DAT4 Course Repository

Course materials for General Assembly's Data Science course in Washington, DC (12/15/14 - 3/16/15).

Instructors: Sinan Ozdemir and Kevin Markham (Data School blog, email newsletter, YouTube channel)

Teaching Assistant: Brandon Burroughs

Office hours: 1-3pm on Saturday and Sunday (Starbucks at 15th & K), 5:15-6:30pm on Monday (GA)

Course Project information

Monday	Wednesday
12/15: Introduction	12/17: Python
12/22: Getting Data	12/24: No Class
12/29: No Class	12/31: No Class
1/5: Git and GitHub	1/7: Pandas Milestone: Question and Data Set
1/12: Numpy, Machine Learning, KNN	1/14: scikit-learn, Model Evaluation Procedures
1/19: No Class	1/21: Linear Regression
1/26: Logistic Regression, Preview of Other Models	1/28: Model Evaluation Metrics Milestone: Data Exploration and Analysis Plan
2/2: Working a Data Problem	2/4: Clustering and Visualization Milestone: Deadline for Topic Changes
2/9: Naive Bayes	2/11: Natural Language Processing
2/16: No Class	2/18: Decision Trees Milestone: First Draft
2/23: Ensembling	2/25: Databases and MapReduce
3/2: Recommenders	3/4: Advanced scikit-learn Milestone: Second Draft (Optional)
3/9: Course Review	3/11: Project Presentations
3/16: Project Presentations

Installation and Setup

• Install the Anaconda distribution of Python 2.7x.
• Install Git and create a GitHub account.
• Once you receive an email invitation from Slack, join our "DAT4 team" and add your photo!

Class 1: Introduction

• Introduction to General Assembly
• Course overview: our philosophy and expectations (slides)
• Data science overview (slides)
• Tools: check for proper setup of Anaconda, overview of Slack

Homework:

• Resolve any installation issues before next class.

Optional:

• Review the code from Saturday's Python refresher for a recap of some Python basics.
• Read Analyzing the Analyzers for a useful look at the different types of data scientists.
• Subscribe to the Data Community DC newsletter or check out their event calendar to become acquainted with the local data community.

Class 2: Python

• Brief overview of Python environments: Python interpreter, IPython interpreter, Spyder
• Python quiz (solution)
• Working with data in Python
  • Obtain data from a public data source
  • FiveThirtyEight alcohol data, and revised data (continent column added)
  • Reading and writing files in Python (code)

Homework:

• Python exercise (solution)
• Read through the project page in detail.
• Review a few projects from past Data Science courses to get a sense of the variety and scope of student projects.
  • Check for proper setup of Git by running git clone https://github.com/justmarkham/DAT-project-examples.git

Optional:

• If you need more practice with Python, review the "Python Overview" section of A Crash Course in Python, work through some of Codecademy's Python course, or work through Google's Python Class and its exercises.
• For more project inspiration, browse the student projects from Andrew Ng's Machine Learning course at Stanford.

Resources:

• Online Python Tutor is useful for visualizing (and debugging) your code.

Class 3: Getting Data

• Checking your homework
• Regular expressions, web scraping, APIs (slides, regex code, web scraping and API code)
• Any questions about the course project?

Homework:

• Think about your project question, and start looking for data that will help you to answer your question.
• Prepare for our next class on Git and GitHub:
  • You'll need to know some command line basics, so please work through GA's excellent command line tutorial and then take this brief quiz.
  • Check for proper setup of Git by running git clone https://github.com/justmarkham/DAT-project-examples.git. If that doesn't work, you probably need to install Git.
  • Create a GitHub account. (You don't need to download anything from GitHub.)

Optional:

• If you aren't feeling comfortable with the Python we've done so far, keep practicing using the resources above!

Resources:

• regex101 is an excellent tool for testing your regular expressions. For learning more regular expressions, Google's Python Class includes an excellent regex lesson (which includes a video).
• Mashape and Apigee allow you to explore tons of different APIs. Alternatively, a Python API wrapper is available for many popular APIs.

Class 4: Git and GitHub

• Special guest: Nick DePrey presenting his class project from DAT2
• Git and GitHub (slides)

Homework:

• Project milestone: Submit your question and data set to your folder in DAT4-students before class on Wednesday! (This is a great opportunity to practice writing Markdown and creating a pull request.)

