This course introduces relevant programming techniques for data analytics. Topics include programming languages, relevant software packages, good programming practices, linear algebra in data analytics, numerical computing, and 4~5 machine learning algorithms as running problems. After completing the course, students will gain the skills to implement a data analytics pipeline (data collection, data retrieval, data analysis, data visualization) and several "handy" machine learning algorithms.

Piazza Discussion Forum

We will use Piazza for discussion (e.g., homework, project). Post your questions there, and the teaching staff and your fellow classmates will be able to help answer them quickly. You can also use Piazza to find project teammates.

T-square will only be used for submission of assignments and projects.

Office Hours

Instructor	Da Kuang	Thu, 2-3pm, Klaus 1315
Instructor	Polo Chau	Thu, 4-5pm, Klaus 1315
TA	Lianxiao (Shawn) Qiu	Mon, 1-2pm, Klaus 2108

Prerequisites

Students should have some experience in programming with any language, for example, working knowledge of variables, operators, statements, control flows, reference, functions, classes, etc., and should feel comfortable reading documentation.

Additional formal prerequisites

Undergraduate semester level CS 1371 (Computing for Engineers) or a different programming course, minimum grade of D.

Schedule (tentative)

Date		Topic	Wed	Fri	Events
Aug	20, 22	* Course introduction * Course survey * Introduction to Python and its data structures	Slides	Slides
	27, 29	* Python exercises Q&A * Data collection wget, urllib/urllib2, API		Slides
Sep	3, 5	* Data collection (cont'd) BeautifulSoup * Topics in Python	Slides	Slides	HW1 out (Wed)
	10, 12	* Charting/Visualization Charting in R	R resources (Link 1) (Link 2) (Link 3)	Slides
	17, 19	* Data storage and retrieval in sqlite * Basic linear algebra overview Vectors, matrices	Slides	Notes	HW1 due (Fri)
	24, 26	* Dense and sparse matrices (including Numpy) * Good programming practices	Slides
Oct	1, 3	* Basic linear algebra overview Matrix-vector multiplication Norms	Notes	Notes	HW2 out (Mon)
	8, 10	* Linear regression Least squares Computing Least squares Case study	Notes	Scripts	HW2 due (Fri) HW3 out (Sat)
	15, 17	* Logistic regression Regression vs. Classification Gradient descent Case study	Slides	Scripts
	22, 24	* Computer architecture overview * Vectorization in Numpy and R	Slides	Scripts	HW3 due (Fri/Sat)
	29, 31	* K-means clustering * Project proposal presentations	Slides		Project proposal due (Thu)
Nov	5, 7	* K-means clustering: Case studies * Efficient implementation of K-means	Scripts (README)
	12, 14	* Numerical software stacks * Singular value decomposition (SVD)	Slides	Notes
	19, 21	* SVD, eigenvalue decomposition (EVD), and PCA * Computing SVD, EVD and PCA	Notes		Progress report due (Wed)
	26, 28	(Thanksgiving holiday)
Dec	3, 5	* Latest research; popular topics * Final project presentations	Slides		Final report due (Thu)

Grading

• 10% Class and Piazza participation
• 50% Homework
• 40% Final project

Late Submissions Policy

• No late homework allowed. However, there are no penalties for medical reasons or emergencies. You must submit a doctor's note or an official letter explaining the emergency.

Textbooks, references, and reading materials

• None required.


• References for programming:
  • Python (official) tutorial.
  • Java (official) tutorial.
  • Clean code: A handbook of agile software craftsmanship by Robert C. Martin.
  • Refactoring: Improving the design of existing code by Martin Fowler.
  • Effective Java (2nd edition) by Joshua Bloch.
  • Programming pearls (2nd edition) by Jon Bentley.


• References for linear algebra / numerical computing:
  • Numerical linear algebra by Lloyd N. Trefethen and David Bau, III (on Google Books).
  • Applied numerical linear algebra by James W. Demmel (partially on Google Books).


• References for "practical" machine learning:
  • Python for data analysis by Wes McKinney.
  • Mining the social web: Data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more (2nd edition) by Matthew A. Russell (code online).
  • Mining of massive datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman (free online).

Homework (tentative)

Please note that while collaboration is allowed, individual collaborators *must* write up their own answers. All GT students must observe the honor code.

• [10%] HW1: Collecting Yelp data using Python [starter code for Part 1] [starter code for Part 2]
• [10%] HW2: Querying and plotting Yelp data
• [10%] HW3: Linear algebra exercises
• [10%] HW4: Estimating the running time
• [10%] HW5

Project

Team project: 2-3 people. Description and grading policy available now! (proposal + presentation, progress report, final report + presentation)

Dataset Ideas

• Freebase
• Yelp
• Numerous APIs from Google (e.g., Maps, Freebase, YouTube, etc.)
• Trulia, Zillow: real estate listing sites
• Movies data: Rotten Tomatoes, IMDB
• Dataset about soccer games, players, clubs.
  No API, but easy to scrape.
  For a soccer player: transfer history, performance, nationality, birth date, etc.
  For a soccer club: performance, squad, etc.
  Thanks Ding!
• Article Search API from the New York Times (all the way back to 1851!) (Thanks Guido!)

Auditors

Auditors must first obtain instructor's permission of the instructor, then enroll in the course. The auditor must attend all lectures, and optionally complete the assignments.

Acknowledgements & Related Classes

Many thanks to our colleagues for sharing their course materials:

Prof. Le Song - Introduction to Computational Data Analysis - Spring 2014

---