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)
Class 1: Introduction to Data Science
- 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 the setup checklist yourself (and let us know you have done so).
Class 2: Command Line and Version Control
- Slack tour
- Review the command line pre-class exercise (code)
- Git and GitHub (slides)
- Intermediate command line
- Complete the command line homework assignment with the Chipotle data.
- Review the code from the beginner and intermediate 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 the IPython 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's Mastering 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
Class 3: Data Reading and Cleaning
- Git and GitHub assorted tips (slides)
- Review command line homework (solution)
- 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)
- 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.)
Class 4: Exploratory Data Analysis
- Pandas (code):
- Project question exercise
Class 5: Visualization
- Your project question write-up is due on Thursday.
- Complete the Pandas homework assignment with the IMDb 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.)
- To learn more Pandas, read this three-part tutorial, or review these two excellent (but extremely long) notebooks on Pandas: introduction and data 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 of joins 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.
- Watch Look at Your Data (18 minutes) for an excellent example of why visualization is useful for understanding your data.
- For more on Pandas plotting, read this notebook or the visualization page from the official Pandas documentation.
- To learn how to customize your plots further, browse through this notebook on matplotlib or this similar notebook.
- Read Overview of Python Visualization Tools for a useful comparison of Matplotlib, Pandas, Seaborn, ggplot, Bokeh, Pygal, and Plotly.
- To explore different types of visualizations and when to use them, Choosing a Good Chart and The Graphic Continuum are nice one-page references, and the interactive R Graph Catalog has handy filtering capabilities.
- This PowerPoint presentation from Columbia's Data Mining class contains lots of good advice for properly using different types of visualizations.
Harvard's Data Science course includes an excellent lecture on Visualization Goals, Data Types, and Statistical Graphs (83 minutes), for which the slides are also available.
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)
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:
- For a very quick summary of the key points about machine learning, watch What is machine learning, and how does it work? (10 minutes) or read the associated notebook.
- For a more in-depth introduction to machine learning, read section 2.1 (14 pages) of Hastie and Tibshirani's excellent book, An Introduction to Statistical Learning. (It's a free PDF download!)
- The Learning Paradigms video (13 minutes) from Caltech's Learning From Data course provides a nice comparison of supervised versus unsupervised learning, as well as an introduction to "reinforcement learning".
Real-World Active Learning is a readable and thorough introduction to "active learning", a variation of machine learning in which humans label only the most "important" observations.
- For a preview of some of the machine learning content we will cover during the course, read Sebastian Raschka's overview of the supervised learning process.
Data Science, Machine Learning, and Statistics: What is in a Name? discusses the differences between these (and other) terms.
The Emoji Translation Project is a really fun application of machine learning.
- Look up the characteristics of your zip code, and then read about the 67 distinct segments in detail.
IPython Notebook Resources:
Class 7: Getting Data
- Pandas homework with the IMDb data due (solution)
- Optional "human learning" exercise with the iris data due (solution)
- APIs (code)
- Web scraping (code)
Optional: Complete the homework exercise listed in the web 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.)
- 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:
- The Beautiful Soup documentation is incredibly thorough, but is hard to use as a reference guide. However, the section on specifying a parser may be helpful if Beautiful Soup appears to be parsing a page incorrectly.
- For more Beautiful Soup examples and tutorials, see Web Scraping 101 with Python, a former DAT student's well-commented notebook on scraping Craigslist, this notebook from Stanford's Text As Data course, and this notebook and associated video from Harvard's Data Science course.
- For a much longer web scraping tutorial covering Beautiful Soup, lxml, XPath, and Selenium, watch Web Scraping with Python (3 hours 23 minutes) from PyCon 2014. The slides and code are also available.
- For more complex web scraping projects, Scrapy is a popular application framework that works with Python. It has excellent documentation, and here's a tutorial with detailed slides and code.
robotstxt.org has a concise explanation of how to write (and read) the
import.io and Kimono claim to allow you to scrape websites without writing any code.
How a Math Genius Hacked OkCupid to Find True Love and How Netflix Reverse Engineered Hollywood are two fun examples of how web scraping has been used to build interesting datasets.
Class 8: K-Nearest Neighbors
Class 9: Basic Model Evaluation
Model Evaluation Resources:
Class 10: Linear Regression
- 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 the homework assignment with the Yelp data. This is due on Thursday (9/24).
Linear Regression Resources:
- To go much more in-depth on linear regression, read Chapter 3 of An Introduction to Statistical Learning. Alternatively, watch the related videos or read my quick reference guide to the key points in that chapter.
- This introduction to linear regression is more detailed and mathematically thorough, and includes lots of good advice.
- This is a relatively quick post on the assumptions of linear regression.
- Setosa has an interactive visualization of linear regression.
- For a brief introduction to confidence intervals, hypothesis testing, p-values, and R-squared, as well as a comparison between scikit-learn code and Statsmodels code, read my DAT7 lesson on linear regression.
- Here is a useful explanation of confidence intervals from Quora.
Hypothesis Testing: The Basics provides a nice overview of the topic, and John Rauser's talk on Statistics Without the Agonizing Pain (12 minutes) gives a great explanation of how the null hypothesis is rejected.
- Earlier this year, a major scientific journal banned the use of p-values:
- Scientific American has a nice summary of the ban.
- This response to the ban in Nature argues that "decisions that are made earlier in data analysis have a much greater impact on results".
- Andrew Gelman has a readable paper in which he argues that "it's easy to find a p < .05 comparison even if nothing is going on, if you look hard enough".
Science Isn't Broken includes a neat tool that allows you to "p-hack" your way to "statistically significant" results.
Accurately Measuring Model Prediction Error compares adjusted R-squared, AIC and BIC, train/test split, and cross-validation.
Class 11: First Project Presentation
Class 12: Logistic Regression
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
- Exercise with bank marketing data (notebook, data, data dictionary)
Class 14: Naive Bayes and Text Data
- Conditional probability and Bayes' theorem
- Naive Bayes classification
- Applying Naive Bayes to text data in scikit-learn (notebook)
- Complete another homework assignment with the Yelp 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).
- 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. This notebook 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).
- 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.)
Class 16: Kaggle Competition
- 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).
Class 17: Decision Trees
- 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
Class 19: Advanced scikit-learn and Clustering
Class 20: Regularization and Regular Expressions
- Regularization (notebook)
- Regular expressions
- 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.
Regular Expressions Resources:
Class 21: Course Review and Final Project Presentation
Class 22: Final Project Presentation
Databases and SQL
- This GA slide deck provides a brief introduction to databases and SQL. The Python 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 this SQL Bootcamp contains an extremely well-commented SQL script that is suitable for walking through on your own.
- This GA 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.
- This GA slide deck provides a brief introduction to recommendation systems, and the Python 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.