DATA 401: Data Science
Fall 2016

Instructors:

• Alexander Dekhtyar, dekhtyar@calpoly.edu, 14-210
• Dennis Sun , dsun09@calpoly.edu, 25-105

  Office Hours:

  When		Who	Where
  Monday	7:10pm - 8:00pm	Dennis	25-105
  Tuesday	1:10pm - 2:00pm	Dennis	25-105
  Wednesday	8:10am - 10:00am	Alex	14-210
  Wednesday	10:10am - 11:00am	Dennis	25-105
  Thursday	8:10am - 10:00am	Alex	14-210
  Friday	5:10pm - 6:00pm	Dennis	Google Hangouts

  Additional appoinments: send email.

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  News and Notes

  • Please provide us with your Github ID using this Google Form
    [October 2, 2016]
  • Welcome to DATA 401! Please, take the survey: DATA 401 Survey

  Course Materials

  Syllabus	PDF
  Piazza Website	[HERE]
  Jupyter Website	jupyter.calpolydatascience.org
  Reading List	[HTML]

  Projects

  Project 1	Due: October 19	PDF	Dataset	[October 1, 2016]
  Project 3	Due: November 28	PDF	Dataset	[November 9, 2016]
  Project 4	Due: December 12	PDF		[December 7, 2016]

  Activities

  Lab 0	September 26	Find Something Interesting	PDF	Lab Data	[September 25, 2016]
  Lab 1		Linear Regression		Lab Data	[September 28, 2016]
  Lab 2	October 5	Feature Engineering	PDF	Notebook	[October 5, 2016]
  Lab 3	October 10	Data Engineering	PDF		[October 10, 2016]
  Lab 4	October 12	Gradient Descent		Notebook	[October 10, 2016]
  Lab 5	October 19	Logistic Regression		Notebook	[October 19, 2016]
  Lab 7	November 7	Perceptron		Notebook HousingSmallSet.csv	[November 7, 2016]
  Lab 7-1	November 14	Support Vector Machines		Notebook HousingSmallSet.csv	[November 8, 2016]
  Lab 8	December 7	TensorFlow		Notebook	[December 7, 2016]

  Data Science Lecture Notes

  Lecture 1	Data Science Process and Challenges	PDF	[September 25, 2016]
  Lecture 1-2	Data Science Challenges	PDF	[September 28, 2016]
  Lecture 2	Requirements Elicitation	PDF	[October 3, 2016]
  Lecture 3	Data Acquisition	PDF	[October 10, 2016]
  Lecture 4	Data Integration	PDF	[October 19, 2016]

  Methodology Lecture Notes

  Lecture 1	Linear Regression	PDF	[September 28, 2016]
  Lecture 2	Feature Engineering	PDF	[October 3, 2016]
  Lecture 3	Model Comparison	PDF	[October 10, 2016]
  Lecture 4	Model Selection	PDF	[October 10, 2016]
  Lecture 5	Gradient Descent	PDF	[October 12, 2016]
  Lecture 6	Principle of Maximum Likliehood	PDF	[October 19, 2016]
  Lecture 7	Logistic Regression	PDF	[October 19, 2016]
  Lecture 8, 9	Classification: K-Nearest Neighbors, Perceptron and Support Vector Machines	PDF	[October 24, 2016]
  Lecture 10	Classification: Non-Linear Methods (KNN, Decision Trees)	PDF	[October 24, 2016]
  Lecture 11	Classification: Ensemble methods	PDF	[November 7, 2016]

  Case Studies

  Case Study 1

  Linear Regression

  PDF

  [October 5, 2016]

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  Other Materials

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  March 28, 2016, dekhtyar at csc.calpoly.edu