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Getting Started with Data Science\n\n* Data Science \n    - [DataScienceIntro.ipynb](DataScienceBasics/DataScienceIntro.ipynb)\n\n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Introduction to Data Science            | 2021-01-11 | [youtu.be/R7pM6SluD60](https://youtu.be/R7pM6SluD60) |\n\n## Phase 1 Topic 02 - Bash and Git\n\n* Bash\n    - [command_line_basics.ipynb](CommandLine/Unix/command_line_basics.ipynb)\n* Git \\\u0026 GitHub\n    - [git_intro.ipynb](Git/git_intro.ipynb)\n    - [github.ipynb](Git/github.ipynb)\n    - [Git Tools](Git/Tools)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Command Line Basics                     | 2021-01-11 | [youtu.be/fjZFp2oTveg](https://youtu.be/fjZFp2oTveg) |\n|Intro to Using Git with GitHub          | 2021-01-12 | [youtu.be/GGh9X5Iby10](https://youtu.be/GGh9X5Iby10) |    \n\n\n## Phase 1 Topic 03 - Control Flow, Functions, and Statistics\n\n* Python \n    - [core_python.ipynb](CodingBasics/Python/core_python.ipynb)\n* Coding Conventions\n    - [coding_best_practices.ipynb](CodingBasics/CodingConventions/coding_best_practices.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------| \n|Python Conventions \\\u0026 Best Practices    | 2021-01-11 | [youtu.be/3YxS_5dW3aY](https://youtu.be/3YxS_5dW3aY) |\n|Python Basics                           | 2021-01-11 | [youtu.be/0ffOdnVmjHg](https://youtu.be/0ffOdnVmjHg) |\n|Some More Python Basics: Control Flow   | 2021-01-12 | [youtu.be/iLrqpbZvWb0](https://youtu.be/iLrqpbZvWb0) |\n|More Python: Functions                  | 2021-01-13 | [youtu.be/FrklluKZWHw](https://youtu.be/FrklluKZWHw) |\n\n    \n## Phase 1 Topic 04 - Python Libraries: Numpy and Pandas\n\n* NumPy \n    - [intro_to_numpy.ipynb](CodingBasics/NumPy/intro_to_numpy.ipynb)\n    - [numpy_intro_activity.ipynb](CodingBasics/NumPy/numpy_intro_activity.ipynb)\n* Pandas\n    - [from_numpy_to_pandas.ipynb](DataScienceBasics/Pandas/from_numpy_to_pandas.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------| \n|Intro to NumPy                          | 2021-01-13 | [youtu.be/-z-n8Hrtvl8](https://youtu.be/-z-n8Hrtvl8) |\n|Intro to Pandas from NumPy              | 2021-01-15 | [youtu.be/S7p2w4cXc9o](https://youtu.be/S7p2w4cXc9o) |\n    \n## Phase 1 Topic 05 - Data Cleaning in Pandas\n\n* Data Cleaning\n    - [manipulating_data.ipynb](DataScienceBasics/Pandas/manipulating_data.ipynb)\n    - [exploring_data.ipynb](DataScienceBasics/Pandas/exploring_data.ipynb)\n    - [data_cleaning_with_pandas_overview.ipynb](DataScienceBasics/Pandas/data_cleaning_with_pandas_overview.ipynb)\n* Aggregation\n    - [aggregation.ipynb](DataScienceBasics/Pandas/aggregation.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------| \n|More Pandas: Exploring and Manipulating Data | 2021-01-19 | [youtu.be/m67HtpXYv3U](https://youtu.be/m67HtpXYv3U) |\n \n\n## Phase 1 Topic 06 - Data Visualization\n\n* Warmup\n    - [visualization_warmup.ipynb](DataScienceBasics/Visualization/visualization_warmup.ipynb)\n* Data Visualization\n    - [motivation.ipynb](DataScienceBasics/Visualization/motivation.ipynb)\n    - [how_to_use_visualizations.ipynb](DataScienceBasics/Visualization/how_to_use_visualizations.ipynb)\n    - [good_visualizations.ipynb](DataScienceBasics/Visualization/good_visualizations.ipynb)\n    - [down_with_pie_chart.ipynb](DataScienceBasics/Visualization/down_with_pie_chart.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------| \n|Concepts of Data Visualization          | 2021-01-20 | [youtu.be/AxlWpplunVo](https://youtu.be/AxlWpplunVo) |\n\n\n## Phase 1 Topic 07 - SQL and Relational Databases \\\u0026 Phase 1 Topic 08 - Other Database Structures\n\n* SQL\n    - [intro_to_sql.ipynb](DataEngineering/SQL/intro_to_sql.ipynb)\n    - [using_sql.ipynb](DataEngineering/SQL/using_sql.ipynb)\n    - [sql_lesson.ipynb](DataEngineering/SQL/sql_lesson.ipynb)\n    - [sql_exercises.ipynb](DataEngineering/SQL/sql_exercises.ipynb)\n    - [joins.ipynb](DataEngineering/SQL/joins.ipynb)\n    - [advanced_topics.ipynb](DataEngineering/SQL/advanced_topics.ipynb)\n* SQLite\n    - [sqlalchemy.ipynb](DataEngineering/SQLite/sqlalchemy.ipynb)\n    - [sqlite.ipynb](DataEngineering/SQLite/sqlite.ipynb)    \n* NoSQL\n    - [nosql_intro.ipynb](DataEngineering/NoSQL/nosql_intro.ipynb)\n    - [mongodb.ipynb](DataEngineering/NoSQL/mongodb.ipynb) \n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------| \n|SQL with Python \\\u0026 Pandas               | 2021-01-22 | [youtu.be/VFN89HOa9m0](https://youtu.be/VFN89HOa9m0) |\n\n\n\u003c/details\u003e\n\n----------------------------\n\n\n# Curriculum (v2.1)\n\n\u003cdetails\u003e\n\u003csummary style=\"font-weight:bold;\"\u003eModule 1\u003c/summary\u003e\n    \n## Module 1 Section 01 - Getting Started with Data Science\n\n* Python \n    - [core_python.ipynb](CodingBasics/Python/core_python.ipynb)\n* Coding Conventions\n    - [coding_best_practices.ipynb](CodingBasics/CodingConventions/coding_best_practices.