MrGeislinger
MrGeislinger
flatiron-school-data-science-curriculum-resources
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Lesson material on data science and machine learning topics/concepts

Last updated Mar 14, 2026
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#data-science#data-visualization#deep-learning#gradient-descent#learning#linear-regression#machine-learning#neural#numpy#pandas#sql#statistical-distributions#statistics
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README

Cohort FT-011121

Phase 1

Phase 1 Topic 01 - Getting Started with Data Science

  • Data Science
- DataScienceIntro.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Introduction to Data Science | 2021-01-11 | youtu.be/R7pM6SluD60 |

Phase 1 Topic 02 - Bash and Git

  • Bash
- commandline_basics.ipynb
  • Git \& GitHub
- git_intro.ipynb - github.ipynb - Git Tools

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Command Line Basics | 2021-01-11 | youtu.be/fjZFp2oTveg | |Intro to Using Git with GitHub | 2021-01-12 | youtu.be/GGh9X5Iby10 |

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

  • Python
- core_python.ipynb
  • Coding Conventions
- codingbest_practices.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Python Conventions \& Best Practices | 2021-01-11 | youtu.be/3YxS_5dW3aY | |Python Basics | 2021-01-11 | youtu.be/0ffOdnVmjHg | |Some More Python Basics: Control Flow | 2021-01-12 | youtu.be/iLrqpbZvWb0 | |More Python: Functions | 2021-01-13 | youtu.be/FrklluKZWHw |

Phase 1 Topic 04 - Python Libraries: Numpy and Pandas

  • NumPy
- introto_numpy.ipynb - numpyintro_activity.ipynb
  • Pandas
- fromnumpyto_pandas.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Intro to NumPy | 2021-01-13 | youtu.be/-z-n8Hrtvl8 | |Intro to Pandas from NumPy | 2021-01-15 | youtu.be/S7p2w4cXc9o |

Phase 1 Topic 05 - Data Cleaning in Pandas

  • Data Cleaning
- manipulating_data.ipynb - exploring_data.ipynb - datacleaningwithpandas_overview.ipynb
  • Aggregation
- aggregation.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |More Pandas: Exploring and Manipulating Data | 2021-01-19 | youtu.be/m67HtpXYv3U |

Phase 1 Topic 06 - Data Visualization

  • Warmup
- visualization_warmup.ipynb
  • Data Visualization
- motivation.ipynb - howtouse_visualizations.ipynb - good_visualizations.ipynb - downwithpie_chart.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Concepts of Data Visualization | 2021-01-20 | youtu.be/AxlWpplunVo |

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

  • SQL
- introto_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

| Title | Date | URL | |----------------------------------------|------------|------------------------| |SQL with Python \& Pandas | 2021-01-22 | youtu.be/VFN89HOa9m0 |

Curriculum (v2.1)

Module 1

Module 1 Section 01 - Getting Started with Data Science

  • Python
- core_python.ipynb
  • Coding Conventions
- codingbest_practices.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |The Data Science Process | 2020-01-23 | youtu.be/UZlPoaD4Bvw | |Python Basics & Coding Practices | 2020-01-23 | youtu.be/uw4in0E8vvE |

Module 1 Section 02 - Bash and Git

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

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Forking a GitHub Repo | 2020-01-22 | youtu.be/SOKH8Xni_BE | |Copy GitHub Repo Without Forking | 2020-01-22 | youtu.be/q0_MMK8AS8E | |Command Line Basics | 2020-01-28 | youtu.be/Nta5HpFKDRc | |The Git Basics | 2020-01-28 | youtu.be/Rx85RNB4gn4 | |GitHub Basics with Git | 2020-01-28 | youtu.be/F-VQbMxgm1o |

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

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

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Python Basics: Lists, Dictionaries, and More | 2020-01-29 | youtu.be/Mdi1dWzCIZE | |Python Basics: Control Flow | 2020-01-29 | youtu.be/q1ZMx9p6dJo | |Python Basics: Functions | 2020-01-29 | youtu.be/7pcILR2LtKo |

