https://github.com/mrgeislinger/flatiron-school-data-science-curriculum-resources
Lesson material on data science and machine learning topics/concepts
https://github.com/mrgeislinger/flatiron-school-data-science-curriculum-resources
data-science data-visualization deep-learning gradient-descent learning linear-regression machine-learning neural numpy pandas sql statistical-distributions statistics
Last synced: 2 months ago
JSON representation
Lesson material on data science and machine learning topics/concepts
- Host: GitHub
- URL: https://github.com/mrgeislinger/flatiron-school-data-science-curriculum-resources
- Owner: MrGeislinger
- Created: 2019-04-01T01:28:16.000Z (about 6 years ago)
- Default Branch: main
- Last Pushed: 2021-05-27T17:04:49.000Z (almost 4 years ago)
- Last Synced: 2023-11-07T18:24:30.821Z (over 1 year ago)
- Topics: data-science, data-visualization, deep-learning, gradient-descent, learning, linear-regression, machine-learning, neural, numpy, pandas, sql, statistical-distributions, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 44.5 MB
- Stars: 128
- Watchers: 9
- Forks: 138
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Cohort FT-011121
Phase 1
## Phase 1 Topic 01 - Getting Started with Data Science
* Data Science
- [DataScienceIntro.ipynb](DataScienceBasics/DataScienceIntro.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Introduction to Data Science | 2021-01-11 | [youtu.be/R7pM6SluD60](https://youtu.be/R7pM6SluD60) |
## Phase 1 Topic 02 - Bash and Git
* Bash
- [command_line_basics.ipynb](CommandLine/Unix/command_line_basics.ipynb)
* Git \& GitHub
- [git_intro.ipynb](Git/git_intro.ipynb)
- [github.ipynb](Git/github.ipynb)
- [Git Tools](Git/Tools)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Command Line Basics | 2021-01-11 | [youtu.be/fjZFp2oTveg](https://youtu.be/fjZFp2oTveg) |
|Intro to Using Git with GitHub | 2021-01-12 | [youtu.be/GGh9X5Iby10](https://youtu.be/GGh9X5Iby10) |
## Phase 1 Topic 03 - Control Flow, Functions, and Statistics
* Python
- [core_python.ipynb](CodingBasics/Python/core_python.ipynb)
* Coding Conventions
- [coding_best_practices.ipynb](CodingBasics/CodingConventions/coding_best_practices.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Python Conventions \& Best Practices | 2021-01-11 | [youtu.be/3YxS_5dW3aY](https://youtu.be/3YxS_5dW3aY) |
|Python Basics | 2021-01-11 | [youtu.be/0ffOdnVmjHg](https://youtu.be/0ffOdnVmjHg) |
|Some More Python Basics: Control Flow | 2021-01-12 | [youtu.be/iLrqpbZvWb0](https://youtu.be/iLrqpbZvWb0) |
|More Python: Functions | 2021-01-13 | [youtu.be/FrklluKZWHw](https://youtu.be/FrklluKZWHw) |
## Phase 1 Topic 04 - Python Libraries: Numpy and Pandas
* NumPy
- [intro_to_numpy.ipynb](CodingBasics/NumPy/intro_to_numpy.ipynb)
- [numpy_intro_activity.ipynb](CodingBasics/NumPy/numpy_intro_activity.ipynb)
* Pandas
- [from_numpy_to_pandas.ipynb](DataScienceBasics/Pandas/from_numpy_to_pandas.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Intro to NumPy | 2021-01-13 | [youtu.be/-z-n8Hrtvl8](https://youtu.be/-z-n8Hrtvl8) |
|Intro to Pandas from NumPy | 2021-01-15 | [youtu.be/S7p2w4cXc9o](https://youtu.be/S7p2w4cXc9o) |
## Phase 1 Topic 05 - Data Cleaning in Pandas
* Data Cleaning
- [manipulating_data.ipynb](DataScienceBasics/Pandas/manipulating_data.ipynb)
- [exploring_data.ipynb](DataScienceBasics/Pandas/exploring_data.ipynb)
- [data_cleaning_with_pandas_overview.ipynb](DataScienceBasics/Pandas/data_cleaning_with_pandas_overview.ipynb)
* Aggregation
- [aggregation.ipynb](DataScienceBasics/Pandas/aggregation.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|More Pandas: Exploring and Manipulating Data | 2021-01-19 | [youtu.be/m67HtpXYv3U](https://youtu.be/m67HtpXYv3U) |
## Phase 1 Topic 06 - Data Visualization
* Warmup
- [visualization_warmup.ipynb](DataScienceBasics/Visualization/visualization_warmup.ipynb)
* Data Visualization
- [motivation.ipynb](DataScienceBasics/Visualization/motivation.ipynb)