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|The Data Science Process            | 2020-01-23 | [youtu.be/UZlPoaD4Bvw](https://youtu.be/UZlPoaD4Bvw) |\n|Python Basics \u0026 Coding Practices  | 2020-01-23 | [youtu.be/uw4in0E8vvE](https://youtu.be/uw4in0E8vvE) |\n\n\n## Module 1 Section 02 - Bash and Git\n\n* Bash Shell (Command Line Interface)\n    - [command_line_basics.ipynb](CommandLine/Unix/command_line_basics.ipynb)\n* Git \u0026 GitHub\n    - [git_intro.ipynb](Git/git_intro.ipynb)\n    - [github.ipynb](Git/github.ipynb)\n    - [git_collaboration.ipynb](Git/git_collaboration.ipynb)\n    - [git_advanced.ipynb](Git/git_advanced.ipynb)\n* Activities\n    - [git_collaboration_activity.ipynb](Git/Activity/git_collaboration_activity.ipynb)\n* Extras for Using Git\n    - [Git/Tools/](Git/Tools/)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Forking a GitHub Repo            | 2020-01-22 | [youtu.be/SOKH8Xni_BE](https://youtu.be/SOKH8Xni_BE) |\n|Copy GitHub Repo Without Forking  | 2020-01-22 | [youtu.be/q0_MMK8AS8E](https://youtu.be/q0_MMK8AS8E) |\n|Command Line Basics       | 2020-01-28 | [youtu.be/Nta5HpFKDRc](https://youtu.be/Nta5HpFKDRc) | \n|The Git Basics       | 2020-01-28 | [youtu.be/Rx85RNB4gn4](https://youtu.be/Rx85RNB4gn4) | \n|GitHub Basics with Git       | 2020-01-28 | [youtu.be/F-VQbMxgm1o](https://youtu.be/F-VQbMxgm1o) | \n\n\n## Module 1 Section 03 - Control Flow, Functions, and Statistics\n\n* Control Flow\n    - [core_python.ipynb](CodingBasics/Python/core_python.ipynb)\n* Functions\n    - [functions.ipynb](CodingBasics/Python/functions.ipynb)\n* Statistics\n    - [summary_statistics.ipynb](ProbabilityAndStats/StatisticsBasics/summary_statistics.ipynb)\n    - Correlation \u0026 Correlation [linear_regressions_and_simple_relationships.ipynb](MachineLearning/LinearRegression/linear_regressions_and_simple_relationships.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Python Basics: Lists, Dictionaries, and More | 2020-01-29 | [youtu.be/Mdi1dWzCIZE](https://youtu.be/Mdi1dWzCIZE) |\n|Python Basics: Control Flow             | 2020-01-29 | [youtu.be/q1ZMx9p6dJo](https://youtu.be/q1ZMx9p6dJo) |\n|Python Basics: Functions                | 2020-01-29 | [youtu.be/7pcILR2LtKo](https://youtu.be/7pcILR2LtKo) |\n\n\n## Module 1 Section 04 - Python Libraries: NumPy and Pandas\n\n* NumPy\n    - [intro_to_numpy.ipynb](CodingBasics/NumPy/intro_to_numpy.ipynb)\n    - (OPTIONAL EXTRA) [math_with_tensors.ipynb](Mathematics/LinearAlgebra/math_with_tensors.ipynb)\n    - Activity: [numpy_intro_activity.ipynb](CodingBasics/NumPy/numpy_intro_activity.ipynb)\n* Pandas\n    - [from_numpy_to_pandas.ipynb](DataScienceBasics/Pandas/from_numpy_to_pandas.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|NumPy Intro                             | 2020-02-05 | [youtu.be/Ea5tmWo0e5k](https://youtu.be/Ea5tmWo0e5k) |\n|NumPy Activity                          | 2020-02-05 | [youtu.be/ROiNq5WTjCc](https://youtu.be/ROiNq5WTjCc) |\n|From NumPy to Pandas                    | 2020-02-05 | [youtu.be/Ng_TzUentmk](https://youtu.be/Ng_TzUentmk) |    \n    \n## Module 1 Section 05 - Data Cleaning in Pandas\n\n* Pandas \u0026 Data\n    - [from_numpy_to_pandas.ipynb](DataScienceBasics/Pandas/from_numpy_to_pandas.ipynb)\n    - [manipulating_data.ipynb](DataScienceBasics/Pandas/manipulating_data.ipynb)    \n    - [aggregation.ipynb](DataScienceBasics/Pandas/aggregation.ipynb)\n    \u003c!-- TODO and coming soon\n    - [combining_data.ipynb](DataScienceBasics/Pandas/combining_data.ipynb)\n    --\u003e\n* Data Exploration \u0026 Cleaning\n    - [data_cleaning_with_pandas_overview.ipynb](DataScienceBasics/Pandas/data_cleaning_with_pandas_overview.ipynb)\n    - [exploring_data.ipynb](DataScienceBasics/Pandas/exploring_data.