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Ph.D. in Data Science and Technology |

Mode:

Regular

Location:

Vidyavihar - Mumbai

Course Duration:

3 Years

Programme Code:

ST1753

In the transforming landscape of technology, the PhD in Data Science and Technology at K J Somaiya Institute of Management (KJSIM) stands as a beacon for aspiring scholars and researchers who are passionate about pushing the boundaries of data-driven innovation. This advanced programme is designed to cultivate the next generation of thought leaders who will drive the transformation of industries and societies through the power of data.

Why Choose Data Science and Technology
Core Highlights
Research Focus Areas
Eligibility Criteria
Coursework Structure and Requirements
Scholarly Guides

Why Choose Research Area - Data Science and Technology with KJSIM?

The global reliance on data for decision-making, problem-solving, and value creation has never been greater. Advancements in data analysis technology have revolutionised how businesses access, store, and process information, enabling organisations to efficiently extract and apply insights from vast datasets. The data analysis technology market is on the brink of explosive growth, with an estimated market size of USD 80.5 billion in 2024, projected to soar to USD 941.8 billion by 2034. With a robust CAGR of 31.0%, this decade will be crucial for technological innovation, reshaping how industries harness data to achieve success.

Given the surging demand in this field, there has never been a better time to pursue a PhD in Data Science and Technology. KJSIM offers an exceptional programme designed to equip scholars with the expertise needed to lead in this dynamic and rapidly evolving industry.

Core Highlights

Specialised Research Opportunities

Scholars at KJSIM have the chance to engage in trending research areas. The programme encourages interdisciplinary collaboration, allowing researchers to explore innovative solutions to complex data challenges.

Expert Faculty and Mentorship:

Learn from and collaborate with a distinguished faculty comprising leading experts in data science and technology. These mentors bring a wealth of industry experience and academic knowledge, guiding scholars through their research journey and helping them to produce impactful work.

Industry Collaboration and Real-World Applications

The programme offers numerous opportunities for collaboration with top industry partners, enabling scholars to apply their research in real-world scenarios. These partnerships provide valuable insights into industry trends and help bridge the gap between academic research and practical application.

Access to Top Technology and Resources

Scholars benefit from access to state-of-the-art technology, including high-performance computing clusters, advanced software tools, and extensive datasets. These resources are critical for conducting high-quality research and staying at the forefront of developments in the field.

Flexible Learning:

The first semester requires full-time attendance, while the second semester offers a more flexible schedule to accommodate research needs.

Global Research Network

Become part of an international community of researchers and thought leaders in data science. The programme offers opportunities to attend global conferences, publish in top-tier journals, and collaborate on international research projects, enhancing your academic profile and impact.

Research Focus Areas

  • IT-Enabled Cross-Functional Applications and Decision Making: Exploring how IT can enhance decision-making processes across different functions within organisations.
  • Big Data Analytics: Investigating methods to analyse and derive actionable insights from large and complex datasets.
  • Internet of Things (IoT): Researching the integration of IoT technologies in various sectors to improve efficiency and connectivity.
  • Web-Based Applications and Analytics: Developing and analysing web-based platforms for data processing and business intelligence.
  • Networking, Technology Adoption, and Frameworks: Studying the adoption of new technologies and frameworks within organisations to optimise performance.
  • E-Services and Cyber Security: Examining the development and security of electronic services in a digital world.
  • Digital Interventions and Impact on Cross-Functional Processes: Analysing how digital technologies influence and improve cross-functional processes within businesses.
  • Diffusion of Digital Innovations: Understanding the spread and adoption of digital innovations such as M-Commerce, E-Learning, and E-Governance.
  • Digital Transformation: Investigating the comprehensive changes organisations undergo to integrate digital technologies into their operations.

Eligibility Criteria

Educational Qualifications

  • A Master's degree or equivalent professional degree with at least 55% marks, as per UGC regulations.
  • Candidates awaiting the final viva-voce of their Master's degree may apply, provided they complete their degree before provisional admission.
  • Candidates with a Master's degree from a recognised foreign institution are also eligible.

Ph.D Entrance Exam

  1. All candidates must qualify in the SVU Ph.D Entrance Examination unless exempted.
  2. Exemption Criteria:
    • Qualified/valid scores in NET-JRF, JEST, or holding a JRF Fellowship with CSIR/UGC/ICAR/ICMR/DBT.
    • A valid GMAT score (350 minimum) from the past two years.
    • Five years of teaching/research experience with published research in SCOPUS/Web of Science journals, patents, or government grants (subject to submission of relevant documents).

Coursework Structure and Requirements

  1. The Ph.D coursework spans one academic year, divided into two semesters. The first semester is a full-time commitment, where students are required to attend in-person sessions at the college, department, section, or laboratory as per the scheduled timetable.
  2. Students must complete a total of 19 credits—14 credits in the first semester and 5 credits in the second semester. Achieving a satisfactory Cumulative Grade Point Index (CGPI) in accordance with the Ph.D regulations is mandatory for eligibility to register for the Ph.D programme.

Scholarly Guides

Sr.No. Research Guide Qualification Research Areas
1 Prof. Dr. D G Jha Ph.D. (University of Mumbai, 2012) Data Management, Business - Domain Information Systems/Applications, Data Driven Decision Making
2 Prof. Dr. Sindhu S Singh Ph.D. (SNDT University, 2016) Technology Management, E-commerce, M-Commerce, Machine Learning
3 Prof. Dr. Jaya Iyer Ph.D. (SNDT University, 2016) E-Government, ICT, M-Government, Digital Government, Public service sector, E-learning, E-business, Adoption of website quality, Cloud computing, MVC architecture.
4 Prof. Dr. Bharati V Wukkadada Ph.D. (JJT University, 2015) Technology Management, data science (machine learning, R, python, Networking (wired & wireless)
5 Prof. Dr. Kirti V Wankhede Ph.D. (D Y Patil University, 2013) Big data analytics, IoT analytics, Web development
6 Prof. Dr. Chandan Singhavi Ph.D. (ITM University Raipur, 2019) Technology Adoption and Integration, ICT Adoption in Education, Blockchain in Banking, ERP Implementation
7 Prof. Dr. Sangeetha Rajesh Ph.D. (Annamalai University, 2022) Artificial Intelligence, Machine Learning & Deep Learning, Gen AI in Business, Business Analytics, Audio Processing
8 Prof. Dr. Krantee Jamdaade Ph.D. (University of Mumbai, 2019) Data Science, Machine Learning and Artificial Intelligence

Take a Transformative Leap with KJSIM