- [how_to_use_visualizations.ipynb](DataScienceBasics/Visualization/how_to_use_visualizations.ipynb)
- [good_visualizations.ipynb](DataScienceBasics/Visualization/good_visualizations.ipynb)
- [down_with_pie_chart.ipynb](DataScienceBasics/Visualization/down_with_pie_chart.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Concepts of Data Visualization | 2021-01-20 | [youtu.be/AxlWpplunVo](https://youtu.be/AxlWpplunVo) |
## Phase 1 Topic 07 - SQL and Relational Databases \& Phase 1 Topic 08 - Other Database Structures
* SQL
- [intro_to_sql.ipynb](DataEngineering/SQL/intro_to_sql.ipynb)
- [using_sql.ipynb](DataEngineering/SQL/using_sql.ipynb)
- [sql_lesson.ipynb](DataEngineering/SQL/sql_lesson.ipynb)
- [sql_exercises.ipynb](DataEngineering/SQL/sql_exercises.ipynb)
- [joins.ipynb](DataEngineering/SQL/joins.ipynb)
- [advanced_topics.ipynb](DataEngineering/SQL/advanced_topics.ipynb)
* SQLite
- [sqlalchemy.ipynb](DataEngineering/SQLite/sqlalchemy.ipynb)
- [sqlite.ipynb](DataEngineering/SQLite/sqlite.ipynb)
* NoSQL
- [nosql_intro.ipynb](DataEngineering/NoSQL/nosql_intro.ipynb)
- [mongodb.ipynb](DataEngineering/NoSQL/mongodb.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|SQL with Python \& Pandas | 2021-01-22 | [youtu.be/VFN89HOa9m0](https://youtu.be/VFN89HOa9m0) |
----------------------------
# Curriculum (v2.1)
Module 1
## Module 1 Section 01 - Getting Started with Data Science
* Python
- [core_python.ipynb](CodingBasics/Python/core_python.ipynb)
* Coding Conventions
- [coding_best_practices.ipynb](CodingBasics/CodingConventions/coding_best_practices.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|The Data Science Process | 2020-01-23 | [youtu.be/UZlPoaD4Bvw](https://youtu.be/UZlPoaD4Bvw) |
|Python Basics & Coding Practices | 2020-01-23 | [youtu.be/uw4in0E8vvE](https://youtu.be/uw4in0E8vvE) |
## Module 1 Section 02 - Bash and Git
* Bash Shell (Command Line Interface)
- [command_line_basics.ipynb](CommandLine/Unix/command_line_basics.ipynb)
* Git & GitHub
- [git_intro.ipynb](Git/git_intro.ipynb)
- [github.ipynb](Git/github.ipynb)
- [git_collaboration.ipynb](Git/git_collaboration.ipynb)
- [git_advanced.ipynb](Git/git_advanced.ipynb)
* Activities
- [git_collaboration_activity.ipynb](Git/Activity/git_collaboration_activity.ipynb)
* Extras for Using Git
- [Git/Tools/](Git/Tools/)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Forking a GitHub Repo | 2020-01-22 | [youtu.be/SOKH8Xni_BE](https://youtu.be/SOKH8Xni_BE) |
|Copy GitHub Repo Without Forking | 2020-01-22 | [youtu.be/q0_MMK8AS8E](https://youtu.be/q0_MMK8AS8E) |
|Command Line Basics | 2020-01-28 | [youtu.be/Nta5HpFKDRc](https://youtu.be/Nta5HpFKDRc) |
|The Git Basics | 2020-01-28 | [youtu.be/Rx85RNB4gn4](https://youtu.be/Rx85RNB4gn4) |
|GitHub Basics with Git | 2020-01-28 | [youtu.be/F-VQbMxgm1o](https://youtu.be/F-VQbMxgm1o) |
## Module 1 Section 03 - Control Flow, Functions, and Statistics
* Control Flow
- [core_python.ipynb](CodingBasics/Python/core_python.ipynb)
* Functions
- [functions.ipynb](CodingBasics/Python/functions.ipynb)
* Statistics
- [summary_statistics.ipynb](ProbabilityAndStats/StatisticsBasics/summary_statistics.ipynb)
- Correlation & Correlation [linear_regressions_and_simple_relationships.ipynb](MachineLearning/LinearRegression/linear_regressions_and_simple_relationships.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Python Basics: Lists, Dictionaries, and More | 2020-01-29 | [youtu.be/Mdi1dWzCIZE](https://youtu.be/Mdi1dWzCIZE) |
|Python Basics: Control Flow | 2020-01-29 | [youtu.be/q1ZMx9p6dJo](https://youtu.be/q1ZMx9p6dJo) |
|Python Basics: Functions | 2020-01-29 | [youtu.be/7pcILR2LtKo](https://youtu.be/7pcILR2LtKo) |
## Module 1 Section 04 - Python Libraries: NumPy and Pandas
* NumPy
- [intro_to_numpy.ipynb](CodingBasics/NumPy/intro_to_numpy.ipynb)
- (OPTIONAL EXTRA) [math_with_tensors.ipynb](Mathematics/LinearAlgebra/math_with_tensors.ipynb)
- Activity: [numpy_intro_activity.ipynb](CodingBasics/NumPy/numpy_intro_activity.ipynb)
* Pandas
