Graphs and Data Science

Learn about the algorithms used in social network analysis and by search engines


icon Live Hours: 1 hour per week

icon Additional hours to be spent: 3-5 hours per session

icon Do I get a badge: Yes. There is an evaluation and a badge

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INSOFE’s Graphs and Data Science course is designed to introduce the field of graph algorithms in data science to excited students and working professionals. It is live, online, free and explores all crucial parts of data science (coding, mathematics, data mining) adequately. This course provides a gentle introduction to the field of computational thinking and data science.

Who is it aimed at?

Graph data occurs in many modern applications. This course is an excellent introduction to graph algorithms in data science for engineering students, graduates and working professionals who have had prior exposure to basic programming.

Through selected problems, students are introduced to the art of computational problem solving - an approach to solving problems using techniques and concepts from computer science and computational techniques.

The Graphs and Data Science course best suits two types of students and working professionals who want to become Data Scientists:


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Those who love computer science and coding will learn about fundamental big data algorithms.

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Those who love core engineering will get a gentle introduction to algorithms and data science.



Course Highlights

  • Classes are conducted live & online, every Thursday from 6:30 PM to 7:30 PM IST, starting from 3 September 2020.
  • There will be 10 classes in total and are repeated circularly. So, after the 10th class, the first class starts again.
  • Every class is associated with an online quiz of 5 questions.
  • The modules are made as independent as possible, so that students can join at several points and then complete 10 lectures. Typically, a student can start with the beginning of any module.

icon Live Hours: 1 hour per week

icon Additional hours to be spent: 3-5 hours per session

icon Do I get a badge: Yes. There is an evaluation and a badge.

graphs-and-data-science

Curriculum

 

The Graphs and Data Science course focuses on selected computational problems in data mining. More specifically, the following problems will be discussed:


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How can Internet companies decide which advertisements to assign to page views or search queries?

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How does a search engine choose the most relevant pages to show in response to a search query?

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Given a large graph that represents connections in a social network, how can one discover communities in this network?


 

Module 1 Link Analysis 4 Hours

Module 2 Advertising on the Web 3 Hours

Module 3 Mining Social-Network Graphs 3 Hours

About the Instructor

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Prof. Ashwin Ganesan

Associate Professor, INSOFE

Research areas: Distributed algorithms, graphs and algorithms in communication networks, discrete mathematics and graph theory, interconnection networks, applied combinatorics

  • He received the Bachelor’s degree from Marquette University, Milwaukee, Wisconsin, majoring in Electrical and Computer Engineering. He received the Master’s degree from the University of Wisconsin at Madison, specializing in communications and signal processing.
  • Prof. Ganesan has held various research and teaching positions, including as a Graduate Student Researcher and Graduate Student Instructor at the University of California at Berkeley, as a Research and Teaching Assistant at the University of Wisconsin at Madison, and as a faculty member at various engineering institutes.
  • He is an Associate Professor at the International School of Engineering (INSOFE), Mumbai, Maharashtra.

Awards and Honors


  • Top Scholar in Curriculum Award from Marquette University
  • Frank Rogers Bacon Fellowship from UW-Madison
  • Regents’ Fellowship from UC-Berkeley
  • He is a member of National Honor Society, Eta Kappa Nu, Pi Mu Epsilon, and Tau Beta Pi

Graphs and Data Science course is absolutely
live, online and free!

Learn about the algorithms used in social network analysis and by search engines.


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