Optional:

• Clone this repo (DAT4) for easy access to the course files.

Resources:

• Read the first two chapters of Pro Git to gain a much deeper understanding of version control and basic Git commands.
• GitRef is an excellent reference guide for Git commands.
• Git quick reference for beginners is a shorter reference guide with commands grouped by workflow.
• The Markdown Cheatsheet covers standard Markdown and a bit of "GitHub Flavored Markdown."

Class 5: Pandas

• Pandas for data exploration, analysis, and visualization (code)
  • Split-Apply-Combine pattern
  • Simple examples of joins in Pandas

Homework:

• Pandas homework

Optional:

• To learn more Pandas, review this three-part tutorial, or review these three excellent (but extremely long) notebooks on Pandas: introduction, data wrangling, and plotting.

Resources:

• For more on Pandas plotting, read the visualization page from the official Pandas documentation.
• To learn how to customize your plots further, browse through this notebook on matplotlib.
• To explore different types of visualizations and when to use them, Choosing a Good Chart is a handy one-page reference, and Columbia's Data Mining class has an excellent slide deck.

Class 6: Numpy, Machine Learning, KNN

• Numpy (code)
• "Human learning" with iris data (code, solution)
• Machine Learning and K-Nearest Neighbors (slides)

Homework:

• Read this excellent article, Understanding the Bias-Variance Tradeoff, and be prepared to discuss it in class on Wednesday. (You can ignore sections 4.2 and 4.3.) Here are some questions to think about while you read:
  • In the Party Registration example, what are the features? What is the response? Is this a regression or classification problem?
  • In the interactive visualization, try using different values for K across different sets of training data. What value of K do you think is "best"? How do you define "best"?
  • In the visualization, what do the lighter colors versus the darker colors mean? How is the darkness calculated?
  • How does the choice of K affect model bias? How about variance?
  • As you experiment with K and generate new training data, how can you "see" high versus low variance? How can you "see" high versus low bias?
  • Why should we care about variance at all? Shouldn't we just minimize bias and ignore variance?
  • Does a high value for K cause over-fitting or under-fitting?

Resources:

• For a more in-depth look at 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!)

Class 7: scikit-learn, Model Evaluation Procedures

• Introduction to scikit-learn with iris data (code)
• Exploring the scikit-learn documentation: user guide, module reference, class documentation
• Discuss the article on the bias-variance tradeoff
• Model evaluation procedures (slides, code)

Homework:

• Keep working on your project. Your data exploration and analysis plan is due in two weeks!

Optional:

• Practice what we learned in class today!
  • If you have gathered your project data already: Try using KNN for classification, and then evaluate your model. Don't worry about using all of your features, just focus on getting the end-to-end process working in scikit-learn. (Even if your project is regression instead of classification, you can easily convert a regression problem into a classification problem by converting numerical ranges into categories.)
  • If you don't yet have your project data: Pick a suitable dataset from the UCI Machine Learning Repository, try using KNN for classification, and evaluate your model. The Glass Identification Data Set is a good one to start with.
  • Either way, you can submit your commented code to DAT4-students, and we'll give you feedback.

Resources:

• Here's a great 30-second explanation of overfitting.
• For more on today's topics, these videos from Hastie and Tibshirani are useful: overfitting and train/test split (14 minutes), cross-validation (14 minutes). (Note that they use the terminology "validation set" instead of "test set".)
  • Alternatively, read section 5.1 (12 pages) of An Introduction to Statistical Learning, which covers the same content as the videos.
• This video from Caltech's machine learning course presents an excellent, simple example of the bias-variance tradeoff (15 minutes) that may help you to visualize bias and variance.

Class 8: Linear Regression

• Linear regression (IPython notebook)

Homework:

• Keep working on your project. Your data exploration and analysis plan is due next Wednesday!

Optional:

• Similar to last class, your optional exercise is to practice what we have been learning in class, either on your project data or on another dataset.

Resources:

• To go much more in-depth on linear regression, read Chapter 3 of An Introduction to Statistical Learning, from which this lesson was adapted. Alternatively, watch the related videos or read my quick reference guide to the key points in that chapter.
• To learn more about Statsmodels and how to interpret the output, DataRobot has some decent posts on simple linear regression and multiple linear regression.
• This introduction to linear regression is much more detailed and mathematically thorough, and includes lots of good advice.
• This is a relatively quick post on the assumptions of linear regression.