- [from_numpy_to_pandas.ipynb](DataScienceBasics/Pandas/from_numpy_to_pandas.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|NumPy Intro | 2020-02-05 | [youtu.be/Ea5tmWo0e5k](https://youtu.be/Ea5tmWo0e5k) |
|NumPy Activity | 2020-02-05 | [youtu.be/ROiNq5WTjCc](https://youtu.be/ROiNq5WTjCc) |
|From NumPy to Pandas | 2020-02-05 | [youtu.be/Ng_TzUentmk](https://youtu.be/Ng_TzUentmk) |
## Module 1 Section 05 - Data Cleaning in Pandas
* Pandas & Data
- [from_numpy_to_pandas.ipynb](DataScienceBasics/Pandas/from_numpy_to_pandas.ipynb)
- [manipulating_data.ipynb](DataScienceBasics/Pandas/manipulating_data.ipynb)
- [aggregation.ipynb](DataScienceBasics/Pandas/aggregation.ipynb)
* Data Exploration & Cleaning
- [data_cleaning_with_pandas_overview.ipynb](DataScienceBasics/Pandas/data_cleaning_with_pandas_overview.ipynb)
- [exploring_data.ipynb](DataScienceBasics/Pandas/exploring_data.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Brief Extra: Pandas & Loading Data | 2020-02-05 | [youtu.be/-nr7bi7lVxQ](https://youtu.be/-nr7bi7lVxQ) |
|Data Exploration with Pandas | 2020-02-11 | [youtu.be/W_ey_4uIGQ0](https://youtu.be/W_ey_4uIGQ0) |
|Data Exploration & Cleaning with Python | 2020-02-11 | [youtu.be/KXNzYfWUoUM](https://youtu.be/KXNzYfWUoUM) |
## Module 1 Section 06 - Data Visualization
* Data Visualization Intro
- [motivation.ipynb](DataScienceBasics/Visualization/motivation.ipynb)
- [how_to_use_visualizations.ipynb](DataScienceBasics/Visualization/how_to_use_visualizations.ipynb)
* Good & Bad Visualizations
- [good_visualizations.ipynb](DataScienceBasics/Visualization/good_visualizations.ipynb)
- [down_with_pie_chart.ipynb](DataScienceBasics/Visualization/down_with_pie_chart.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Why Should I Visualize Data? | 2020-02-11 | [youtu.be/AjEdgBRbvUU](https://youtu.be/AjEdgBRbvUU) |
|Who Are Visualizations For? | 2020-02-11 | [youtu.be/8t452nMFApc](https://youtu.be/8t452nMFApc) |
|Visualizations: The Good, The Bad & The Ugly| 2020-02-12 | [youtu.be/yvwyvCt8qAI](https://youtu.be/yvwyvCt8qAI) |
|Data Exploration Activity | 2020-02-12 | [youtu.be/XPT6QgMbPos](https://youtu.be/XPT6QgMbPos) |
## Module 1 Section 07 - SQL and Relational Databases
* Introduction to SQL
- [sql_lesson.ipynb](DataEngineering/SQL/sql_lesson.ipynb)
- [intro_to_sql.ipynb](DataEngineering/SQL/intro_to_sql.ipynb)
- [sql_exercises.ipynb](DataEngineering/SQL/sql_exercises.ipynb)
* More SQL
- [using_sql.ipynb](DataEngineering/SQL/using_sql.ipynb)
- [joins.ipynb](DataEngineering/SQL/joins.ipynb)
- [advanced_topics.ipynb](DataEngineering/SQL/advanced_topics.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|SQL & Realtional Databases Intro | 2020-02-18 | [youtu.be/Ca-8RRZlLLo](https://youtu.be/Ca-8RRZlLLo) |
|Running SQL in Python | 2020-02-18 | [youtu.be/IjF3bNF-eHc](https://youtu.be/IjF3bNF-eHc) |
|More SQL & Joining Tables | 2020-02-18 | [youtu.be/1PXDL-S71Cc](https://youtu.be/1PXDL-S71Cc) |
|Creating and Updating SQL Databases | 2020-02-18 | [youtu.be/c8Gyv_LXH8o](https://youtu.be/c8Gyv_LXH8o) |
|SQL & Execution Order | 2020-02-19 | [youtu.be/NJEOpxZP9TI](https://youtu.be/NJEOpxZP9TI) |
|SQL Subqueries | 2020-02-19 | [youtu.be/mAEgY7BGlN8](https://youtu.be/mAEgY7BGlN8) |
## Module 1 Section 08: Other Database structures
### Recordings
## Module 1 Section 09: JSON and APIs
* JSON
- [json_and_xml_intro.ipynb](DataEngineering/JSONAndXML/json_and_xml_intro.ipynb)
* APIs
- [apis.ipynb](DataEngineering/APIs/apis.ipynb)
- [lifx_example.ipynb](DataEngineering/APIs/lifx_example.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|JSON Data Format for Python | 2020-02-19 | [youtu.be/EbCjd6OPdvg](https://youtu.be/EbCjd6OPdvg) |
|APIs with Python | 2020-02-19 | [youtu.be/NsfITpjTqAA](https://youtu.be/NsfITpjTqAA) |
|API Example with LIFX | 2020-02-19 | [youtu.be/-zsoxAzkSLU](https://youtu.be/-zsoxAzkSLU) |
## Module 1 Section 10: HTML, CSS, and Web Scraping
* HTML & CSS
- [html_css_intro.ipynb](DataEngineering/WebScraping/html_css_intro.ipynb)
* Web Scraping
