marcharper / DAT-23-NYC

GA Data Science NYC #23

GA Data Science NYC #23

Team

• Daniel Demoray, producer
• Ruben Naeff, instructor (owner repo)
• Antoine Grant, expert in residence
• egroup & Slack channel, Google Drive folder, everyone

Please do not hesitate to contact any of us!

Logisitics

• June 25 - September 10, 2015
• Tuesdays and Thursdays 6.30-9.30
• GA West, 10 East 21st St, room 4A (4th floor)
• Office hours: TBD

Please fill out an exit ticket after each class

Please see Cloning the repo for how to download the latest course notes to your laptop.

Resources

• Calendar
• Final Project
• Guest speakers
• All datasets
• Further Reading

I. Data Exploration (Analytics)

• 01: INTRODUCTION TO DATA SCIENCE

  • Slides
  • Setting Up Your Environment
  • Data Science at the Command Line including exercises

• 02: DATABASES, SQL, PYTHON

  • Slides
  • SQL Exercises
  • Python Exercises
  • Data Exploration in Python including exercises

• 03: PYTHON, PANDAS

  • Slides
  • Pandas Exercises

• 04: VISUALIZATIONS AND MORE DATA GATHERING

  • Slides
  • Web scraping optional demo
  • Twitter API optional demo
  • How To Present Your Insights
  • Visualizations including exercises
  • Anscombe's Quartet illustrating the need for visualizations
  • Assignment #1: Data Exploration

II. Supervised Learning

• 05: INTRODUCTION TO MACHINE LEARNING
  • Slides
  • kNN Classification Iris dataset
  • kNN implementation optional exercise

Regression models

• 06: LINEAR REGRESSION

  • Presentations Assignment #1
  • Slides
  • Introduction to numpy optional
  • Linear Algebra recap optional
  • Linear Regression statsmodels, patsy, seaborn; salaries, house prices
  • 3D plot in Python example as reference

• 07: POLYNOMIAL REGRESSION & REGULARIZATION

  • Slides
  • Regularization polynomials, makepipeline, Ridge, Lasso

• 08: REGRESSION & TEXT PROCESSING

  • One slide
  • Text Processing Amazon movie reviews, CountVectorizer (demo)
  • Guest Speaker: Amy Roberts, CEO & Founder of Healthy Bytes
  • Assignment #2: Linear Regression Salary Prediction

III. Supervised Learning: Classification

• 09: LOGISTIC REGRESSION

  • Slides
  • Logistic Regression Iris dataset, precision/recall, decision boundaries; exercises
  • Insult Classification exercise
  • Area Under the ROC Curve optional deep dive
  • Non-linear decision boundaries optional

• 10: REVIEW

  • Slides Exit tickets review
  • Review Assignment #2 and leaderboard
  • Final project announcement guidelines, deadlines, sample projects
  • Guest Speaker: Rohit Acharya, Chief Data Scientist at First Access

• 11: STATISTICS & BAYES

  • Slides
  • 20 Newsgroups CountVectorizer, TfidfVectorizer, MultinomialNB (demo)
  • Naive Bayes implementation exercise
  • Statistics & Probability recap optional basic recap
  • Bayesian coin flips optional deep dive

• 12: ENSEMBLE LEARNING & RANDOM FORESTS

  • Slides
  • Random Forests in sklearn DecisionTree, RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
  • Drawing trees in sklearn optional Graphviz, pydot
  • Plotting decision boundaries optional ExtraTreesClassifier, AdaBoost
  • Random Forests implementation with notebook demonstration optional deep dive

• 13: SUPPORT VECTOR MACHINES

  • Slides
  • Recognizing hand-written digits demo
  • Plotting hyperplanes and support vectors demo
  • Plotting different SVM kernels optional demo of non-linear kernels
  • Separating mushrooms exercise
  • Guest Speaker: Sandy Griffith, Biostatistician and Technical Lead at Flatiron Health
  • Flask demonstration

• 14: REVIEW & COMPETITION

  • Slides Classification Review
  • Comparison of all classifiers exercise
  • Guest Speaker: Bob Filbin, Chief Data Scientist at Crisis Text Line
  • Assignment #3: Classification Kaggle competition, data exploration and demo solution

IV. Unsupervised Learning

• 15: CLUSTERING

  • Slides
  • Clustering irises sklearn, simple demo
  • Clustering text sklearn, 20-newsgroups
  • Clustering tags in Stack Overflow Jaccard distance, exercise
  • KMeans implementation: notebook and code exercise

• 16: DIMENSIONALITY REDUCTION

  • Presentations data explorations final project
  • Slides
  • PCA demo demo of the math
  • SVD demo demo of the math
  • Clustering House Legislatures demo PCA, polarizing politics
  • Facial Recognition demo PCA, SVM and exercise
  • Latent Semantic Analysis demo SVD, text clustering

• 17/18: RECOMMENDATION SYSTEMS

  • Slides
  • Recommending Beers demo of several recommendation methods
  • Who To Follow exercise item-based collaborative filtering
  • Guest Speaker: George Kailas, CEO at Instadat
  • Extracurricular: predicting student responses

V. Various

• 19: GUEST LECTURE - HASHING, A/B TESTING, CONJUGATE PRIORS

  • Guest Lecturer: Robert Doherty, Lead Data Science at Outbrain
  • Streaming Data Algorithms: Part 1 slides
  • Bayesian A/B Headline Testing: Part 2 slides
  • The Beta Distribution
  • Instant Headline Testing
  • Amazon Resellers LAB incl exercises

• 20: NEURAL NETWORKS

  • Slides
  • Videos: Neurons and the brain, Digit recognition and Autonomous driving by Andrew Ng
  • Python implementation and demonstrating notebook optional deep dive
  • Restriced Boltzmann Machines optional demo unsupervied neural nets in sklearn
  • Code Reviews work-in-progress

• 21: Amazon Web Services

  • Slides
  • Introduction to Amazon Web Services
  • Setting Up AWS
  • The Hadoop File System (HDFS)
  • Spark

• 22: MAP-REDUCE

  • Slides
  • Multiprocessing in Python local parallel computing
  • mrjob optional mapreduce framework in python
  • Guest Speaker: Ethics by Monica Bulger, Researcher at Data & Society

• 23: FINAL PROJECT PRESENTATIONS