E0 259 : Data Analytics

Instructors

Ramesh Hariharan (Strand Genomics and ECE, IISc)
Vikram Srinivasan (Founder, Needl.ai and ECE, IISc)
Rajesh Sundaresan (ECE and RBCCPS, IISc)

Teaching Assistants

Aditya Gupta
Shankaradityaa Venkateswaran
Tarun Kumar Sahu

Teams Code

8b1ysov

Lecture Hours

Lectures: Tuesdays and Thursdays 15:30 - 17:10 hrs
Location: MP 20
First class: Tuesday 06 January 2026, 15:30 - 17:10 hrs

Teaching Assistant Hours

TA hour: Thursdays, 18:00 - 19:30 hrs
Location: MP20

Project Presentation

Thursday 16 April 2026, 09:00 hrs onward, attendance in your slot is compulsory
Location: MP20

Examinations

Quizzes: Will be announced in class.
Final examination: TBA, as per SCC final exam timetable.

Course syllabus

Data sets from astronomy, genomics, neuroscience, sports, surveillance cameras, and social networks will be analysed to answer specific scientific questions. Statistical tools and modeling techniques will be introduced as needed to analyse the data and eventually address the scientific question.

Prerequisites

• Random Processes (E2 202) OR Probability and Statistics (E0 232) OR equivalent.

Course Grade

• 10 marks: Engagement in mandatory lab classes (3 points per lab, 1 lab/week during the TA session, max three-fourths of the scores will be summed and scaled to 10 marks)
  
• 20 marks: Submitted assignments (code and report) and viva voce examination during identified TA sessions (10 points/assignment, six assignments, all assignments count, total scaled to 20 marks)
  
• 20 marks: In-class written quizzes (six quizzes, 10 points/quiz, best four will be summed and scaled to 20 marks)
  
• 20 marks: Course project and presentation
  
• 30 marks: Final written examination
  

Reference Texts

• There is no text book for this course. Slides of lectures will be available on the Teams page (2026). (The slides on this webpage are older versions for the public.)

2026 Lecture Progression

• Community detection
• Recommendation systems
• Colour blindness
• TBA
• TBA
• TBA

Course Description

Data Analytics has assumed increasing importance in recent times. Several industries are now built around the use of data for decision making. Several research areas too, genomics and neuroscience being notable examples, are increasingly focused on large-scale data generation rather than small-scale experimentation to generate initial hypotheses. This brings about a need for data analytics. This course will develop modern statistical tools and modeling techniques through hands-on data analysis in a variety of application domains.

The course will illustrate the principles of hands-on data analytics through several case studies (10 such studies). On each topic, we will introduce a scientific question and discuss why it should be addressed. Next, we will present the available data, how it was collected, etc. We will then discuss models, provide analyses, and finally touch upon how to address the scientific question using the analyses.

In one of the previous offerings, we covered the following case studies.

• Astronomy: From Tycho Brahe's observations to the conclusion that Mars moves in an elliptical orbit.
  
• Visual Neuroscience: Neural correlates predict search difficulty.
  
• Genomics: Understanding the causes of cancer.
  
• Sports: The Duckworth-Lewis-Stern method for setting targets in shortened limited overs cricket matches.
  
• Genomics: The basis for red-green colour blindness.
  
• Genomics: Population history of India.
  
• Signal Processing: Video background separation.
  
• Networks: Community detection.
  
• Recommendation systems.
  

2024 Lecture Progression

• Community detection
• Mars orbit
• Effects of smoking
• Primer on hypothesis testing (in preparation for the following modules)
• Cricket - Duckworth-Lewis-Stern method
• Visual neuroscience
• Colour blindness
• Recommendation systems
• Natural language processing

2023 Lecture Progression

• Cricket - Duckworth-Lewis-Stern method
• Primer on hypothesis testing (in preparation for the following modules)
• Community detection
• Effects of smoking
• Visual neuroscience
• Mars orbit
• Covid-19 modelling
• Colour blindness
• Recommendation systems
• Natural language processing

2021 Lectures and assignments

• To submit assignments, login to the course's moodle page.
  
• Module 1: Mars orbit (Ramesh Hariharan)
  
• Module 2: The Duckworth-Lewis-Stern method (Rajesh Sundaresan)
  
• Module 3: Covid-19 modelling (Rajesh Sundaresan)
  
• Module 4: Colour blindness (Ramesh Hariharan)
  
• Module 5: Community detection (Rajesh Sundaresan)
  
• Module 6: Effects of smoking (Ramesh Hariharan)
  
• Module 7: Visual neuroscience and oddball detection (Rajesh Sundaresan)
  

2018 complementary lectures and assignments

• Module 8: Recommendation systems (Ramesh Hariharan)
  
• Module 9: Video background separation (Rajesh Sundaresan)
  
• Module 10: Retinoblastoma and age-related cancer (Ramesh Hariharan)
  

2017 complementary lectures and assignments

• Module 11: Population history of India (Ramesh Hariharan)
  

2016 complementary lectures and assignments

• Module 12: Functional connectivity patterns of the brain (Rajesh Sundaresan)
  

2015 complementary lectures and assignments

• Module 13: Speech recognition (Karthik Visweswariah, IBM India Research Lab)
  
• Module 14: Industrial Internet of Things (Babu Narayanan, Tapan Shah, GE India)
  
• Module 15: Sports analytics (Shubhabrata Das, IIM Bangalore (Venue: Faculty Hall, Thursday 26 Nov 2015, 4:00 PM))