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Brief Extra: Pandas \u0026 Loading Data      | 2020-02-05 | [youtu.be/-nr7bi7lVxQ](https://youtu.be/-nr7bi7lVxQ) |\n|Data Exploration with Pandas            | 2020-02-11 | [youtu.be/W_ey_4uIGQ0](https://youtu.be/W_ey_4uIGQ0) |\n|Data Exploration \u0026 Cleaning with Python | 2020-02-11 | [youtu.be/KXNzYfWUoUM](https://youtu.be/KXNzYfWUoUM) |\n\n## Module 1 Section 06 - Data Visualization\n\n* Data Visualization Intro\n    - [motivation.ipynb](DataScienceBasics/Visualization/motivation.ipynb)\n    - [how_to_use_visualizations.ipynb](DataScienceBasics/Visualization/how_to_use_visualizations.ipynb)    \n* Good \u0026 Bad Visualizations\n    - [good_visualizations.ipynb](DataScienceBasics/Visualization/good_visualizations.ipynb)\n    - [down_with_pie_chart.ipynb](DataScienceBasics/Visualization/down_with_pie_chart.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Why Should I Visualize Data?            | 2020-02-11 | [youtu.be/AjEdgBRbvUU](https://youtu.be/AjEdgBRbvUU) |\n|Who Are Visualizations For?             | 2020-02-11 | [youtu.be/8t452nMFApc](https://youtu.be/8t452nMFApc) |\n|Visualizations: The Good, The Bad \u0026 The Ugly| 2020-02-12 | [youtu.be/yvwyvCt8qAI](https://youtu.be/yvwyvCt8qAI) |\n|Data Exploration Activity               | 2020-02-12 | [youtu.be/XPT6QgMbPos](https://youtu.be/XPT6QgMbPos) |\n\n\n## Module 1 Section 07 - SQL and Relational Databases\n\n* Introduction to SQL\n    - [sql_lesson.ipynb](DataEngineering/SQL/sql_lesson.ipynb)\n    - [intro_to_sql.ipynb](DataEngineering/SQL/intro_to_sql.ipynb)\n    - [sql_exercises.ipynb](DataEngineering/SQL/sql_exercises.ipynb)\n* More SQL\n    - [using_sql.ipynb](DataEngineering/SQL/using_sql.ipynb)\n    - [joins.ipynb](DataEngineering/SQL/joins.ipynb)\n    - [advanced_topics.ipynb](DataEngineering/SQL/advanced_topics.ipynb)\n\n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|SQL \u0026 Realtional Databases Intro        | 2020-02-18 | [youtu.be/Ca-8RRZlLLo](https://youtu.be/Ca-8RRZlLLo) |\n|Running SQL in Python                   | 2020-02-18 | [youtu.be/IjF3bNF-eHc](https://youtu.be/IjF3bNF-eHc) |\n|More SQL \u0026 Joining Tables               | 2020-02-18 | [youtu.be/1PXDL-S71Cc](https://youtu.be/1PXDL-S71Cc) |\n|Creating and Updating SQL Databases     | 2020-02-18 | [youtu.be/c8Gyv_LXH8o](https://youtu.be/c8Gyv_LXH8o) |\n|SQL \u0026 Execution Order                   | 2020-02-19 | [youtu.be/NJEOpxZP9TI](https://youtu.be/NJEOpxZP9TI) |\n|SQL Subqueries                          | 2020-02-19 | [youtu.be/mAEgY7BGlN8](https://youtu.be/mAEgY7BGlN8) |\n\n## Module 1 Section 08: Other Database structures\n\n### Recordings\n\n## Module 1 Section 09: JSON and APIs\n\n* JSON\n    - [json_and_xml_intro.ipynb](DataEngineering/JSONAndXML/json_and_xml_intro.ipynb)\n* APIs\n    - [apis.ipynb](DataEngineering/APIs/apis.ipynb)\n    - [lifx_example.ipynb](DataEngineering/APIs/lifx_example.ipynb)\n\n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|JSON Data Format for Python             | 2020-02-19 | [youtu.be/EbCjd6OPdvg](https://youtu.be/EbCjd6OPdvg) |\n|APIs with Python                        | 2020-02-19 | [youtu.be/NsfITpjTqAA](https://youtu.be/NsfITpjTqAA) |\n|API Example with LIFX                   | 2020-02-19 | [youtu.be/-zsoxAzkSLU](https://youtu.be/-zsoxAzkSLU) |\n\n\n## Module 1 Section 10: HTML, CSS, and Web Scraping\n\n* HTML \u0026 CSS\n    - [html_css_intro.ipynb](DataEngineering/WebScraping/html_css_intro.ipynb)\n* Web Scraping\n    - [web_scraping.ipynb](DataEngineering/WebScraping/web_scraping.ipynb)\n    - [web_scraping_beautiful_soup_activity_00.ipynb](Activities/web_scraping_beautiful_soup_activity_00.ipynb) {**IN PROGRESS**}\n\n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|HTML and CSS Intro for Web Scraping     | 2020-02-26 | [youtu.be/MadMEVGMTUE](https://youtu.be/MadMEVGMTUE) |\n|Intro \u0026 Ethics to Web Scraping          | 2020-02-26 | [youtu.be/ceH08GJlIOo](https://youtu.be/ceH08GJlIOo) |\n|Web Scraping with Python \u0026 Beautiful Soup| 2020-02-26|[youtu.be/f6lj7xC0Y2g](https://youtu.be/f6lj7xC0Y2g) |\n|Web Scraping Demo: Adventure Time       | 2020-02-26 | [youtu.be/v_a1qUuXd1Y](https://youtu.be/v_a1qUuXd1Y) |\n\n\n\n## Module 1 Project: Movie Analysis\n\n* Project Details\n    - [mod1_project_notes-pt_012120.ipynb](Projects/MovieAnalysis/mod1_project_notes-pt_012120.ipynb)\n* Advice\n    - [general_advice.ipynb](Projects/general_advice.ipynb)\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary style=\"font-weight:bold;\"\u003eModule 2\u003c/summary\u003e\n    \n## Module 2 Section 11 - Combinatorics and Probability\n\n* Conditional Probability \n    - [probability_and_notation.ipynb](ProbabilityAndStats/Probability/probability_and_notation.ipynb)\n    - [conditional_probability.ipynb](ProbabilityAndStats/Probability/conditional_probability.ipynb)\n* Combinatorics\n    - [combinatorics.ipynb](ProbabilityAndStats/Probability/combinatorics.