Course Name and Number: DATA 620 Web Analytics Credits: 3 cr. Prerequisite(s): IS 606 and IS 607

Organizations, both commercial and community, can benefit from deep analysis of their website interactions and mobile data. Social networks have also become a source of information for companies; search engines are an important referral mechanism. Popular social networks and other online communities provide rich sources of user information and (inter-) actions through their application programming interfaces. This data can help to identify a number of individual user preferences and behaviors, as well as fundamental relationships within the community. Search engines use algorithms to rank sites. Students will learn how to analyze social network data for types of networks, the fundamental calculations used in social networks (e.g., centrality, cohesion, affiliations, and clustering coefficient) as well as network structures and roles. Beyond social network data, students will learn about important concepts of analyzing website traffic such as click streams, referrals, keywords, page views, and drop rates. The course will touch on the fundamentals of search algorithms and search engine optimization. To provide a basic context for understanding these online user and community behaviors, students will learn about relevant social science theories such as homophily, social capital, trust, and motivations as well as business and social use contexts. In addition, this course will address ethical and privacy issues as they relate to information on the Internet and social responsibility.

At the end of this course, students will be able to:

• Analyze text data, including natural language processing and text representation, word association, topic mining, opinion mining and sentiment analysis, and text-based prediction.
• Perform network analysis, including creating graphs, calculating statistics on nodes, and graph visualization.
• Work with various social network APIs, including Twitter, Facebook, and Linked In.

Students will be required to:

• Apply what they learning about network analysis and text mining in a series of increasing complex projects and associated presentations.

Text mining is about working with unstructured data. Network analysis focuses more on relationships than entities. These are two of the fastest growing sub-fields of data science, and are increasingly successful for success in the workplace.

• Social Network Analysis for Startups, Maksim Tsvetovat and Alexander Kouznetsov, O'Reilly, Sep 30, 2011. https://github.com/maksim2042/SNABook
• Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper, O'Reilly, Jun 30, 2009.
• Mining the Social Web, 2/e, Matthew A. Russell, Oct 20, 2013. https://github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition provides a rich set of additional materials; author’s blog http://miningthesocialweb.com/

• Mining the Social Web, 2/e, Matthew A. Russell, Oct 20, 2013. https://github.com/ptwobrussell/Mining-the-Social-Web-2nd-Edition provides a rich set of additional materials; author’s blog http://miningthesocialweb.com/
• Networks, Crowds, and Markets: Reasoning About a Highly Connected World, David Easley and Jon Kleinberg, Cambridge 2010. Freely downloadable: http://www.cs.cornell.edu/home/kleinber/networks-book/
• Network Science Book, Lazlo Barabasi. Freely downloadable: http://barabasilab.neu.edu/networksciencebook/. Printed version expected 2015 by Cambridge University Press
• Graph Databases, Ian Robinson, Jim Webber, and Emil Eifrem, O'Reilly, June 20, 2013. Freely downloadable from O'Reilly.
• Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman, http://infolab.stanford.edu/~ullman/mmds/book.pdf. Supporting materials, including links to Coursera course: http://www.mmds.org/

• Python 2.7 or Python 3 with NetworkX and NLTK installed (free distribution from Anaconda here: https://www.anaconda.com/download/)
• Assignments turned in as Jupyter notebooks, with notebooks in Github, and links to notebooks in assignment submission text.
• Some students have used Turi Create. Freely available for students. https://github.com/apple/turicreate. It doesn’t really work on Windows unless you use WSL.

Alain Ledon [email protected]

You are encouraged to ask me questions on the “Ask Your Instructor” forum on the course discussion board where other students will be able to benefit from your inquiries.

I am available by e-mail or by cell phone. We can set up virtual one-on-one meetings. For the most part, you can expect me to respond to questions by email within 24 to 48 hours. If you do not hear back from me within 48 hours of sending an email, please resend your message.

This course is conducted entirely online. Here is what your weekly workload and deliverable schedule will look like:

• Each week’s material is available.
• You’ll have a list of readings. There will also be a number of short videos to watch most weeks.
• There is a short, lightly graded discussion topic each week. Your initial post due before the meet-up, and your response due end of day the following Friday.
• For each course track, you’ll submit four projects and a final project Each submission has to have a short video explaining your work. Most weeks when there are not projects due, you’ll have shorter coding assignments.
• You may always propose in advance to substitute your own datasets for the assigned datasets.
• Students are expected to complete all assignments by their due dates. Any work turned in after the due date will receive a maximum score of 80%. If solutions have been posted for an assignment before you’ve turned it in, you’ll need to propose an alternative assignment acceptable to the instructor. Future data scientists please take note: there is an overwhelmingly positive correlation between how early students turn in their assignments and their course grades!
• There will also be short ungraded “hands on labs” that will help you prepare for your assignments.
• Working in teams on the projects is strongly encouraged, but not required. The ability to work effectively on virtual teams is an important “soft skill” for data scientists.
• If you take non-trivial amounts of code from the web or other sources, you must provide full attribution. This way, your grade will be based on the code that you added to the found “starter” code.

DATA 620 Meetups (8-9 Wednesday) Alain

Please join my meeting from your computer, tablet or smartphone.

https://global.gotomeeting.com/join/480814045

You can also dial in using your phone. United States: +1 (646) 749-3112 Access Code: 480-814-045

New to GoToMeeting? Get the app now and be ready when your first meeting starts:

https://global.gotomeeting.com/install/480814045

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http://sps.cuny.edu/student_services/disabilityservices.html

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