MS (Data Science)
This program equips students to transform data into actionable insights that enable one to make complex business decisions. Students will able to process large and complex data sets through computational, statistical, and machine learning techniques. This program will provide exposure to the latest trends and technologies in this field. Thus, producing the man power to fuel national and international emerging market of data science products.
Registration in “MS Thesis/Project– I” is allowed provided the student has:
- Degree in relevant subject of Science or Engineering, earned from a recognized university after 16 years of education AND
- At least 60% marks or CGPA of at least 2.0(on a scale of 4.0).
- Past Academic Record (Bachelor): 50%
- Performance in NU MS Admission Test: 50%
Typical course load in a semester is four courses. However, NUCES staff cannot register for more than two courses in a semester. In the second semester, a student has the option to pursue MS by undertaking either a 6 credit hour MS Thesis or Project, spread over two regular semesters.
Note 1: Applied Programming course is of No Credit (NC), but it must be passed.
Note 2: Registration in “MS Thesis - I” is allowed provided the student has:
- Earned at least 15 credits
- Passed the “Research Methodology” course
- CGPA is equal to or more than 2.5
- DS 5001 Advance Big Data Analytics
- DS 5006 Deep Learning
- DS 5007 Natural Language Processing
- DS 5005 Distributed Data Processing
Program Educational Objectives (PEO)
- To produce computer scientists who fulfil the requirements of the national and international market of data science products.
- To equip students to transform data into actionable insights that enable them to make complex business decisions.
- To enable students to apply computational, statistical, and machine learning techniques to process large and complex data sets.
- To enable students to conceive and execute data science projects.
Program Learning Outcomes (PLOs)
- To equip students to transform data into actionable insights to make complex business decisions.
- To enable students, understand and analyze a problem and arrive at computable solutions.
- To expose students to the set of technologies that match those solutions.
- To gain hands-on experience on data-centric tools for statistical analysis, visualization, and big data applications at the same rigorous scale as in a practical data science project.
- To understand the implications of handling data in terms of data security and business ethics.
- Students shall have the ability to make effective oral and written presentations on technical topics.