The Master of Technology in Data Science and Industrial Analytics is a rigorous academic programme that prepares students for careers in Data Science and Industrial Analytics. The programme covers a wide range of courses such as; Research Methods, Machine Learning with Python, Data Security and Professional Ethics, Calculus and Matrix Algebra with MATLAB, Data visualization with Power BI, Big Data mining and others. Students will also learn to use analytical tools for big data analyses, conduct practical work-based research and design, maintain and develop data systems to suit current industry demands.

The programme is of one year duration, made up of two semesters. In the first semester, the focus is on coursework, with a heavier load of six courses. Then, in the second semester, they transition to more practical experiences like industrial attachment, seminars and thesis work, with the option to take up to three courses alongside these activities. This balance allows students to gain both theoretical knowledge and practical experience in their field of study. Details of structure is given below;

 YEAR ONE, SEMESTER ONE

YEAR ONE, SEMESTER TWO

Mode of Delivery

• Lectures, Seminars and Workshops
• Assignments and Exercises
• Individual and Group Presentations

Assessment

• Class Test and Quizzes. Students will be assessed through regular class test and quizzes, which carry a weightage of 10%.
• Assignments, with a weightage of 10%.
• Mid-Semester Examination, which carry a weightage of 20%
• End-Semester Examination, which carry a weightage of 60%

It is expected that by the time students’ graduate from the programme, they should be:

• Able to competently use analytical tools like Python, R, Power BI for big data analysis and interpretation.
• Able to conduct practical work-based research
• Able to integrate fields within computer science, optimization and statistics to generate adept and well-rounded industrial solutions.
• Able to identify problems and evaluate the extents of such problems and recommend appropriate solutions.
• Able to design, maintain and develop data systems to suit current innovative demands
• Able to interpret data findings effectively to any audience orally, visually and written formats.
•  

Career Prospects

1. Data Scientist

• Develop and implement advanced data models and algorithms.
• Analyze large datasets to derive actionable insights and solve complex business problems.

2. Machine Learning Engineer

• Design, build, and deploy scalable machine learning models.
• Optimize algorithms for performance and accuracy in various industrial applications.

3. Data Analyst

• Perform statistical analysis and create reports to help businesses make informed decisions.
• Interpret data trends and patterns to provide strategic recommendations.
• Business Intelligence Analyst
• Develop and manage BI solutions for businesses.
• Create and maintain dashboards, reports, and data visualizations.

4. Industrial Data Analyst

• Analyze manufacturing and production data to improve efficiency and reduce costs.
• Implement predictive maintenance models to minimize equipment downtime.

5. Data Engineer

• Design, construct, and maintain data pipelines and databases.
• Ensure data integrity and accessibility for analysis and reporting purposes.

6. Analytics Consultant

• Provide expert advice on data strategy, analytics solutions, and technology implementation.
• Help organizations leverage data to drive business growth and innovation.

7. Quantitative Analyst (Quant)

• Use statistical and mathematical models to assess financial risks and opportunities.
• Develop trading algorithms and investment strategies.

8. Data Architect

• Design and manage an organization’s data architecture and frameworks.
• Ensure data systems are scalable, secure, and meet business requirements.

9. Chief Data Officer (CDO)

• Lead data management and governance strategies within an organization.
• Drive data initiatives and ensure alignment with business goals.

10. AI Research Scientist

• Conduct research on advanced AI and machine learning techniques.
• Publish findings and contribute to the development of new technologies.

11. Industrial Engineer

• Use data science techniques to optimize industrial processes and systems.
• Develop simulation models to predict and enhance production outcomes.

12. Fraud Analyst

• Use data analytics to detect and prevent fraudulent activities.
• Implement fraud detection models and conduct investigations.

13. Customer Insights Analyst

• Analyze customer data to understand behaviors and preferences.
• Provide insights to improve customer experience and drive engagement.

14. Supply Chain Analyst

• Optimize supply chain operations using data analysis.
• Predict demand and manage inventory levels effectively.

These roles cut across various sectors including but not limited to: technology, finance, healthcare, manufacturing, retail, and more, reflecting the versatility and high demand for professionals with expertise in data science and industrial analytics.