- [web_scraping.ipynb](DataEngineering/WebScraping/web_scraping.ipynb)
- [web_scraping_beautiful_soup_activity_00.ipynb](Activities/web_scraping_beautiful_soup_activity_00.ipynb) {**IN PROGRESS**}
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|HTML and CSS Intro for Web Scraping | 2020-02-26 | [youtu.be/MadMEVGMTUE](https://youtu.be/MadMEVGMTUE) |
|Intro & Ethics to Web Scraping | 2020-02-26 | [youtu.be/ceH08GJlIOo](https://youtu.be/ceH08GJlIOo) |
|Web Scraping with Python & Beautiful Soup| 2020-02-26|[youtu.be/f6lj7xC0Y2g](https://youtu.be/f6lj7xC0Y2g) |
|Web Scraping Demo: Adventure Time | 2020-02-26 | [youtu.be/v_a1qUuXd1Y](https://youtu.be/v_a1qUuXd1Y) |
## Module 1 Project: Movie Analysis
* Project Details
- [mod1_project_notes-pt_012120.ipynb](Projects/MovieAnalysis/mod1_project_notes-pt_012120.ipynb)
* Advice
- [general_advice.ipynb](Projects/general_advice.ipynb)
Module 2
## Module 2 Section 11 - Combinatorics and Probability
* Conditional Probability
- [probability_and_notation.ipynb](ProbabilityAndStats/Probability/probability_and_notation.ipynb)
- [conditional_probability.ipynb](ProbabilityAndStats/Probability/conditional_probability.ipynb)
* Combinatorics
- [combinatorics.ipynb](ProbabilityAndStats/Probability/combinatorics.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
|Conditional Probability | 2020-03-17 | [youtu.be/JDgm4Wqsvuw](https://youtu.be/JDgm4Wqsvuw) |
|Combinatorics | 2020-03-17 | [youtu.be/hs5EFpUcTzw](https://youtu.be/hs5EFpUcTzw) |
## Module 2 Section 12 - Statistical Distributions
* Statistical Distributions
- [statistical_distributions_intro.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions_intro.ipynb)
- [statistical_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions.ipynb)
- [more_statistical_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/more_statistical_distributions.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| Frequency Distributions & More Statistics | 2020-03-19 | [youtu.be/bNUpLoDgLig](https://youtu.be/bNUpLoDgLig) |
| Review & Other Statistical Distributions | 2020-03-24 | [youtu.be/YRor7gBV9Kw](https://youtu.be/YRor7gBV9Kw) |
| Even More Statistical Distributions | 2020-03-24 | [youtu.be/dVSnNHKyeAM](https://youtu.be/dVSnNHKyeAM) |
## Module 2 Section 13 - Central Limit Theorem and Confidence Intervals
* Central Limit Theorem
- [sampling.ipynb](ProbabilityAndStats/StatisticalDistributions/sampling.ipynb)
- [central_limit_theorem.ipynb](ProbabilityAndStats/StatisticalDistributions/central_limit_theorem.ipynb)
* Confidence Intervals
- [confidence_intervals.ipynb](ProbabilityAndStats/StatisticalDistributions/confidence_intervals.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| Sampling | 2020-03-24 | [youtu.be/x5KVX3ccbuc](https://youtu.be/x5KVX3ccbuc) |
| Central Limit Theorem | 2020-03-24 | [youtu.be/c2NDqWrCBno](https://youtu.be/c2NDqWrCBno) |
| Where Do Confidence Intervals Come From? | 2020-03-26 | [youtu.be/jHLoLCCtumc](https://youtu.be/jHLoLCCtumc) |
## Module 2 Section 14 - Hypothesis Testing
* Experiment Design
- [experiment_design_intro.ipynb](ProbabilityAndStats/ExperimentalDesign/experiment_design_intro.ipynb)
- [hypothesis_testing_intro.ipynb](ProbabilityAndStats/ExperimentalDesign/hypothesis_testing_intro.ipynb)
* Considerations
- [warnings.ipynb](ProbabilityAndStats/ExperimentalDesign/warnings.ipynb)
- [multiple_comparisons.ipynb](ProbabilityAndStats/ExperimentalDesign/multiple_comparisons.ipynb)
* Statistical Tests
- [statistical_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_tests.ipynb)
- [types_of_errors.ipynb](ProbabilityAndStats/ExperimentalDesign/types_of_errors.ipynb)
* t-Tests
- [t_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/t_distributions.ipynb)
- [t_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/t_tests.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| What Makes a Good Experiment? | 2020-03-26 | [youtu.be/746no4_NvRM](https://youtu.be/746no4_NvRM) |
| Hypothesis Testing Intro | 2020-03-26 | [youtu.be/TE8C-PsZfrw](https://youtu.be/TE8C-PsZfrw) |
