Regression

• Simple Linear Regression
  • linear_regressions_and_simple_relationships.ipynb

Recordings

Module 2 Section 19 - Multiple Linear Regression

• Multiple Linear Regression
  • multiple_linear_regression.ipynb
  • multicollinearity.ipynb
  • model_validation.ipynb
  • linear_regression_example.ipynb

Recordings

Module 2 Section 20 - Extensions to Linear Regression

• Polynomial & Interacting Terms
  • improving_linear_regression.ipynb

Recordings

Module 3 Section 17 - Combinatorics

• probability_and_notation.ipynb
• conditional_probability.ipynb
• Permutations & Combinations
  • combinatorics.ipynb

Module 3 Section 18 - Statistical Distributions

• statistical_distributions_intro.ipynb
• statistical_distributions.ipynb

Module 3 Section 19 - Central Limit Theorem

• Central Limit Theorem
  • sampling-and-central-limit-theorem.ipynb
• Sampling Statistics
  • sampling-and-central-limit-theorem.ipynb
• Confidence Intervals
  • confidence-intervals.ipynb
  • t_distributions.ipynb

Module 3 Section 20 - Hypothesis Testing

• Intro to Experimental Design
  • experiment_design_intro.ipynb
• P-Values & Null Hypothesis
  • statistical_tests.ipynb
• Effect Sizes
  • effect_size.ipynb
• T-Tests
  • t_distributions.ipynb
  • t_tests.ipynb
• Type 1 & Type 2 Errors
  • types_of_errors.ipynb

Module 3 Section 21 - Statistical Power & ANOVA

• Statistical Power
  • statistical_power.ipynb
• Welch's T-Test
  • welchs_t_test.ipynb
• Multiple Comparisons & Goodhart's Law
  • warnings.ipynb
  • extras.ipynb
• ANOVA
  • anova.ipynb

Module 3 Section 22 - AB Testing

• A/B Testing
  • ab_testings.ipynb

Module 3 Section 23 - Bayesian Statistics

• Bayes Theorem
  • bayes_theorem.ipynb
• Naive Bayes
  • naive_bayes_classification.ipynb

Module 3 Section 24 - Resampling and Monte Carlo Simulation

• Data Generation
  • data_generation.ipynb
• Resampling
  • resampling.ipynb
• Monte Carlo
  • monte_carlo.ipynb
  • ultimate_hopscotch_simulation.ipynb

Module 4 Section 25 - A Complete Data Science Project Using Multiple Regression

Module 4 Section 26 - Linear Algebra

• Linear Algebra Intro
  • intro_to_linear_algebra
• Math with Tensors
  • math_with_tensors.ipynb
• Solving With Linear Algebra
  • solving_with_linear_algebra.ipynb

Module 4 Section 27 - Calculus, Cost Function, and Gradient Descent

Derivatives - derivatives.ipynb

• Gradient Descent
  • gradient_descent.ipynb
• Gradient Descent Walkthrough
  • walkthrough_gradient_descent.ipynb

Module 4 Section 28 - Extensions to Linear Models

• Improving Linear Regression (Interactions & Polynomial)
  • improving_linear_regression.ipynb
• Regularization
  • regularization.ipynb
• Bias & Variance
  • bias_and_variance.ipynb

Module 4 Section 29 - Introduction to Logistic Regression

• Logistic Regression Intro
  • logistic_regression_intro.ipynb
• Logistic Regression
  • logistic_regression.ipynb
• Evaluation Metrics (Confusion Matrices)
  • evaluation_metrics.ipynb
• Evaluation Curves (ROC & AUC)
  • evaluation_curves.ipynb

Module 4 Section 30 - In-depth Logistic Regression

Module 4 Section 31 - Working with Time Series Data

• Time Series Intro
  • time_series_intro.ipynb
• Time Series Visualization
  • time_series_visualization.ipynb
• Time Series Trends
  • time_series_trends.ipynb

Module 4 Section 32 - Time Series Modeling

• Time Series Models Intro
  • time_series_models_basic.ipynb
• ARMA Model
  • time_series_model_arma.ipynb

Module 5 Section 33 - K Nearest Neighbors

• Distance Metrics
  • distance_metrics.ipynb
• K Nearest Neighbors
  • k_nearest_neighbors.ipynb

Module 5 Section 34 - Decision Trees

• Decision Trees
  • decision_trees_intro.ipynb
  • information_to_make_decisions.ipynb
  • decision_tree_hyperparameters.ipynb
  • decision_tree_code_example.ipynb

Module 5 Section 35 - Ensemble Methods

• Ensemble Methods (Bagging, Random Forest, Adaboost, Gradient Boosting)
  • ensemble_methods.ipynb
  • bagging.ipynb
  • boosting.ipynb

Recordings

Module 5 Section 36 - Support Vector Machines

• Support Vector Machine Intro
  • support_vector_machine_intro.ipynb
• Kernel Trick
  • kernel_trick.ipynb

Module 5 Section 37 - Principal Component Analysis

• Dimensionality
  • dimensionality.ipynb
• Principal Component Analysis
  • pca.ipynb
  • pca_example.ipynb

Module 5 Section 38 - Clustering

• K-means
  • k_means.ipynb
  • k_means_issues.ipynb
• Hierarchical Clustering
  • hierarchical_clustering.ipynb
• DBSCAN
  • dbscan.ipynb

Module 5 Section 39 - Building a Machine Learning Pipeline

• Pipelines
  • pipeline_intro.ipynb
• Grid Search
  • grid_search.ipynb

Recordings

Module 5 Section 40 - Big Data in PySpark

• Big Data Introduction
  • big_data_intro.ipynb
• Distributed Computing
  • distributed_parallel_computing.ipynb
  • tools_of_distributed_systems.ipynb
• MapReduce
  • map_reduce.ipynb
  • map_reduce_code.ipynb

Recordings

Module 5 Section 41 - Recommendation Systems

• Recommendation Systems
  • recommendation_systems_intro.ipynb
• Neighbor Memory Based Collab Filtering
  • neighbor_memory_based_collab_filtering.ipynb
• Matrix Factorization
  • matrix_factorization.ipynb

Recordings

Module 6 Section 42 - Graph Theory

• Graph Theory
  • graph_theory_basics.ipynb
  • paths.ipynb

Module 6 Section 43 - Foundations of Natural Language Processing

• NLP Introduction
  • intro_to_nlp.ipynb
  • text_processing.ipynb
  • feature_extraction.ipynb

Module 6 Section 44 - Introduction to Deep Learning

• Neural Networks
  • neural_networks.ipynb
  • activation_functions.ipynb
  • keras_implementation.ipynb

Module 6 Section 45 - Multi-Layer Perceptrons

• Neural Networks & Parts
  • neural_networks.ipynb
  • activation_functions.ipynb
  • keras_implementation.ipynb

Module 6 Section 46 - Tuning Neural Networks

• Overfitting
  • avoiding_overfitting.ipynb
• Optimization
  • optimizations.ipynb

Moduel Section 49 - Deep NLP - Word Embeddings

• Word Embeddings
  • embeddings.ipynb