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n|Conditional Probability     | 2020-03-17 | [youtu.be/JDgm4Wqsvuw](https://youtu.be/JDgm4Wqsvuw) |\n|Combinatorics               | 2020-03-17 | [youtu.be/hs5EFpUcTzw](https://youtu.be/hs5EFpUcTzw) |\n\n\n## Module 2 Section 12 - Statistical Distributions\n\n* Statistical Distributions\n    - [statistical_distributions_intro.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions_intro.ipynb)\n    - [statistical_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions.ipynb)\n    - [more_statistical_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/more_statistical_distributions.ipynb)    \n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| Frequency Distributions \u0026 More Statistics | 2020-03-19 | [youtu.be/bNUpLoDgLig](https://youtu.be/bNUpLoDgLig) |\n| Review \u0026 Other Statistical Distributions | 2020-03-24 | [youtu.be/YRor7gBV9Kw](https://youtu.be/YRor7gBV9Kw) |\n| Even More Statistical Distributions | 2020-03-24 | [youtu.be/dVSnNHKyeAM](https://youtu.be/dVSnNHKyeAM) |\n\n## Module 2 Section 13 - Central Limit Theorem and Confidence Intervals\n\n* Central Limit Theorem\n    - [sampling.ipynb](ProbabilityAndStats/StatisticalDistributions/sampling.ipynb)\n    - [central_limit_theorem.ipynb](ProbabilityAndStats/StatisticalDistributions/central_limit_theorem.ipynb)\n* Confidence Intervals\n    - [confidence_intervals.ipynb](ProbabilityAndStats/StatisticalDistributions/confidence_intervals.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| Sampling | 2020-03-24 | [youtu.be/x5KVX3ccbuc](https://youtu.be/x5KVX3ccbuc) |\n| Central Limit Theorem | 2020-03-24 | [youtu.be/c2NDqWrCBno](https://youtu.be/c2NDqWrCBno) |\n| Where Do Confidence Intervals Come From? | 2020-03-26 | [youtu.be/jHLoLCCtumc](https://youtu.be/jHLoLCCtumc) |\n\n\n\n## Module 2 Section 14 - Hypothesis Testing\n\n* Experiment Design\n    - [experiment_design_intro.ipynb](ProbabilityAndStats/ExperimentalDesign/experiment_design_intro.ipynb)\n    - [hypothesis_testing_intro.ipynb](ProbabilityAndStats/ExperimentalDesign/hypothesis_testing_intro.ipynb)\n* Considerations\n    - [warnings.ipynb](ProbabilityAndStats/ExperimentalDesign/warnings.ipynb)\n    - [multiple_comparisons.ipynb](ProbabilityAndStats/ExperimentalDesign/multiple_comparisons.ipynb)\n* Statistical Tests\n    - [statistical_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_tests.ipynb)\n    - [types_of_errors.ipynb](ProbabilityAndStats/ExperimentalDesign/types_of_errors.ipynb)\n* t-Tests\n    - [t_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/t_distributions.ipynb)\n    - [t_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/t_tests.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| What Makes a Good Experiment? | 2020-03-26 | [youtu.be/746no4_NvRM](https://youtu.be/746no4_NvRM) |\n| Hypothesis Testing Intro | 2020-03-26 | [youtu.be/TE8C-PsZfrw](https://youtu.be/TE8C-PsZfrw) |\n| Hypothesis Testing | 2020-03-31 | [youtu.be/JnO5wKYnNfQ](https://youtu.be/JnO5wKYnNfQ) |\n| The t-Distribution \u0026 t-Test | 2020-03-31 | [youtu.be/8zey4ICieg0](https://youtu.be/8zey4ICieg0) |\n| Type 1 vs Type 2 Errors | 2020-03-31 | [youtu.be/1IybE0mXWl4](https://youtu.be/1IybE0mXWl4) |\n\n\n## Module 2 Section 15 - Statistical Power \u0026 ANOVA\n\n* Parts of Hypothesis Tests\n    - [types_of_errors.ipynb](ProbabilityAndStats/ExperimentalDesign/types_of_errors.ipynb)\n    - [statistical_power.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_power.ipynb)\n    - [effect_size.ipynb](ProbabilityAndStats/ExperimentalDesign/effect_size.ipynb)\n* Welch's t-test \u0026 ANOVA\n    - [welchs_t_test.ipynb](ProbabilityAndStats/ExperimentalDesign/welchs_t_test.ipynb)\n    - [multiple_comparisons.ipynb](ProbabilityAndStats/ExperimentalDesign/multiple_comparisons.ipynb)\n    - [anova.ipynb](ProbabilityAndStats/ExperimentalDesign/anova.