| Hypothesis Testing | 2020-03-31 | [youtu.be/JnO5wKYnNfQ](https://youtu.be/JnO5wKYnNfQ) |
| The t-Distribution & t-Test | 2020-03-31 | [youtu.be/8zey4ICieg0](https://youtu.be/8zey4ICieg0) |
| Type 1 vs Type 2 Errors | 2020-03-31 | [youtu.be/1IybE0mXWl4](https://youtu.be/1IybE0mXWl4) |
## Module 2 Section 15 - Statistical Power & ANOVA
* Parts of Hypothesis Tests
- [types_of_errors.ipynb](ProbabilityAndStats/ExperimentalDesign/types_of_errors.ipynb)
- [statistical_power.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_power.ipynb)
- [effect_size.ipynb](ProbabilityAndStats/ExperimentalDesign/effect_size.ipynb)
* Welch's t-test & ANOVA
- [welchs_t_test.ipynb](ProbabilityAndStats/ExperimentalDesign/welchs_t_test.ipynb)
- [multiple_comparisons.ipynb](ProbabilityAndStats/ExperimentalDesign/multiple_comparisons.ipynb)
- [anova.ipynb](ProbabilityAndStats/ExperimentalDesign/anova.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
|Effect Size & Statistical Power Relationship | 2020-03-31 | [youtu.be/0HtaoDgOF_A](https://youtu.be/0HtaoDgOF_A) |
|Welch's t-Test vs Student's t-Test | 2020-04-01| [youtu.be/QNftsEYSwFA](https://youtu.be/QNftsEYSwFA) |
|Multiple Comparisons Warning | 2020-04-07| [youtu.be/voHPvSkX3f4](https://youtu.be/voHPvSkX3f4) |
|Introduction to ANOVA | 2020-04-07| [youtu.be/y1UWYQHw5Jo](https://youtu.be/y1UWYQHw5Jo) |
|Coding ANOVA: SciPy Method | 2020-04-07| [youtu.be/QnE8sBrKoNU](https://youtu.be/QnE8sBrKoNU) |
|Coding ANOVA: Statsmodels OLS Method | 2020-04-07| [youtu.be/3cCM0lQFMM4](https://youtu.be/3cCM0lQFMM4) |
## Module 2 Section 16 - A/B Testing
* A/B Testing
- [ab_testing.ipynb](ProbabilityAndStats/ExperimentalDesign/ab_testing.ipynb)
- [ab_test_walkthrough.ipynb](ProbabilityAndStats/ExperimentalDesign/ab_test_walkthrough.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| A/B Testing | 2020-04-07 | [youtu.be/2DVXuR-2LeA](https://youtu.be/2DVXuR-2LeA) |
## Module 2 Section 17 - Bayesian Statistics
* Bayes' Theorem
- [bayes_theorem.ipynb](ProbabilityAndStats/BayesianClassification/bayes_theorem.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| Bayesian Thinking | 2020-04-21 | [youtu.be/odZOxI_3BNI](https://youtu.be/odZOxI_3BNI] |
| Bayes' Theorem Coding Example: Testing Positive | 2020-04-21 | [youtu.be/yN7BPP25Bvg](https://youtu.be/yN7BPP25Bvg] |
| Visual of Bayes' Theorem | 2020-04-21 | [youtu.be/ib1a7c8MrtQ](https://youtu.be/ib1a7c8MrtQ] |
| Bayes' Theorem Followup: Testing Positive Twice | 2020-04-21 | [youtu.be/VgGUngEkYok](https://youtu.be/VgGUngEkYok] |
## Module 2 Section 18 - Introduction to Linear Regression
* Simple Linear Regression
- [linear_regressions_and_simple_relationships.ipynb](MachineLearning/LinearRegression/linear_regressions_and_simple_relationships.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
|Intro to Linear Regression | 2020-04-09 | [youtu.be/PBv749p-9yY](https://youtu.be/PBv749p-9yY) |
## Module 2 Section 19 - Multiple Linear Regression
* Multiple Linear Regression
- [multiple_linear_regression.ipynb](MachineLearning/LinearRegression/multiple_linear_regression.ipynb)
- [multicollinearity.ipynb](MachineLearning/LinearRegression/multicollinearity.ipynb)
- [model_validation.ipynb](EvaluatingModels/model_validation.ipynb)
- [linear_regression_example.ipynb](MachineLearning/LinearRegression/linear_regression_example.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
|Multiple Linear Regression | 2020-04-15| [youtu.be/drbltsGcRNQ](https://youtu.be/drbltsGcRNQ)|
|Handling Categorical Variables | 2020-04-15| [youtu.be/57Cy58UnKv0](https://youtu.be/57Cy58UnKv0)|
|Dealing with Multicollinearity | 2020-04-16| [youtu.be/eGSG79vF6_E](https://youtu.be/eGSG79vF6_E)|
|Validating Models & k-Fold Cross-Validation | 2020-04-16| [youtu.be/nmIxCbv09G0](https://youtu.be/nmIxCbv09G0)|
## Module 2 Section 20 - Extensions to Linear Regression
* Polynomial & Interacting Terms
