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General Assembly's Excellent Data Science Course by Kevin Markham

DAT8 Course Repository

Course materials for General Assembly’s Data Science course in Washington, DC (8/18/15 – 10/29/15).

Instructor:Kevin Markham ( Data School blog , email newsletter , YouTube channel )

General Assembly's Excellent Data Science Course by Kevin Markham

Tuesday Thursday
8/18:Introduction to Data Science 8/20:Command Line, Version Control
8/25:Data Reading and Cleaning 8/27:Exploratory Data Analysis
9/1:Visualization 9/3:Machine Learning
9/8:Getting Data 9/10:K-Nearest Neighbors
9/15:Basic Model Evaluation 9/17:Linear Regression
9/22:First Project Presentation 9/24:Logistic Regression
9/29:Advanced Model Evaluation 10/1:Naive Bayes and Text Data
10/6:Natural Language Processing 10/8:Kaggle Competition
10/13:Decision Trees 10/15:Ensembling
10/20:Advanced scikit-learn, Clustering 10/22:Regularization, Regex
10/27:Course Review 10/29:Final Project Presentation

Python Resources

Course project

Comparison of machine learning models

Comparison of model evaluation procedures and metrics

Advice for getting better at data science

Additional resources

Class 1: Introduction to Data Science

  • Course overview (slides)
  • Introduction to data science (slides)
  • Discuss the course project:requirements andexample projects
  • Types of data (slides) andpublic data sources
  • Welcome from General Assembly staff

Homework:

  • Work through GA’s friendly command line tutorial using Terminal (Linux/Mac) or Git Bash (Windows).
  • Read through this command line reference , and complete the pre-class exercise at the bottom. (There’s nothing you need to submit once you’re done.)
  • Watch videos 1 through 8 (21 minutes) of Introduction to Git and GitHub , or read sections 1.1 through 2.2 of Pro Git .
  • If your laptop has any setup issues, please work with us to resolve them by Thursday. If your laptop has not yet been checked, you should come early on Thursday, or just walk through thesetup checklist yourself (and let us know you have done so).

Resources:

Class 2: Command Line and Version Control

  • Slack tour
  • Review the command line pre-class exercise (code)
  • Git and GitHub (slides)
  • Intermediate command line

Homework:

  • Complete the command line homework assignment with the Chipotle data.
  • Review the code from thebeginner andintermediate Python workshops. If you don’t feel comfortable with any of the content (excluding the "requests" and "APIs" sections), you should spend some time this weekend practicing Python:
    • Introduction to Python does a great job explaining Python essentials and includes tons of example code.
    • If you like learning from a book, Python for Informatics has useful chapters on strings, lists, and dictionaries.
    • If you prefer interactive exercises, try these lessons from Codecademy : "Python Lists and Dictionaries" and "A Day at the Supermarket".
    • If you have more time, try missions 2 and 3 from DataQuest’s Learning Python course.
    • If you’ve already mastered these topics and want more of a challenge, try solving Python Challenge number 1 (decoding a message) and send me your code in Slack.
  • To give you a framework for thinking about your project, watch What is machine learning, and how does it work? (10 minutes). (This is theIPython notebook shown in the video.) Alternatively, read A Visual Introduction to Machine Learning , which focuses on a specific machine learning model called decision trees.
  • Optional: Browse through some more example student projects , which may help to inspire your own project!

Git and Markdown Resources:

  • Pro Git is an excellent book for learning Git. Read the first two chapters to gain a deeper understanding of version control and basic commands.
  • If you want to practice a lot of Git (and learn many more commands), Git Immersion looks promising.
  • If you want to understand how to contribute on GitHub, you first have to understand forks and pull requests .
  • GitRef is my favorite reference guide for Git commands, and Git quick reference for beginners is a shorter guide with commands grouped by workflow.
  • Cracking the Code to GitHub’s Growth explains why GitHub is so popular among developers.
  • Markdown Cheatsheet provides a thorough set of Markdown examples with concise explanations. GitHub’sMastering Markdown is a simpler and more attractive guide, but is less comprehensive.

Command Line Resources:

  • If you want to go much deeper into the command line, Data Science at the Command Line is a great book. The companion website provides installation instructions for a "data science toolbox" (a virtual machine with many more command line tools), as well as a long reference guide to popular command line tools.
  • If you want to do more at the command line with CSV files, try out csvkit , which can be installed via pip .

Class 3: Data Reading and Cleaning

  • Git and GitHub assorted tips (slides)
  • Review command line homework (solution)
  • Python:
    • Spyder interface
    • Looping exercise
    • Lesson on file reading with airline safety data (code, data , article )
    • Data cleaning exercise
    • Walkthrough of Python homework with Chipotle data (code, data , article )

Homework:

  • Complete the Python homework assignment with the Chipotle data, add a commented Python script to your GitHub repo, and submit a link using the homework submission form. You have until Tuesday (9/1) to complete this assignment. ( Note: Pandas, which is covered in class 4, should not be used for this assignment.)

