DATA 301: Introduction to Data Science
Spring 2016

Instructor: Alexander Dekhtyar, dekhtyar@calpoly.edu, 14-210

Office Hours:

When		Who	Where
Monday	2:10pm - 3:00pm	Alex	14-210
Wednesday	2:10pm - 3:00pm	Alex	14-210
Wednesday	8:30am - 10:00am	Alex	14-210

Additional appoinments: send email.

News and Notes

• Our Jupyter website.

• Welcome to DATA 301! Please, take the survey:

  DATA 301 Survey

Course Materials

Project

Project Specification

Due: June 8

Postscript

PDF

Dataset sources

[May 18, 2016]

Labs

Lab 1	Due: April 4	Data Science in a nutshell	Postscript	PDF	Lab Data	[March 28, 2016]
Lab 2	Due: April 6	Tabular Data Extraction	Postscript	PDF	Lab Data	[April 4, 2016]
Lab 2-2	Due: April 9	Tabular Data Extraction	Postscript	PDF	Lab Data	[April 4, 2016]
Lab 4	Due: April 15 18	Work with Text Documents	Postscript	PDF	Lab Data	[April 8, 2016]
Lab 5	Due: April 25	Website Scraping and News Analysis	Postscript	PDF	Lab Data	[April 13, 2016]
Lab 6	Due: April 27 28	Jupyter	Postscript	PDF	Jupyter	[April 27, 2016]
Lab 7	Due: May 2 4	NumPy and Jokes	Postscript	PDF	Jupyter	[April 27, 2016]
Lab 8	Due: May 6	NumPy and T-test	Postscript	PDF	Jupyter	[May 6, 2016]
Lab 9	Due: May 11	NumPy and Linear Regression	Postscript	PDF	Jupyter	[May 6, 2016]
Lab 10	Due: May 20	Joke Predictions	Postscript	PDF	Jupyter	[May 11, 2016]
Lab 11	Due: May 29	Classification and Clustering	Postscript	PDF	Jupyter	[May 23, 2016]
Lab 12	Due: June 1	Lab Exam Prep	Postscript	PDF	Jupyter	[May 27, 2016]

Assigned Reading

• Week 1. Due: April 4
  Vasant Dhar, Data Science and Prediction, Communications of the ACM, vol. 56 (12), PDF
  [March 28, 2016]

• Week 2. Due: April 11
  Philip Guo, Software Tools to Facilitate Research Programming, Ph.D. Thesis, Stanford University, PDF.
  Read Chapters 1 and 2
  [April 3, 2016]

• Week 4. Due: April 26
  M.F. Porter, An algorithm for suffix stripping, Program, vol. 14 no. 3, pp 130-137, July 1980, TXT.
  [April 19, 2016]

Homeworks

Lecture Notes

Lecture 1	What is Data Science?	Postscript	PDF	[March 28, 2016]
Lecture 2	Data Science Process	Postscript	PDF	[April 3, 2016]
Lecture 3	Data Acquisition	Postscript	PDF	[April 3, 2016]
Lecture 4	Tabular Data	Postscript	PDF	[April 3, 2016]
Lecture 5	Textual Data	Postscript	PDF	[April 5, 2016]
Lecture 6	XML Data	Postscript	PDF	[April 11, 2016]
Lecture 7	Document Object Model (DOM)	Postscript	PDF	[April 11, 2016]
Lecture 8	HTML and Beautiful Soup	Postscript	PDF	[April 20, 2016]
Lecture 9	Maps and JSON	Postscript	PDF	[April 20, 2016]
Lecture 14	Recommendation Predictions	Postscript	PDF	[May 11, 2016]
Lecture 15	Supervised Learning (Classification)	Postscript	PDF	[May 18, 2016]
Lecture 16	Unsupervised Learning (Clustering)	Postscript	PDF	[May 23, 2016]

Other Materials