- [improving_linear_regression.ipynb](MachineLearning/LinearRegression/ExtendingLinearRegression/improving_linear_regression.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| Extending Linear Regression: Polynomial & Interacting Terms | 2020-04-22 | [youtu.be/QbkwZ9cCb8I](https://youtu.be/QbkwZ9cCb8I] |
----------------------------
# Curriculum (v2.0)
Module 3
## Module 3 Section 17 - Combinatorics
* [probability_and_notation.ipynb](ProbabilityAndStats/Probability/probability_and_notation.ipynb)
* [conditional_probability.ipynb](ProbabilityAndStats/Probability/conditional_probability.ipynb)
* Permutations & Combinations
- [combinatorics.ipynb](ProbabilityAndStats/Probability/combinatorics.ipynb)
## Module 3 Section 18 - Statistical Distributions
* [statistical_distributions_intro.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions_intro.ipynb)
* [statistical_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions.ipynb)
## Module 3 Section 19 - Central Limit Theorem
* Central Limit Theorem
- [sampling-and-central-limit-theorem.ipynb](ProbabilityAndStats/StatisticalDistributions/sampling-and-central-limit-theorem.ipynb)
* Sampling Statistics
- [sampling-and-central-limit-theorem.ipynb](ProbabilityAndStats/StatisticalDistributions/sampling-and-central-limit-theorem.ipynb)
* Confidence Intervals
- [confidence-intervals.ipynb](ProbabilityAndStats/StatisticalDistributions/confidence-intervals.ipynb)
- [t_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/t_distributions.ipynb)
## Module 3 Section 20 - Hypothesis Testing
* Intro to Experimental Design
- [experiment_design_intro.ipynb](ProbabilityAndStats/ExperimentalDesign/experiment_design_intro.ipynb)
* P-Values & Null Hypothesis
- [statistical_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_tests.ipynb)
* Effect Sizes
- [effect_size.ipynb](ProbabilityAndStats/ExperimentalDesign/effect_size.ipynb)
* T-Tests
- [t_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/t_distributions.ipynb)
- [t_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/t_tests.ipynb)
* Type 1 & Type 2 Errors
- [types_of_errors.ipynb](ProbabilityAndStats/ExperimentalDesign/types_of_errors.ipynb)
## Module 3 Section 21 - Statistical Power & ANOVA
* Statistical Power
- [statistical_power.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_power.ipynb)
* Welch's T-Test
- [welchs_t_test.ipynb](ProbabilityAndStats/ExperimentalDesign/welchs_t_test.ipynb)
* Multiple Comparisons & Goodhart's Law
- [warnings.ipynb](ProbabilityAndStats/ExperimentalDesign/warnings.ipynb)
- [extras.ipynb](ProbabilityAndStats/ExperimentalDesign/extras.ipynb)
* ANOVA
- [anova.ipynb](ProbabilityAndStats/ExperimentalDesign/anova.ipynb)
## Module 3 Section 22 - AB Testing
* A/B Testing
- [ab_testings.ipynb](ProbabilityAndStats/ExperimentalDesign/ab_testings.ipynb)
## Module 3 Section 23 - Bayesian Statistics
* Bayes Theorem
- [bayes_theorem.ipynb](ProbabilityAndStats/BayesianClassification/bayes_theorem.ipynb)
* Naive Bayes
- [naive_bayes_classification.ipynb](ProbabilityAndStats/BayesianClassification/naive_bayes_classification.ipynb)
## Module 3 Section 24 - Resampling and Monte Carlo Simulation
* Data Generation
- [data_generation.ipynb](ProbabilityAndStats/DataGeneration/data_generation.ipynb)
* Resampling
- [resampling.ipynb](ProbabilityAndStats/DataGeneration/resampling.ipynb)
* Monte Carlo
- [monte_carlo.ipynb](ProbabilityAndStats/DataGeneration/monte_carlo.ipynb)
- [ultimate_hopscotch_simulation.ipynb](ProbabilityAndStats/DataGeneration/ultimate_hopscotch_simulation.ipynb)
Module 4
## 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](Mathematics/LinearAlgebra/intro_to_linear_algebra.ipynb)
* Math with Tensors
- [math_with_tensors.ipynb](Mathematics/LinearAlgebra/math_with_tensors.ipynb)
* Solving With Linear Algebra
- [solving_with_linear_algebra.ipynb](Mathematics/LinearAlgebra/solving_with_linear_algebra.ipynb)
## Module 4 Section 27 - Calculus, Cost Function, and Gradient Descent
Derivatives
- [derivatives.ipynb](Mathematics/Calculus/derivatives.ipynb)
* Gradient Descent
- [gradient_descent.ipynb](Mathematics/Calculus/gradient_descent.ipynb)