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n|Effect Size \u0026 Statistical Power Relationship | 2020-03-31 | [youtu.be/0HtaoDgOF_A](https://youtu.be/0HtaoDgOF_A) |\n|Welch's t-Test vs Student's t-Test | 2020-04-01| [youtu.be/QNftsEYSwFA](https://youtu.be/QNftsEYSwFA) |\n|Multiple Comparisons Warning | 2020-04-07| [youtu.be/voHPvSkX3f4](https://youtu.be/voHPvSkX3f4) |\n|Introduction to ANOVA | 2020-04-07| [youtu.be/y1UWYQHw5Jo](https://youtu.be/y1UWYQHw5Jo) |\n|Coding ANOVA: SciPy Method | 2020-04-07| [youtu.be/QnE8sBrKoNU](https://youtu.be/QnE8sBrKoNU) |\n|Coding ANOVA: Statsmodels OLS Method | 2020-04-07| [youtu.be/3cCM0lQFMM4](https://youtu.be/3cCM0lQFMM4) |\n\n\n## Module 2 Section 16 - A/B Testing\n\n* A/B Testing\n    - [ab_testing.ipynb](ProbabilityAndStats/ExperimentalDesign/ab_testing.ipynb)\n    - [ab_test_walkthrough.ipynb](ProbabilityAndStats/ExperimentalDesign/ab_test_walkthrough.ipynb)\n\n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| A/B Testing | 2020-04-07 | [youtu.be/2DVXuR-2LeA](https://youtu.be/2DVXuR-2LeA) |\n\n\n\n## Module 2 Section 17 - Bayesian Statistics\n\n* Bayes' Theorem\n    - [bayes_theorem.ipynb](ProbabilityAndStats/BayesianClassification/bayes_theorem.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| Bayesian Thinking          | 2020-04-21 | [youtu.be/odZOxI_3BNI](https://youtu.be/odZOxI_3BNI] |\n| Bayes' Theorem Coding Example: Testing Positive | 2020-04-21 | [youtu.be/yN7BPP25Bvg](https://youtu.be/yN7BPP25Bvg] |\n| Visual of Bayes' Theorem   | 2020-04-21 | [youtu.be/ib1a7c8MrtQ](https://youtu.be/ib1a7c8MrtQ] |\n| Bayes' Theorem Followup: Testing Positive Twice | 2020-04-21 | [youtu.be/VgGUngEkYok](https://youtu.be/VgGUngEkYok] |\n\n\n## Module 2 Section 18 - Introduction to Linear Regression\n\n* Simple Linear Regression\n    - [linear_regressions_and_simple_relationships.ipynb](MachineLearning/LinearRegression/linear_regressions_and_simple_relationships.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n|Intro to Linear Regression  | 2020-04-09 | [youtu.be/PBv749p-9yY](https://youtu.be/PBv749p-9yY) |\n\n\n## Module 2 Section 19 - Multiple Linear Regression\n\n* Multiple Linear Regression\n    - [multiple_linear_regression.ipynb](MachineLearning/LinearRegression/multiple_linear_regression.ipynb)\n    - [multicollinearity.ipynb](MachineLearning/LinearRegression/multicollinearity.ipynb)\n    - [model_validation.ipynb](EvaluatingModels/model_validation.ipynb)\n    - [linear_regression_example.ipynb](MachineLearning/LinearRegression/linear_regression_example.ipynb)\n    \n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n|Multiple Linear Regression | 2020-04-15| [youtu.be/drbltsGcRNQ](https://youtu.be/drbltsGcRNQ)|\n|Handling Categorical Variables | 2020-04-15| [youtu.be/57Cy58UnKv0](https://youtu.be/57Cy58UnKv0)|\n|Dealing with Multicollinearity | 2020-04-16| [youtu.be/eGSG79vF6_E](https://youtu.be/eGSG79vF6_E)|\n|Validating Models \u0026 k-Fold Cross-Validation | 2020-04-16| [youtu.be/nmIxCbv09G0](https://youtu.be/nmIxCbv09G0)|\n\n\n## Module 2 Section 20 - Extensions to Linear Regression\n\n* Polynomial \u0026 Interacting Terms\n    - [improving_linear_regression.ipynb](MachineLearning/LinearRegression/ExtendingLinearRegression/improving_linear_regression.ipynb)\n\n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| Extending Linear Regression: Polynomial \u0026 Interacting Terms | 2020-04-22 | [youtu.be/QbkwZ9cCb8I](https://youtu.be/QbkwZ9cCb8I] |\n\n\u003c/details\u003e\n\n----------------------------\n\n# Curriculum (v2.0)\n\n\u003cdetails\u003e\n\u003csummary style=\"font-weight:bold;\"\u003eModule 3\u003c/summary\u003e\n    \n## Module 3 Section 17 - Combinatorics \n\n* [probability_and_notation.ipynb](ProbabilityAndStats/Probability/probability_and_notation.ipynb)\n* [conditional_probability.ipynb](ProbabilityAndStats/Probability/conditional_probability.ipynb)\n* Permutations \u0026 Combinations\n    - [combinatorics.ipynb](ProbabilityAndStats/Probability/combinatorics.ipynb)\n\n## Module 3 Section 18 - Statistical Distributions\n\n* [statistical_distributions_intro.