Class 9: Logistic Regression, Preview of Other Models

• Logistic regression (slides, exercise, solution)
• Preview of other models

Resources:

• For more on logistic regression, watch the first three videos (30 minutes total) from Chapter 4 of An Introduction to Statistical Learning.
• UCLA's IDRE has a handy table to help you remember the relationship between probability, odds, and log-odds.
• Better Explained has a very friendly introduction (with lots of examples) to the intuition behind "e".
• Here are some useful lecture notes on interpreting logistic regression coefficients.

Class 10: Model Evaluation Metrics

• Finishing model evaluation procedures (slides, code)
  • Review of test set approach
  • Cross-validation
• Model evaluation metrics (slides)
  • Regression:
    • Root Mean Squared Error (code)
  • Classification:
    • Confusion matrix (code)
    • ROC curve (video)

Homework:

• Model evaluation homework, due by midnight on Sunday.
  • Sample solution code.
• Watch Kevin's Kaggle project presentation video (16 minutes) for an overview of the end-to-end machine learning process, including some aspects that we have not yet covered in class.
• Read this short article on Google's Smart Autofill, and see if you can figure out exactly how the system works.

Optional:

• For more on Kaggle, watch Kaggle Transforms Data Science Into Competitive Sport (28 minutes).

Resources:

• scikit-learn has extensive documentation on model evaluation.
• The Kaggle wiki has a decent page describing other common model evaluation metrics.
• Kevin wrote a simple guide to confusion matrix terminology that you can use as a reference guide.
• Kevin's blog post about the ROC video includes the complete transcript and screenshots, in case you learn better by reading instead of watching.
• Rahul Patwari has two excellent and highly accessible videos on Sensitivity and Specificity (9 minutes) and ROC Curves (12 minutes).

Class 11: Working a Data Problem

• Today we will work on a real world data problem! Our data is stock data over 7 months of a fictional company ZYX including twitter sentiment, volume and stock price. Our goal is to create a predictive model that predicts forward returns.

• Project overview (slides)

  • Be sure to read documentation thoroughly and ask questions! We may not have included all of the information you need...

Class 12: Clustering and Visualization

• The slides today will focus on our first look at unsupervised learning, K-Means Clustering!
• The code for today focuses on two main examples:
  • We will investigate simple clustering using the iris data set.
  • We will take a look at a harder example, using Pandora songs as data. See data.

Homework:

• Read Paul Graham's A Plan for Spam and be prepared to discuss it in class on Monday. Here are some questions to think about while you read:
  • Should a spam filter optimize for sensitivity or specificity, in Paul's opinion?
  • Before he tried the "statistical approach" to spam filtering, what was his approach?
  • How exactly does his statistical filtering system work?
  • What did Paul say were some of the benefits of the statistical approach?
  • How good was his prediction of the "spam of the future"?
• Below are the foundational topics upon which Monday's class will depend. Please review these materials before class:
  • Confusion matrix: Kevin's guide roughly mirrors the lecture from class 10.
  • Sensitivity and specificity: Rahul Patwari has an excellent video (9 minutes).
  • Basics of probability: These introductory slides (from the OpenIntro Statistics textbook) are quite good and include integrated quizzes. Pay specific attention to these terms: probability, sample space, mutually exclusive, independent.
• You should definitely be working on your project! Your rough draft is due in two weeks!

Resources:

• Introduction to Data Mining has a nice chapter on cluster analysis.
• The scikit-learn user guide has a nice section on clustering.

Class 13: Naive Bayes

• Briefly discuss A Plan for Spam
• Probability and Bayes' theorem
  • Slides part 1
  • Visualization of conditional probability
  • Applying Bayes' theorem to iris classification (code)
• Naive Bayes classification
  • Slides part 2
  • Example with spam email
  • Airport security example
• Naive Bayes classification in scikit-learn (code)
  • Data set: SMS Spam Collection
  • scikit-learn documentation: CountVectorizer, Naive Bayes

Resources:

• The first part of the slides was adapted from Visualizing Bayes' theorem, which includes an additional example (using Venn diagrams) of how this applies to testing for breast cancer.
• For an alternative introduction to Bayes' Theorem, Bayes' Rule for Ducks, this 5-minute video on conditional probability, or these slides on conditional probability may be helpful.
• For more details on Naive Bayes classification, Wikipedia has two useful articles (Naive Bayes classifier and Naive Bayes spam filtering), and Cross Validated has an excellent Q&A.
• 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.