* Gradient Descent Walkthrough
- [walkthrough_gradient_descent.ipynb](Mathematics/Calculus/walkthrough_gradient_descent.ipynb)
## Module 4 Section 28 - Extensions to Linear Models
* Improving Linear Regression (Interactions & Polynomial)
- [improving_linear_regression.ipynb](StatisticalModeling/ExtendingLinearRegression/improving_linear_regression.ipynb)
* Regularization
- [regularization.ipynb](StatisticalModeling/ExtendingLinearRegression/regularization.ipynb)
* Bias & Variance
- [bias_and_variance.ipynb](EvaluatingModels/bias_and_variance.ipynb)
## Module 4 Section 29 - Introduction to Logistic Regression
* Logistic Regression Intro
- [logistic_regression_intro.ipynb](MachineLearning/LogisticRegression/logistic_regression_intro.ipynb)
* Logistic Regression
- [logistic_regression.ipynb](MachineLearning/LogisticRegression/logistic_regression.ipynb)
* Evaluation Metrics (Confusion Matrices)
- [evaluation_metrics.ipynb](EvaluatingModels/evaluation_metrics.ipynb)
* Evaluation Curves (ROC & AUC)
- [evaluation_curves.ipynb](EvaluatingModels/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](StatisticalModeling/TimeSeries/time_series_intro.ipynb)
* Time Series Visualization
- [time_series_visualization.ipynb](StatisticalModeling/TimeSeries/time_series_visualization.ipynb)
* Time Series Trends
- [time_series_trends.ipynb](StatisticalModeling/TimeSeries/time_series_trends.ipynb)
## Module 4 Section 32 - Time Series Modeling
* Time Series Models Intro
- [time_series_models_basic.ipynb](StatisticalModeling/TimeSeries/time_series_models_basic.ipynb)
* ARMA Model
- [time_series_model_arma.ipynb](StatisticalModeling/TimeSeries/time_series_model_arma.ipynb)
Module 5
## Module 5 Section 33 - K Nearest Neighbors
* Distance Metrics
- [distance_metrics.ipynb](MachineLearning/KNN/distance_metrics.ipynb)
* K Nearest Neighbors
- [k_nearest_neighbors.ipynb](MachineLearning/KNN/k_nearest_neighbors.ipynb)
## Module 5 Section 34 - Decision Trees
* Decision Trees
- [decision_trees_intro.ipynb](MachineLearning/DecisionTrees/decision_trees_intro.ipynb)
- [information_to_make_decisions.ipynb](MachineLearning/DecisionTrees/information_to_make_decisions.ipynb)
- [decision_tree_hyperparameters.ipynb](MachineLearning/DecisionTrees/decision_tree_hyperparameters.ipynb)
- [decision_tree_code_example.ipynb](MachineLearning/DecisionTrees/decision_tree_code_example.ipynb)
## Module 5 Section 35 - Ensemble Methods
* Ensemble Methods (Bagging, Random Forest, Adaboost, Gradient Boosting)
- [ensemble_methods.ipynb](MachineLearning/Ensembles/ensemble_methods.ipynb)
- [bagging.ipynb](MachineLearning/Ensembles/bagging.ipynb)
- [boosting.ipynb](MachineLearning/Ensembles/boosting.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------|------------|------------------------|
| Ensemble Machine Learning: Bagging & Boosting | 2019-10-24 | [youtu.be/xI-XdP2FLis](https://youtu.be/xI-XdP2FLis] |
| Machine Learning with Ensembles: Bagging & Boosting | 2020-09-21 | [youtu.be/nIYnh6uAun0](https://youtu.be/nIYnh6uAun0] |
| Ensemble Methods in Machine Learning: Bagging & Boosting | 2019-11-08 | [youtu.be/j1B1k1PZ8Wg](https://youtu.be/j1B1k1PZ8Wg] |
## Module 5 Section 36 - Support Vector Machines
* Support Vector Machine Intro
- [support_vector_machine_intro.ipynb](MachineLearning/SupportVectorMachine/support_vector_machine_intro.ipynb)
* Kernel Trick
- [kernel_trick.ipynb](MachineLearning/SupportVectorMachine/kernel_trick.ipynb)
## Module 5 Section 37 - Principal Component Analysis
* Dimensionality
- [dimensionality.ipynb](MachineLearning/PCA/dimensionality.ipynb)
* Principal Component Analysis
- [pca.ipynb](MachineLearning/PCA/pca.ipynb)
- [pca_example.ipynb](MachineLearning/PCA/pca_example.ipynb)
## Module 5 Section 38 - Clustering
* K-means
- [k_means.ipynb](MachineLearning/Clustering/k_means.ipynb)
- [k_means_issues.ipynb](MachineLearning/Clustering/k_means_issues.ipynb)
* Hierarchical Clustering
- [hierarchical_clustering.ipynb](MachineLearning/Clustering/hierarchical_clustering.ipynb)