Module 1 Section 04 - Python Libraries: NumPy and Pandas

  • NumPy
- introto_numpy.ipynb - (OPTIONAL EXTRA) mathwith_tensors.ipynb - Activity: numpyintro_activity.ipynb
  • Pandas
- fromnumpyto_pandas.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |NumPy Intro | 2020-02-05 | youtu.be/Ea5tmWo0e5k | |NumPy Activity | 2020-02-05 | youtu.be/ROiNq5WTjCc | |From NumPy to Pandas | 2020-02-05 | youtu.be/Ng_TzUentmk |

Module 1 Section 05 - Data Cleaning in Pandas

  • Pandas & Data
- fromnumpyto_pandas.ipynb - manipulating_data.ipynb - aggregation.ipynb
  • Data Exploration & Cleaning
- datacleaningwithpandas_overview.ipynb - exploring_data.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Brief Extra: Pandas & Loading Data | 2020-02-05 | youtu.be/-nr7bi7lVxQ | |Data Exploration with Pandas | 2020-02-11 | youtu.be/Wey_4uIGQ0 | |Data Exploration & Cleaning with Python | 2020-02-11 | youtu.be/KXNzYfWUoUM |

Module 1 Section 06 - Data Visualization

  • Data Visualization Intro
- motivation.ipynb - howtouse_visualizations.ipynb
  • Good & Bad Visualizations
- good_visualizations.ipynb - downwithpie_chart.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Why Should I Visualize Data? | 2020-02-11 | youtu.be/AjEdgBRbvUU | |Who Are Visualizations For? | 2020-02-11 | youtu.be/8t452nMFApc | |Visualizations: The Good, The Bad & The Ugly| 2020-02-12 | youtu.be/yvwyvCt8qAI | |Data Exploration Activity | 2020-02-12 | youtu.be/XPT6QgMbPos |

Module 1 Section 07 - SQL and Relational Databases

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

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |SQL & Realtional Databases Intro | 2020-02-18 | youtu.be/Ca-8RRZlLLo | |Running SQL in Python | 2020-02-18 | youtu.be/IjF3bNF-eHc | |More SQL & Joining Tables | 2020-02-18 | youtu.be/1PXDL-S71Cc | |Creating and Updating SQL Databases | 2020-02-18 | youtu.be/c8Gyv_LXH8o | |SQL & Execution Order | 2020-02-19 | youtu.be/NJEOpxZP9TI | |SQL Subqueries | 2020-02-19 | youtu.be/mAEgY7BGlN8 |

Module 1 Section 08: Other Database structures

Recordings

Module 1 Section 09: JSON and APIs

  • JSON
- jsonandxml_intro.ipynb
  • APIs
- apis.ipynb - lifx_example.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |JSON Data Format for Python | 2020-02-19 | youtu.be/EbCjd6OPdvg | |APIs with Python | 2020-02-19 | youtu.be/NsfITpjTqAA | |API Example with LIFX | 2020-02-19 | youtu.be/-zsoxAzkSLU |

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

  • HTML & CSS
- htmlcss_intro.ipynb
  • Web Scraping
- web_scraping.ipynb - webscrapingbeautifulsoupactivity_00.ipynb {IN PROGRESS}

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |HTML and CSS Intro for Web Scraping | 2020-02-26 | youtu.be/MadMEVGMTUE | |Intro & Ethics to Web Scraping | 2020-02-26 | youtu.be/ceH08GJlIOo | |Web Scraping with Python & Beautiful Soup| 2020-02-26|youtu.be/f6lj7xC0Y2g | |Web Scraping Demo: Adventure Time | 2020-02-26 | youtu.be/v_a1qUuXd1Y |

Module 1 Project: Movie Analysis

  • Project Details
- mod1projectnotes-pt_012120.ipynb
  • Advice
- general_advice.ipynb

Module 2

Module 2 Section 11 - Combinatorics and Probability

  • Conditional Probability
- probabilityand_notation.ipynb - conditional_probability.ipynb
  • Combinatorics
- combinatorics.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| |Conditional Probability | 2020-03-17 | youtu.be/JDgm4Wqsvuw | |Combinatorics | 2020-03-17 | youtu.be/hs5EFpUcTzw |