Resources:

Class 4: Exploratory Data Analysis

  • Pandas (code):
  • Project question exercise

Homework:

Resources:

Class 5: Visualization

  • Python homework with the Chipotle data due (solution, detailed explanation )
  • Part 2 of Exploratory Data Analysis with Pandas (code)
  • Visualization with Pandas and Matplotlib (notebook)

Homework:

  • Your project question write-up is due on Thursday.
  • Complete the Pandas homework assignment with theIMDb data. You have until Tuesday (9/8) to complete this assignment.
  • If you’re not using Anaconda, install the Jupyter Notebook (formerly known as the IPython Notebook) using pip . (The Jupyter or IPython Notebook is included with Anaconda.)

Pandas Resources:

  • To learn more Pandas, read this three-part tutorial , or review these two excellent (but extremely long) notebooks on Pandas:introduction anddata wrangling.
  • If you want to go really deep into Pandas (and NumPy), read the book Python for Data Analysis , written by the creator of Pandas.
  • This notebook demonstrates the different types ofjoins in Pandas, for when you need to figure out how to merge two DataFrames.
  • This is a nice, short tutorial on pivot tables in Pandas.
  • For working with geospatial data in Python, GeoPandas looks promising. This tutorial uses GeoPandas (and scikit-learn) to build a "linguistic street map" of Singapore.

Visualization Resources:

Class 6: Machine Learning

  • Part 2 of Visualization with Pandas and Matplotlib (notebook)
  • Brief introduction to the Jupyter/IPython Notebook
  • "Human learning" exercise:
  • Introduction to machine learning (slides)

Homework:

  • Optional: Complete the bonus exercise listed in the human learning notebook . It will take the place of any one homework you miss, past or future! This is due on Tuesday (9/8).
  • If you’re not using Anaconda, install requests and Beautiful Soup 4 using pip . (Both of these packages are included with Anaconda.)

Machine Learning Resources:

IPython Notebook Resources:

  • For a recap of the IPython Notebook introduction (and a preview of scikit-learn), watch scikit-learn and the IPython Notebook (15 minutes) or read theassociated notebook.
  • If you would like to learn the IPython Notebook, the officialNotebook tutorials are useful.
  • This Reddit discussion compares the relative strengths of the IPython Notebook and Spyder.

Class 7: Getting Data

Homework:

  • Optional: Complete the homework exercise listed in theweb scraping code. It will take the place of any one homework you miss, past or future! This is due on Tuesday (9/15).
  • Optional: If you’re not using Anaconda, install Seaborn using pip . If you’re using Anaconda, install Seaborn by running conda install seaborn at the command line. (Note that some students in past courses have had problems with Anaconda after installing Seaborn.)

API Resources:

  • This Python script to query the U.S. Census API was created by a former DAT student. It’s a bit more complicated than the example we used in class, it’s very well commented, and it may provide a useful framework for writing your own code to query APIs.
  • Mashape and Apigee allow you to explore tons of different APIs. Alternatively, a Python API wrapper is available for many popular APIs.
  • The Data Science Toolkit is a collection of location-based and text-related APIs.
  • API Integration in Python provides a very readable introduction to REST APIs.
  • Microsoft’s Face Detection API , which powers How-Old.net , is a great example of how a machine learning API can be leveraged to produce a compelling web application.

Web Scraping Resources:

Class 8: K-Nearest Neighbors

  • Brief review of Pandas (notebook)
  • K-nearest neighbors and scikit-learn (notebook)
  • Exercise with NBA player data (notebook, data , data dictionary )
  • Exploring the bias-variance tradeoff (notebook)

Homework:

KNN Resources:

Seaborn Resources:

Class 9: Basic Model Evaluation

Homework:

Model Evaluation Resources:

Reproducibility Resources:

Class 10: Linear Regression

Homework:

  • Your first project presentation is on Tuesday (9/22)! Please submit a link to your project repository (with slides, code, data, and visualizations) by 6pm on Tuesday.
  • Complete thehomework assignment with theYelp data. This is due on Thursday (9/24).

Linear Regression Resources:

Other Resources:

Class 11: First Project Presentation

  • Project presentations!