* DBSCAN
- [dbscan.ipynb](MachineLearning/Clustering/dbscan.ipynb)
## Module 5 Section 39 - Building a Machine Learning Pipeline
* Pipelines
- [pipeline_intro.ipynb](MachineLearning/Pipelines/pipeline_intro.ipynb)
* Grid Search
- [grid_search.ipynb](MachineLearning/Pipelines/grid_search.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Machine Learning Pipelines | 2019-11-14 |[youtu.be/SjeEM0r7RZo](https://www.youtu.be/SjeEM0r7RZo)|
|Grid Search of Hyperparameters | 2019-11-14 |[youtu.be/oi2NjZPQcmQ](https://www.youtu.be/oi2NjZPQcmQ)|
## Module 5 Section 40 - Big Data in PySpark
* Big Data Introduction
- [big_data_intro.ipynb](BigData/big_data_intro.ipynb)
* Distributed Computing
- [distributed_parallel_computing.ipynb](BigData/distributed_parallel_computing.ipynb)
- [tools_of_distributed_systems.ipynb](BigData/tools_of_distributed_systems.ipynb)
* MapReduce
- [map_reduce.ipynb](BigData/MapReduce/map_reduce.ipynb)
- [map_reduce_code.ipynb](BigData/MapReduce/map_reduce_code.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Big Data & MapReduce | 2019-11-12 |[youtu.be/LQVXvg1dL-8](https://youtu.be/LQVXvg1dL-8)|
|Intro to Identifying & Handling Big Data| 2019-08-15 |[youtu.be/tRd_hVTxk24](https://youtu.be/tRd_hVTxk24)|
|Intro to MapReduce | 2019-08-15 |[youtu.be/2Amvm-BpCxg](https://youtu.be/2Amvm-BpCxg)|
|MapReduce Coding Example | 2019-08-15 |[youtu.be/AwsWrryp6tY](https://youtu.be/AwsWrryp6tY)|
## Module 5 Section 41 - Recommendation Systems
* Recommendation Systems
- [recommendation_systems_intro.ipynb](MachineLearning/RecommendationSystems/recommendation_systems_intro.ipynb)
* Neighbor Memory Based Collab Filtering
- [neighbor_memory_based_collab_filtering.ipynb](MachineLearning/RecommendationSystems/neighbor_memory_based_collab_filtering.ipynb)
* Matrix Factorization
- [matrix_factorization.ipynb](MachineLearning/RecommendationSystems/matrix_factorization.ipynb)
### Recordings
| Title | Date | URL |
|----------------------------------------|------------|------------------------|
|Recommendation Systems Intro | 2019-11-15 | [youtu.be/lIIAEVxRl50](https://youtu.be/lIIAEVxRl50) |
|Neighbor-Based Collaboraitve Filtering | 2019-11-15 | [youtu.be/pEOPyOCaoHw](https://youtu.be/pEOPyOCaoHw) |
|Matrix Factorization & Embeddings | 2019-11-15 | [youtu.be/olJKadbzdCQ](https://youtu.be/olJKadbzdCQ) |
|Embeddings Discussion | 2019-11-15 | [youtu.be/V_6S4xw0JnQ](https://youtu.be/V_6S4xw0JnQ) |
|Recommendation Systems & Embeddings | 2019-09-18 | [youtu.be/m1pj8hVnmn0](https://youtu.be/m1pj8hVnmn0) |
Module 6
## Module 6 Section 42 - Graph Theory
* Graph Theory
- [graph_theory_basics.ipynb](Mathematics/GraphTheory/graph_theory_basics.ipynb)
- [paths.ipynb](Mathematics/GraphTheory/paths.ipynb)
## Module 6 Section 43 - Foundations of Natural Language Processing
* NLP Introduction
- [intro_to_nlp.ipynb](NLP/intro_to_nlp.ipynb)
- [text_processing.ipynb](NLP/text_processing.ipynb)
- [feature_extraction.ipynb](NLP/feature_extraction.ipynb)
## Module 6 Section 44 - Introduction to Deep Learning
* Neural Networks
- [neural_networks.ipynb](DeepLearning/NeuralNetworks/neural_networks.ipynb)
- [activation_functions.ipynb](DeepLearning/NeuralNetworks/activation_functions.ipynb)
- [keras_implementation.ipynb](MachineLearning/DeepLearning/keras_implementation.ipynb)
## Module 6 Section 45 - Multi-Layer Perceptrons
* Neural Networks & Parts
- [neural_networks.ipynb](DeepLearning/NeuralNetworks/neural_networks.ipynb)
- [activation_functions.ipynb](DeepLearning/NeuralNetworks/activation_functions.ipynb)
- [keras_implementation.ipynb](DeepLearning/NeuralNetworks/keras_implementation.ipynb)
## Module 6 Section 46 - Tuning Neural Networks
* Overfitting
- [avoiding_overfitting.ipynb](DeepLearning/NeuralNetworks/avoiding_overfitting.ipynb)
* Optimization
- [optimizations.ipynb](DeepLearning/NeuralNetworks/optimizations.ipynb)
## Moduel Section 49 - Deep NLP - Word Embeddings
* Word Embeddings
- [embeddings.ipynb](NLP/embeddings.ipynb)