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions_intro.ipynb)\n* [statistical_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/statistical_distributions.ipynb)\n\n## Module 3 Section 19 - Central Limit Theorem\n\n* Central Limit Theorem\n    - [sampling-and-central-limit-theorem.ipynb](ProbabilityAndStats/StatisticalDistributions/sampling-and-central-limit-theorem.ipynb)\n* Sampling Statistics\n    - [sampling-and-central-limit-theorem.ipynb](ProbabilityAndStats/StatisticalDistributions/sampling-and-central-limit-theorem.ipynb)\n* Confidence Intervals\n    - [confidence-intervals.ipynb](ProbabilityAndStats/StatisticalDistributions/confidence-intervals.ipynb)\n    - [t_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/t_distributions.ipynb)\n\n## Module 3 Section 20 - Hypothesis Testing\n\n* Intro to Experimental Design\n    - [experiment_design_intro.ipynb](ProbabilityAndStats/ExperimentalDesign/experiment_design_intro.ipynb)\n* P-Values \u0026 Null Hypothesis\n    - [statistical_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_tests.ipynb)\n* Effect Sizes\n    - [effect_size.ipynb](ProbabilityAndStats/ExperimentalDesign/effect_size.ipynb)\n* T-Tests\n    - [t_distributions.ipynb](ProbabilityAndStats/StatisticalDistributions/t_distributions.ipynb)\n    - [t_tests.ipynb](ProbabilityAndStats/ExperimentalDesign/t_tests.ipynb)\n* Type 1 \u0026 Type 2 Errors\n    - [types_of_errors.ipynb](ProbabilityAndStats/ExperimentalDesign/types_of_errors.ipynb)\n\n## Module 3 Section 21 - Statistical Power \u0026 ANOVA\n\n* Statistical Power\n    - [statistical_power.ipynb](ProbabilityAndStats/ExperimentalDesign/statistical_power.ipynb)\n* Welch's T-Test\n    - [welchs_t_test.ipynb](ProbabilityAndStats/ExperimentalDesign/welchs_t_test.ipynb)\n* Multiple Comparisons \u0026 Goodhart's Law\n    - [warnings.ipynb](ProbabilityAndStats/ExperimentalDesign/warnings.ipynb)\n    - [extras.ipynb](ProbabilityAndStats/ExperimentalDesign/extras.ipynb)\n* ANOVA\n    - [anova.ipynb](ProbabilityAndStats/ExperimentalDesign/anova.ipynb)\n\n## Module 3 Section 22 - AB Testing\n\n* A/B Testing\n    - [ab_testings.ipynb](ProbabilityAndStats/ExperimentalDesign/ab_testings.ipynb)\n    \n\u003c!--\n* [mle_parameter_inference.ipynb](ProbabilityAndStats/Probability/mle_parameter_inference.ipynb)\n--\u003e\n\n## Module 3 Section 23 - Bayesian Statistics\n\n* Bayes Theorem\n    - [bayes_theorem.ipynb](ProbabilityAndStats/BayesianClassification/bayes_theorem.ipynb)\n* Naive Bayes\n    - [naive_bayes_classification.ipynb](ProbabilityAndStats/BayesianClassification/naive_bayes_classification.ipynb)\n      \n## Module 3 Section 24 - Resampling and Monte Carlo Simulation\n\n* Data Generation\n    - [data_generation.ipynb](ProbabilityAndStats/DataGeneration/data_generation.ipynb)\n* Resampling\n    - [resampling.ipynb](ProbabilityAndStats/DataGeneration/resampling.ipynb)\n* Monte Carlo\n    - [monte_carlo.ipynb](ProbabilityAndStats/DataGeneration/monte_carlo.ipynb) \n    - [ultimate_hopscotch_simulation.ipynb](ProbabilityAndStats/DataGeneration/ultimate_hopscotch_simulation.ipynb)\n\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary style=\"font-weight:bold;\"\u003eModule 4\u003c/summary\u003e\n\n## Module 4 Section 25 - A Complete Data Science Project Using Multiple Regression\n\n## Module 4 Section 26 - Linear Algebra\n\n* Linear Algebra Intro\n    - [intro_to_linear_algebra](Mathematics/LinearAlgebra/intro_to_linear_algebra.ipynb)\n* Math with Tensors\n    - [math_with_tensors.ipynb](Mathematics/LinearAlgebra/math_with_tensors.ipynb)\n* Solving With Linear Algebra\n    - [solving_with_linear_algebra.ipynb](Mathematics/LinearAlgebra/solving_with_linear_algebra.ipynb)    \n\n## Module 4 Section 27 - Calculus, Cost Function, and Gradient Descent\n\nDerivatives\n    - [derivatives.ipynb](Mathematics/Calculus/derivatives.ipynb)\n* Gradient Descent\n    - [gradient_descent.ipynb](Mathematics/Calculus/gradient_descent.ipynb)\n* Gradient Descent Walkthrough\n    - [walkthrough_gradient_descent.ipynb](Mathematics/Calculus/walkthrough_gradient_descent.ipynb)    \n\n## Module 4 Section 28 - Extensions to Linear Models\n\n* Improving Linear Regression (Interactions \u0026 Polynomial)\n    - [improving_linear_regression.ipynb](StatisticalModeling/ExtendingLinearRegression/improving_linear_regression.ipynb)\n* Regularization\n    - [regularization.ipynb](StatisticalModeling/ExtendingLinearRegression/regularization.ipynb)\n* Bias \u0026 Variance\n    - [bias_and_variance.ipynb](EvaluatingModels/bias_and_variance.ipynb)\n\n## Module 4 Section 29 - Introduction to Logistic Regression\n\n* Logistic