Homework:

• Download all of the NLTK collections.
  • In Python, use the following commands to bring up the download menu.
  • import nltk
  • nltk.download()
  • Choose "all".
  • Alternatively, just type nltk.download('all')
• Install two new packages: textblob and lda.
  • Open a terminal or command prompt.
  • Type pip install textblob and pip install lda.

Class 14: Natural Language Processing

• Overview of Natural Language Processing (slides)
• Real World Examples
• Natural Language Processing (code)
• NLTK: tokenization, stemming, lemmatization, part of speech tagging, stopwords, Named Entity Recognition (Stanford NER Tagger), TF-IDF, LDA, document summarization
• Alternative: TextBlob

Resources:

• Natural Language Processing with Python: free online book to go in-depth with NLTK
• NLP online course: no sessions are available, but video lectures and slides are still accessible
• Brief slides on the major task areas of NLP
• Detailed slides on a lot of NLP terminology
• A visual survey of text visualization techniques: for exploration and inspiration
• DC Natural Language Processing: active Meetup group
• Stanford CoreNLP: suite of tools if you want to get serious about NLP
• Getting started with regex: Python introductory lesson and reference guide, real-time regex tester, in-depth tutorials
• SpaCy: a new NLP package

Class 15: Decision Trees

• Decision trees (IPython notebook)

Homework:

• By next Wednesday (before class), review the project drafts of your two assigned peers according to these guidelines. You should upload your feedback as a Markdown (or plain text) document to the "reviews" folder of DAT4-students. If your last name is Smith and you are reviewing Jones, you should name your file smith_reviews_jones.md.

Resources:

• scikit-learn documentation: Decision Trees

Installing Graphviz (optional):

• Mac:
  • Download and install PKG file
• Windows:
  • Download and install MSI file
  • Add it 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

Class 16: Ensembling

• Ensembling (IPython notebook)

Resources:

• scikit-learn documentation: Ensemble Methods
• Quora: How do random forests work in layman's terms?

Class 17: Databases and MapReduce

• Learn the basics of databases database code
• MapReduce basics slides
• MapReduce example in python code

Resources:

• Forbes: Is it Time for Hadoop Alternatives?
• IBM: What is MapReduce?
• Wakari MapReduce IPython notebook
• What Every Data Scientist Needs to Know about SQL
• Brandon's SQL Bootcamp
• SQL tutorials from SQLZOO and Mode Analytics

Class 18: Recommenders

• Recommendation Engines slides
• Recommendation Engine Example code

Resources:

• The Netflix Prize
• Why Netflix never implemented the winning solution
• Visualization of the Music Genome Project
• 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).

Class 19: Advanced scikit-learn

• Advanced scikit-learn (code)
  • Searching for optimal parameters: GridSearchCV
  • Standardization of features: StandardScaler
  • Chaining steps: Pipeline
  • Regularized regression (notebook): Ridge, RidgeCV, Lasso, LassoCV
  • Regularized classification: LogisticRegression
  • Feature selection: RFE, RFECV

Homework:

• 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.

Resources:

• Here is a longer example of feature scaling in scikit-learn, with additional discussion of the types of scaling you can use.
• Clever Methods of Overfitting is a classic post by John Langford.
• Common Pitfalls in Machine Learning is similar to Langford's post, but broader and a bit more readable.

Class 20: Course Review

• Data science review
• Comparing supervised learning algorithms

Resources:

• Choosing a Machine Learning Classifier: Edwin Chen's short and highly readable guide.
• scikit-learn "machine learning map": Their guide for choosing the "right" estimator for your task.
• Machine Learning Done Wrong: Thoughtful advice on common mistakes to avoid in machine learning.
• Practical machine learning tricks from the KDD 2011 best industry paper: More advanced advice than the resources above.
• An Empirical Comparison of Supervised Learning Algorithms: Research paper from 2006.
• Getting in Shape for the Sport of Data Science: 75-minute video of practical tips for machine learning (by the past president of Kaggle).
• Resources for continued learning!

Class 21: Project Presentations

Class 22: Project Presentations