Monday, 3 March 2014

Social Data Mining With Python

Humans have taken their voices online en-masse. Twitter publishes half a billion tweets each day. Over one billion people engage their friends via Facebook. So what makes Facebook worth $170 billion - about $150 per active user? Mainly, it's the ability to sell things to those users, based on analysis of their data and behavior.

That's what a course at, Social Data Mining with Python, is about.  You will use Python to extract valuable signals from these huge, chaotic datasets to explain collective behavior and create computational knowledge bases. Using Python, you will analyze user-generated content such as movie ratings, online comments, status updates, and friendship networks. You will learn algorithms from the fields of social network analysis, text analysis, and recommender systems. Finally, you will gain experience with pragmatic workflows that leverage social APIs to reveal human insights in your own projects.  This course includes substantial Python instruction, and is a good way for those with some Python familiarity to extend their knowledge. The instructor of this course is Dr. Shilad Sen. For more details about course and instructor please visit at

Who Should Take This Course:
Programmers and statisticians familiar with Python who want to learn how to do analysis of text and social network date; analysts who know some Python and who want to deepen their Python knowledge by learning how to mine social data.

Course Program:
Week 1: Recommendation algorithms
  • Refresher on Python data structures
  • Streaming large datasets
  • Filtering noise in long-tailed datasets
  • Algorithmic time complexity in a nutshell
  • Introduction to recommendation algorithms
  • Case study: If you liked “Star Wars” you’ll like ??

Week 2: Introduction to text analysis in Python
  • Python’s “yield” keyword
  • Tf/Idf weighting
  • Algorithms that identify distinctive language
  • K-means clustering
  • Case study: Distinctive language in subreddit communities

Week 3: Social APIs
  • A taxonomy of social APIs and Python interfaces to them
  • Practical workflows when using social APIs
  • Case study: Datasift & sentiment analysis on Twitter

Week 4: Social network analysis
  • Network data structures in Python
  • Your Facebook graph: visualization and community detection
  • Using the Gephi network visualization tool
  • Case study: Inducing network graphs - the landscape of movies

The instructor, Dr. Shilad Sen is an Assistant Professor at Macalester College in St. Paul, and he builds systems that empower people to be better contributors to online communities. His professional experience also includes Sourcelight Technologies, Google, IBM Research, and Thomson Reuters R & D.  Course participants will ask questions and exchange comments with Dr. Sen via a private discussion forum.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

We, the Center for eLearning and Training (C-eLT), Pune, partner with and offer these courses to Indian participants at special prices payable in INR.

For India Registration and pricing, please visit us at

Call: 020 6680 0322

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