Homework:

Class 12: Logistic Regression

Homework:

Logistic Regression Resources:

  • To go deeper into logistic regression, read the first three sections of Chapter 4 of An Introduction to Statistical Learning , or watch the first three videos (30 minutes) from that chapter.
  • For a math-ier explanation of logistic regression, watch the first seven videos (71 minutes) from week 3 of Andrew Ng’s machine learning course , or read the related lecture notes compiled by a student.
  • For more on interpreting logistic regression coefficients, read this excellent guide by UCLA’s IDRE and these lecture notes from the University of New Mexico.
  • The scikit-learn documentation has a nice explanation of what it means for a predicted probability to be calibrated.
  • Supervised learning superstitions cheat sheet is a very nice comparison of four classifiers we cover in the course (logistic regression, decision trees, KNN, Naive Bayes) and one classifier we do not cover (Support Vector Machines).

Confusion Matrix Resources:

Class 13: Advanced Model Evaluation

  • Data preparation (notebook)
    • Handling missing values
    • Handling categorical features (review)
  • ROC curves and AUC
  • Cross-validation
  • Exercise with bank marketing data (notebook, data , data dictionary )

Homework:

ROC Resources:

Cross-Validation Resources:

Other Resources:

Class 14: Naive Bayes and Text Data

  • Conditional probability and Bayes’ theorem
  • Naive Bayes classification
    • Slides
    • Spam filtering example (notebook)
  • Applying Naive Bayes to text data in scikit-learn (notebook)

Homework:

  • Complete anotherhomework assignment with theYelp data. This is due on Tuesday (10/6).
  • Confirm that you have TextBlob installed by running import textblob from within your preferred Python environment. If it’s not installed, run pip install textblob at the command line (not from within Python).

Resources:

  • Sebastian Raschka’s article on Naive Bayes and Text Classification covers the conceptual material from today’s class in much more detail.
  • For more on conditional probability, read these slides , or read section 2.2 of the OpenIntro Statistics textbook (15 pages).
  • For an intuitive explanation of Naive Bayes classification, read this post on airport security .
  • For more details on Naive Bayes classification, Wikipedia has two excellent articles ( Naive Bayes classifier and Naive Bayes spam filtering ), and Cross Validated has a good Q&A .
  • When applying Naive Bayes classification to a dataset with continuous features, it is better to use GaussianNB rather than MultinomialNB . Thisnotebook compares their performances on such a dataset. Wikipedia has a short description of Gaussian Naive Bayes, as well as an excellent example of its usage.
  • These slides from the University of Maryland provide more mathematical details on both logistic regression and Naive Bayes, and also explain how Naive Bayes is actually a "special case" of logistic regression.
  • Andrew Ng has a paper comparing the performance of logistic regression and Naive Bayes across a variety of datasets.
  • If you enjoyed Paul Graham’s article, you can read his follow-up article on how he improved his spam filter and this related paper about state-of-the-art spam filtering in 2004.
  • Yelp has found that Naive Bayes is more effective than Mechanical Turks at categorizing businesses .

Class 15: Natural Language Processing

  • Yelp review text homework due (solution)
  • Natural language processing (notebook)
  • Introduction to our Kaggle competition
    • Create a Kaggle account, join the competition using the invitation link, download the sample submission, and then submit the sample submission (which will require SMS account verification).

Homework:

  • Your draft paper is due on Thursday (10/8)! Please submit a link to your project repository (with paper, code, data, and visualizations) before class.
  • Watch Kaggle: How it Works (4 minutes) for a brief overview of the Kaggle platform.
  • Download the competition files, move them to the DAT8/data directory, and make sure you can open the CSV files using Pandas. If you have any problems opening the files, you probably need to turn off real-time virus scanning (especially Microsoft Security Essentials).
  • Optional: Come up with some theories about which features might be relevant to predicting the response, and then explore the data to see if those theories appear to be true.
  • Optional: Watch my project presentation video (16 minutes) for a tour of the end-to-end machine learning process for a Kaggle competition, including feature engineering. (Or, just read through the slides .)

NLP Resources:

Class 16: Kaggle Competition

Homework:

  • You will be assigned to review the project drafts of two of your peers. You have until Tuesday 10/20 to provide them with feedback, according to the peer review guidelines .
  • Read A Visual Introduction to Machine Learning for a brief overview of decision trees.
  • Download and install Graphviz , which will allow you to visualize decision trees in scikit-learn.
    • Windows users should also add Graphviz to your path: Go to Control Panel, System, Advanced System Settings, Environment Variables. Under system variables, edit "Path" to include the path to the "bin" folder, such as: C:/Program Files (x86)/Graphviz2.38/bin
  • Optional: Keep working on our Kaggle competition! You can make up to 5 submissions per day, and the competition doesn’t close until 6:30pm ET on Tuesday 10/27 (class 21).