Module 2 Section 12 - Statistical Distributions

  • Statistical Distributions
- statisticaldistributions_intro.ipynb - statistical_distributions.ipynb - morestatistical_distributions.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | Frequency Distributions & More Statistics | 2020-03-19 | youtu.be/bNUpLoDgLig | | Review & Other Statistical Distributions | 2020-03-24 | youtu.be/YRor7gBV9Kw | | Even More Statistical Distributions | 2020-03-24 | youtu.be/dVSnNHKyeAM |

Module 2 Section 13 - Central Limit Theorem and Confidence Intervals

  • Central Limit Theorem
- sampling.ipynb - centrallimit_theorem.ipynb
  • Confidence Intervals
- confidence_intervals.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | Sampling | 2020-03-24 | youtu.be/x5KVX3ccbuc | | Central Limit Theorem | 2020-03-24 | youtu.be/c2NDqWrCBno | | Where Do Confidence Intervals Come From? | 2020-03-26 | youtu.be/jHLoLCCtumc |

Module 2 Section 14 - Hypothesis Testing

  • Experiment Design
- experimentdesign_intro.ipynb - hypothesistesting_intro.ipynb
  • Considerations
- warnings.ipynb - multiple_comparisons.ipynb
  • Statistical Tests
- statistical_tests.ipynb - typesof_errors.ipynb
  • t-Tests
- t_distributions.ipynb - t_tests.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | What Makes a Good Experiment? | 2020-03-26 | youtu.be/746no4_NvRM | | Hypothesis Testing Intro | 2020-03-26 | youtu.be/TE8C-PsZfrw | | Hypothesis Testing | 2020-03-31 | youtu.be/JnO5wKYnNfQ | | The t-Distribution & t-Test | 2020-03-31 | youtu.be/8zey4ICieg0 | | Type 1 vs Type 2 Errors | 2020-03-31 | youtu.be/1IybE0mXWl4 |

Module 2 Section 15 - Statistical Power & ANOVA

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

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| |Effect Size & Statistical Power Relationship | 2020-03-31 | youtu.be/0HtaoDgOF_A | |Welch's t-Test vs Student's t-Test | 2020-04-01| youtu.be/QNftsEYSwFA | |Multiple Comparisons Warning | 2020-04-07| youtu.be/voHPvSkX3f4 | |Introduction to ANOVA | 2020-04-07| youtu.be/y1UWYQHw5Jo | |Coding ANOVA: SciPy Method | 2020-04-07| youtu.be/QnE8sBrKoNU | |Coding ANOVA: Statsmodels OLS Method | 2020-04-07| youtu.be/3cCM0lQFMM4 |

Module 2 Section 16 - A/B Testing

  • A/B Testing
- ab_testing.ipynb - abtest_walkthrough.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | A/B Testing | 2020-04-07 | youtu.be/2DVXuR-2LeA |

Module 2 Section 17 - Bayesian Statistics

  • Bayes' Theorem
- bayes_theorem.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | Bayesian Thinking | 2020-04-21 | youtu.be/odZOxI3BNI

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| |Intro to Linear Regression | 2020-04-09 | youtu.be/PBv749p-9yY |

Module 2 Section 19 - Multiple Linear Regression

  • Multiple Linear Regression
- multiplelinear_regression.ipynb - multicollinearity.ipynb - model_validation.ipynb - linearregression_example.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| |Multiple Linear Regression | 2020-04-15| youtu.be/drbltsGcRNQ| |Handling Categorical Variables | 2020-04-15| youtu.be/57Cy58UnKv0| |Dealing with Multicollinearity | 2020-04-16| youtu.be/eGSG79vF6_E| |Validating Models & k-Fold Cross-Validation | 2020-04-16| youtu.be/nmIxCbv09G0|

Module 2 Section 20 - Extensions to Linear Regression

  • Polynomial & Interacting Terms
- improvinglinear_regression.ipynb

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | Extending Linear Regression: Polynomial & Interacting Terms | 2020-04-22 | youtu.be/QbkwZ9cCb8I

Module 3

Module 3 Section 17 - Combinatorics

- combinatorics.ipynb

Module 3 Section 18 - Statistical Distributions

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
- experimentdesign_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
- typesof_errors.ipynb

Module 3 Section 21 - Statistical Power & ANOVA

  • Statistical Power
- statistical_power.ipynb
  • Welch's T-Test
- welchst_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
- naivebayes_classification.ipynb