Regression Intro\n    - [logistic_regression_intro.ipynb](MachineLearning/LogisticRegression/logistic_regression_intro.ipynb)\n* Logistic Regression \n    - [logistic_regression.ipynb](MachineLearning/LogisticRegression/logistic_regression.ipynb)\n* Evaluation Metrics (Confusion Matrices)\n    - [evaluation_metrics.ipynb](EvaluatingModels/evaluation_metrics.ipynb)\n* Evaluation Curves (ROC \u0026 AUC)\n    - [evaluation_curves.ipynb](EvaluatingModels/evaluation_curves.ipynb)\n\n## Module 4 Section 30 - In-depth Logistic Regression\n\n## Module 4 Section 31 - Working with Time Series Data\n\n* Time Series Intro\n    - [time_series_intro.ipynb](StatisticalModeling/TimeSeries/time_series_intro.ipynb)\n* Time Series Visualization\n    - [time_series_visualization.ipynb](StatisticalModeling/TimeSeries/time_series_visualization.ipynb)    \n* Time Series Trends\n    - [time_series_trends.ipynb](StatisticalModeling/TimeSeries/time_series_trends.ipynb)\n\n## Module 4 Section 32 - Time Series Modeling\n\n* Time Series Models Intro\n    - [time_series_models_basic.ipynb](StatisticalModeling/TimeSeries/time_series_models_basic.ipynb)\n* ARMA Model\n    - [time_series_model_arma.ipynb](StatisticalModeling/TimeSeries/time_series_model_arma.ipynb)\n\n \n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary style=\"font-weight:bold;\"\u003eModule 5\u003c/summary\u003e\n\n## Module 5 Section 33 - K Nearest Neighbors\n\n* Distance Metrics\n    - [distance_metrics.ipynb](MachineLearning/KNN/distance_metrics.ipynb)\n* K Nearest Neighbors\n    - [k_nearest_neighbors.ipynb](MachineLearning/KNN/k_nearest_neighbors.ipynb)\n    \n\n## Module 5 Section 34 - Decision Trees\n\n* Decision Trees\n    - [decision_trees_intro.ipynb](MachineLearning/DecisionTrees/decision_trees_intro.ipynb)\n    - [information_to_make_decisions.ipynb](MachineLearning/DecisionTrees/information_to_make_decisions.ipynb)\n    - [decision_tree_hyperparameters.ipynb](MachineLearning/DecisionTrees/decision_tree_hyperparameters.ipynb)\n    - [decision_tree_code_example.ipynb](MachineLearning/DecisionTrees/decision_tree_code_example.ipynb)    \n\n\n## Module 5 Section 35 - Ensemble Methods\n\n* Ensemble Methods (Bagging, Random Forest, Adaboost, Gradient Boosting)\n    - [ensemble_methods.ipynb](MachineLearning/Ensembles/ensemble_methods.ipynb)\n    - [bagging.ipynb](MachineLearning/Ensembles/bagging.ipynb)\n    - [boosting.ipynb](MachineLearning/Ensembles/boosting.ipynb)\n### Recordings\n\n| Title                      | Date       | URL                    |\n|----------------------------|------------|------------------------|\n| Ensemble Machine Learning: Bagging \u0026 Boosting | 2019-10-24 | [youtu.be/xI-XdP2FLis](https://youtu.be/xI-XdP2FLis] |\n| Machine Learning with Ensembles: Bagging \u0026 Boosting | 2020-09-21 | [youtu.be/nIYnh6uAun0](https://youtu.be/nIYnh6uAun0] |\n| Ensemble Methods in Machine Learning: Bagging \u0026 Boosting | 2019-11-08 | [youtu.be/j1B1k1PZ8Wg](https://youtu.be/j1B1k1PZ8Wg] |\n\n\n\n## Module 5 Section 36 - Support Vector Machines\n\n* Support Vector Machine Intro\n    - [support_vector_machine_intro.ipynb](MachineLearning/SupportVectorMachine/support_vector_machine_intro.ipynb)\n* Kernel Trick\n    - [kernel_trick.ipynb](MachineLearning/SupportVectorMachine/kernel_trick.ipynb) \n\n\n## Module 5 Section 37 - Principal Component Analysis\n\n* Dimensionality\n    - [dimensionality.ipynb](MachineLearning/PCA/dimensionality.ipynb)\n* Principal Component Analysis\n    - [pca.ipynb](MachineLearning/PCA/pca.ipynb)\n    - [pca_example.ipynb](MachineLearning/PCA/pca_example.ipynb)\n\n\n## Module 5 Section 38 - Clustering\n\n* K-means\n    - [k_means.ipynb](MachineLearning/Clustering/k_means.ipynb)\n    - [k_means_issues.ipynb](MachineLearning/Clustering/k_means_issues.ipynb)\n* Hierarchical Clustering \n    - [hierarchical_clustering.ipynb](MachineLearning/Clustering/hierarchical_clustering.ipynb)\n* DBSCAN\n    - [dbscan.ipynb](MachineLearning/Clustering/dbscan.ipynb) \n\n\n## Module 5 Section 39 - Building a Machine Learning Pipeline\n\n* Pipelines\n    - [pipeline_intro.ipynb](MachineLearning/Pipelines/pipeline_intro.ipynb)\n* Grid Search\n    - [grid_search.ipynb](MachineLearning/Pipelines/grid_search.