Resources:

Class 17: Decision Trees

  • Decision trees (notebook)
  • Exercise with Capital Bikeshare data (notebook, data , data dictionary )

Homework:

Resources:

  • scikit-learn’s documentation on decision trees includes a nice overview of trees as well as tips for proper usage.
  • For a more thorough introduction to decision trees, read section 4.3 (23 pages) of Introduction to Data Mining . (Chapter 4 is available as a free download.)
  • If you want to go deep into the different decision tree algorithms, this slide deck contains A Brief History of Classification and Regression Trees .
  • The Science of Singing Along contains a neat regression tree (page 136) for predicting the percentage of an audience at a music venue that will sing along to a pop song.
  • Decision trees are common in the medical field for differential diagnosis, such as this classification tree for identifying psychosis .

Class 18: Ensembling

Resources:

Class 19: Advanced scikit-learn and Clustering

Homework:

scikit-learn Resources:

  • This is a longer example offeature scaling in scikit-learn, with additional discussion of the types of scaling you can use.
  • Practical Data Science in Python is a long and well-written notebook that uses a few advanced scikit-learn features: pipelining, plotting a learning curve, and pickling a model.
  • To learn how to use GridSearchCV and RandomizedSearchCV for parameter tuning, watch How to find the best model parameters in scikit-learn (28 minutes) or read theassociated notebook.
  • Sebastian Raschka has a number of excellent resources for scikit-learn users, including a repository of tutorials and examples , a library of machine learning tools and extensions , a newbook, and a semi-active blog .
  • scikit-learn has an incredibly active mailing list that is often much more useful than Stack Overflow for researching functions and asking questions.
  • If you forget how to use a particular scikit-learn function that we have used in class, don’t forget that this repository is fully searchable!

Clustering Resources:

Class 20: Regularization and Regular Expressions

Homework:

  • Your final project is due next week!
  • Optional: Make your final submissions to our Kaggle competition! It closes at 6:30pm ET on Tuesday 10/27.
  • Optional: Read this classic paper, which may help you to connect many of the topics we have studied throughout the course: A Few Useful Things to Know about Machine Learning .

Regularization Resources:

Regular Expressions Resources:

Class 21: Course Review and Final Project Presentation

Resources:

Class 22: Final Project Presentation

Additional Resources

Tidy Data

Databases and SQL

  • ThisGA slide deck provides a brief introduction to databases and SQL. ThePython script from that lesson demonstrates basic SQL queries, as well as how to connect to a SQLite database from Python and how to query it using Pandas.
  • The repository for thisSQL Bootcamp contains an extremely well-commented SQL script that is suitable for walking through on your own.
  • ThisGA notebook provides a shorter introduction to databases and SQL that helpfully contrasts SQL queries with Pandas syntax.
  • SQLZOO , Mode Analytics , Khan Academy , Codecademy , Datamonkey , and Code School all have online beginner SQL tutorials that look promising. Code School also offers an advanced tutorial , though it’s not free.
  • w3schools has a sample database that allows you to practice SQL from your browser. Similarly, Kaggle allows you to query a large SQLite database of Reddit Comments using their online "Scripts" application.
  • What Every Data Scientist Needs to Know about SQL is a brief series of posts about SQL basics, and Introduction to SQL for Data Scientists is a paper with similar goals.
  • 10 Easy Steps to a Complete Understanding of SQL is a good article for those who have some SQL experience and want to understand it at a deeper level.
  • SQLite’s article on Query Planning explains how SQL queries "work".
  • A Comparison Of Relational Database Management Systems gives the pros and cons of SQLite, MySQL, and PostgreSQL.
  • If you want to go deeper into databases and SQL, Stanford has a well-respected series of 14 mini-courses .
  • Blaze is a Python package enabling you to use Pandas-like syntax to query data living in a variety of data storage systems.

Recommendation Systems

  • ThisGA slide deck provides a brief introduction to recommendation systems, and thePython script from that lesson demonstrates how to build a simple recommender.
  • Chapter 9 of Mining of Massive Datasets (36 pages) is a more thorough introduction to recommendation systems.
  • Chapters 2 through 4 of A Programmer’s Guide to Data Mining (165 pages) provides a friendlier introduction, with lots of Python code and exercises.
  • The Netflix Prize was the famous competition for improving Netflix’s recommendation system by 10%. Here are some useful articles about the Netflix Prize:
  • This paper summarizes how Amazon.com’s recommendation system works, and this Stack Overflow Q&A has some additional thoughts.
  • Facebook and Etsy have blog posts about how their recommendation systems work.
  • The Global Network of Discovery provides some neat recommenders for music, authors, and movies.
  • The People Inside Your Machine (23 minutes) is a Planet Money podcast episode about how Amazon Mechanical Turks can assist with recommendation engines (and machine learning in general).
  • Coursera has a course on recommendation systems, if you want to go even deeper into the material.

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