PhD Data Science

Course Description

PhD Data Science at Shoolini University is an interdisciplinary program framed as per the regulations of the National Education Policy (NEP-2020). The concept is that today’s large and complex datasets cannot be handled by traditional data processing applications. The need to handle big data requires big models, and doctoral scholars who are able to do so, are in much demand. 

Experienced faculty members from renowned institutions like NCI, USA, NIH, USA, IISc and Oxford teach this program at Shoolini University. 

This unique program provides students with the opportunity to work alongside industry partners in analysing, capturing, sharing and visualising a great amount of data that can be used to solve challenges in real time. 

Shoolini University gives its students global exposure through collaborations with leading international institutions to enhance their learning experience. Shoolini has over 250 collaborations with top universities like Lanzhou University, China; Gachon University, Korea; University of Naples, Italy; University of Arkansas, USA; University of Maryland, USA. 

Shoolini University is UGC Approved and NAAC Accredited.

Shoolini has dedicated Centres of Excellence for Data Science:

Key Highlights 

  • State-of-the-art computer labs 
  • Collaboration with Ikigai Lab, IIT Kanpur 
  • Patents filing by students and faculty encouraged 
  • 3rd in Research and Patent Filing (India Today) 
  • Guest lectures by global experts in the field of Data Science 
  • Highly qualified and experienced Faculty

 

Research Opportunities

Machine Learning | Artificial Intelligence | Databases| Statistics | Optimization | Natural Language Processing | Computer Vision | Speech Processing

Career Opportunities

Students who successfully complete PhD Data Science have a bright future ahead of them. They can consider positions in teaching, research and industry. Some of the key career positions that students with a PhD in Data Science can seek are: 

  • Data Scientist 
  • Machine Learning Engineer 
  • Machine Learning Scientist
  • Statistician 
  • Application Architect 
  • Enterprise Architect 
  • Data Architect
  • Data Analyst 
  • Infrastructure Architect 
  • Data Engineer 
  • Business Intelligence Developer 
  • Professor and Researcher 

Course Information