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Machine Learning Pipelines              | 2019-11-14 |[youtu.be/SjeEM0r7RZo](https://www.youtu.be/SjeEM0r7RZo)|\n|Grid Search of Hyperparameters          | 2019-11-14 |[youtu.be/oi2NjZPQcmQ](https://www.youtu.be/oi2NjZPQcmQ)|\n\n## Module 5 Section 40 - Big Data in PySpark\n\n* Big Data Introduction\n    - [big_data_intro.ipynb](BigData/big_data_intro.ipynb)\n* Distributed Computing\n    - [distributed_parallel_computing.ipynb](BigData/distributed_parallel_computing.ipynb)\n    - [tools_of_distributed_systems.ipynb](BigData/tools_of_distributed_systems.ipynb)\n* MapReduce\n    - [map_reduce.ipynb](BigData/MapReduce/map_reduce.ipynb)\n    - [map_reduce_code.ipynb](BigData/MapReduce/map_reduce_code.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Big Data \u0026 MapReduce                    | 2019-11-12 |[youtu.be/LQVXvg1dL-8](https://youtu.be/LQVXvg1dL-8)|\n|Intro to Identifying \u0026 Handling Big Data| 2019-08-15 |[youtu.be/tRd_hVTxk24](https://youtu.be/tRd_hVTxk24)|\n|Intro to MapReduce                      | 2019-08-15 |[youtu.be/2Amvm-BpCxg](https://youtu.be/2Amvm-BpCxg)|\n|MapReduce Coding Example                | 2019-08-15 |[youtu.be/AwsWrryp6tY](https://youtu.be/AwsWrryp6tY)|\n\n## Module 5 Section 41 - Recommendation Systems\n\n* Recommendation Systems\n    - [recommendation_systems_intro.ipynb](MachineLearning/RecommendationSystems/recommendation_systems_intro.ipynb)\n* Neighbor Memory Based Collab Filtering\n    - [neighbor_memory_based_collab_filtering.ipynb](MachineLearning/RecommendationSystems/neighbor_memory_based_collab_filtering.ipynb)\n* Matrix Factorization\n    - [matrix_factorization.ipynb](MachineLearning/RecommendationSystems/matrix_factorization.ipynb)\n    \n### Recordings\n\n| Title                                  | Date       | URL                    |\n|----------------------------------------|------------|------------------------|\n|Recommendation Systems Intro            | 2019-11-15 | [youtu.be/lIIAEVxRl50](https://youtu.be/lIIAEVxRl50) |\n|Neighbor-Based Collaboraitve Filtering  | 2019-11-15 | [youtu.be/pEOPyOCaoHw](https://youtu.be/pEOPyOCaoHw) |\n|Matrix Factorization \u0026 Embeddings       | 2019-11-15 | [youtu.be/olJKadbzdCQ](https://youtu.be/olJKadbzdCQ) |\n|Embeddings Discussion                   | 2019-11-15 | [youtu.be/V_6S4xw0JnQ](https://youtu.be/V_6S4xw0JnQ) |\n|Recommendation Systems \u0026 Embeddings     | 2019-09-18 | [youtu.be/m1pj8hVnmn0](https://youtu.be/m1pj8hVnmn0) |\n\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary style=\"font-weight:bold;\"\u003eModule 6\u003c/summary\u003e\n\n## Module 6 Section 42 - Graph Theory\n\n* Graph Theory\n    - [graph_theory_basics.ipynb](Mathematics/GraphTheory/graph_theory_basics.ipynb)\n    - [paths.ipynb](Mathematics/GraphTheory/paths.ipynb)\n    \n\n## Module 6 Section 43 - Foundations of Natural Language Processing\n\n* NLP Introduction\n    - [intro_to_nlp.ipynb](NLP/intro_to_nlp.ipynb)\n    - [text_processing.ipynb](NLP/text_processing.ipynb)\n    - [feature_extraction.ipynb](NLP/feature_extraction.ipynb)\n\n\n## Module 6 Section 44 - Introduction to Deep Learning\n\n* Neural Networks\n    - [neural_networks.ipynb](DeepLearning/NeuralNetworks/neural_networks.ipynb)\n    - [activation_functions.ipynb](DeepLearning/NeuralNetworks/activation_functions.ipynb)\n    - [keras_implementation.ipynb](MachineLearning/DeepLearning/keras_implementation.ipynb)\n\n\n## Module 6 Section 45 - Multi-Layer Perceptrons\n\n* Neural Networks \u0026 Parts\n    - [neural_networks.ipynb](DeepLearning/NeuralNetworks/neural_networks.ipynb)\n    - [activation_functions.ipynb](DeepLearning/NeuralNetworks/activation_functions.ipynb)\n    - [keras_implementation.ipynb](DeepLearning/NeuralNetworks/keras_implementation.ipynb)\n\n\n## Module 6 Section 46 - Tuning Neural Networks\n\n* Overfitting\n    - [avoiding_overfitting.ipynb](DeepLearning/NeuralNetworks/avoiding_overfitting.ipynb)\n* Optimization\n    - [optimizations.ipynb](DeepLearning/NeuralNetworks/optimizations.ipynb)\n\n\n## Moduel Section 49 - Deep NLP - Word Embeddings\n\n* Word Embeddings\n    - [embeddings.ipynb](NLP/embeddings.ipynb)\n\n\u003c/details\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrgeislinger%2Fflatiron-school-data-science-curriculum-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmrgeislinger%2Fflatiron-school-data-science-curriculum-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrgeislinger%2Fflatiron-school-data-science-curriculum-resources/lists"}