Module 3 Section 24 - Resampling and Monte Carlo Simulation

  • Data Generation
- data_generation.ipynb
  • Resampling
- resampling.ipynb
  • Monte Carlo
- monte_carlo.ipynb - ultimatehopscotch_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
- introtolinear_algebra
  • Math with Tensors
- mathwith_tensors.ipynb
  • Solving With Linear Algebra
- solvingwithlinear_algebra.ipynb

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

Derivatives - derivatives.ipynb

  • Gradient Descent
- gradient_descent.ipynb
  • Gradient Descent Walkthrough
- walkthroughgradient_descent.ipynb

Module 4 Section 28 - Extensions to Linear Models

  • Improving Linear Regression (Interactions & Polynomial)
- improvinglinear_regression.ipynb
  • Regularization
- regularization.ipynb
  • Bias & Variance
- biasand_variance.ipynb

Module 4 Section 29 - Introduction to Logistic Regression

  • Logistic Regression Intro
- logisticregression_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
- timeseries_intro.ipynb
  • Time Series Visualization
- timeseries_visualization.ipynb
  • Time Series Trends
- timeseries_trends.ipynb

Module 4 Section 32 - Time Series Modeling

  • Time Series Models Intro
- timeseriesmodels_basic.ipynb
  • ARMA Model
- timeseriesmodel_arma.ipynb

Module 5

Module 5 Section 33 - K Nearest Neighbors

  • Distance Metrics
- distance_metrics.ipynb
  • K Nearest Neighbors
- knearest_neighbors.ipynb

Module 5 Section 34 - Decision Trees

  • Decision Trees
- decisiontrees_intro.ipynb - informationtomake_decisions.ipynb - decisiontree_hyperparameters.ipynb - decisiontreecode_example.ipynb

Module 5 Section 35 - Ensemble Methods

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

Recordings

| Title | Date | URL | |----------------------------|------------|------------------------| | Ensemble Machine Learning: Bagging & Boosting | 2019-10-24 | youtu.be/xI-XdP2FLis

  • 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 - kmeans_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

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Machine Learning Pipelines | 2019-11-14 |youtu.be/SjeEM0r7RZo| |Grid Search of Hyperparameters | 2019-11-14 |youtu.be/oi2NjZPQcmQ|

Module 5 Section 40 - Big Data in PySpark

  • Big Data Introduction
- bigdata_intro.ipynb
  • Distributed Computing
- distributedparallel_computing.ipynb - toolsofdistributed_systems.ipynb
  • MapReduce
- map_reduce.ipynb - mapreduce_code.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Big Data & MapReduce | 2019-11-12 |youtu.be/LQVXvg1dL-8| |Intro to Identifying & Handling Big Data| 2019-08-15 |youtu.be/tRd_hVTxk24| |Intro to MapReduce | 2019-08-15 |youtu.be/2Amvm-BpCxg| |MapReduce Coding Example | 2019-08-15 |youtu.be/AwsWrryp6tY|

Module 5 Section 41 - Recommendation Systems

  • Recommendation Systems
- recommendationsystems_intro.ipynb
  • Neighbor Memory Based Collab Filtering
- neighbormemorybasedcollab_filtering.ipynb
  • Matrix Factorization
- matrix_factorization.ipynb

Recordings

| Title | Date | URL | |----------------------------------------|------------|------------------------| |Recommendation Systems Intro | 2019-11-15 | youtu.be/lIIAEVxRl50 | |Neighbor-Based Collaboraitve Filtering | 2019-11-15 | youtu.be/pEOPyOCaoHw | |Matrix Factorization & Embeddings | 2019-11-15 | youtu.be/olJKadbzdCQ | |Embeddings Discussion | 2019-11-15 | youtu.be/V_6S4xw0JnQ | |Recommendation Systems & Embeddings | 2019-09-18 | youtu.be/m1pj8hVnmn0 |

Module 6

Module 6 Section 42 - Graph Theory

  • Graph Theory
- graphtheory_basics.ipynb - paths.ipynb

Module 6 Section 43 - Foundations of Natural Language Processing

  • NLP Introduction
- introto_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

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