MS Statistics & Scientific Computing

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Department
Mathematics
Level
Postgraduate
Colleges
College of Computer Sciences & Information Systems

The MS programs in Statistics & Scientific Computing develop rigorous foundational mathematical and statistical tools that help in careers as researchers, and solution providers. It prepares students for careers in research, applications, and teaching. Students choose courses from two areas of concentration for their course work: Statistics and Computations. Students are required to qualify successfully for 9 courses (5 compulsory and 4 electives) each of 3 credit hours duration. On successful completion of MS course work students will be allowed to work on a 6 credit hour thesis on a subject of their interest depending on the availability of the faculty. Students will be required to qualify for the Final (Comprehensive) Examination, as well as write and defend a thesis. The MS Program takes usually two years to complete and students must pass the GRE/NTS exam prior to completion of the degree.

MS Statistics & Scientific Computing students learn to:

  • Develop a thorough understanding of statistical methodology before going to apply statistical skills to solve real-life problems

  • Apply rigorous statistical techniques used to handle data to get meaningful results

  • Select and transform data to increase usefulness for solving particular problems

  • Create information visualizations for data exploration and presentation

  • Establish and understand a connection between the techniques of data analysis and scientific computing and their link with the real-life data

Eligibility

16 Years of education in Computer Science, Engineering, Mathematics, Statistics or any other relevant field. Minimum CGPA of 2.5 (on a scale of 4.0).

Program Requirements

MS requires completion of course work and dissertation/thesis. Minimum duration is 2 years and the maximum is 4 years:

  • MS course work requirements consist of six graduate-level courses (27 credit hours)

  • On completion of the dissertation/thesis, the student is awarded 33 credits

    A MS student must additionally complete the following requirements:

  • MS Proposal/Synopsis Development

  • MS Proposal/Synopsis Defense

  • BASR Approval of MS Proposal/Synopsis

  • Continuous enrollment in supervised research courses for meeting the full-time residency requirements

  • Completion of MS Dissertation/Thesis

  • Selection of External Evaluators by BASR

  • Evaluation of MS Dissertation by two external faculty members as per HEC criteria

  • Dissertation/Thesis Finalization

  • Open defense of MS dissertation

  • Any other HEC requirement

  • Final Dissertation/Thesis Submission to BASR

Learning Outcomes

  1. Knowledge of how to apply statistical and scientific computing techniques and algorithms to real-life data sets to extract meaningful insights.
  2. Acquisition of hands-on experience with relevant software tools, languages, data models, and environments for data processing.
  3. Ability to communicate results of analysis effectively (visually and verbally) to a broad audience in the fields of biology, environment, finance and risk management, data science, business management, and other disciplines.

Career Options

  1. Big Data Analyst
  2. Budget Analyst
  3. Business Metrics Analyst
  4. Economist
  5. Financial Analyst
  6. Operations research analyst

Interested in this program? Visit the admissions page for more information.

Required Courses

Compulsory Courses (15 credit hours)

MSS609 Advanced Research Methodology
MSS611 Advanced Statistical Inference
MSS614 Mathematical Statistics
MSS617 Advanced Numerical Computing
MSS618 Statistical Modeling & Computing

 

Elective Courses

Statistics Concentration (6 credit hours)

MSS647 Advanced Design of Experiments
MSS648  Time Series Analysis
MSS649 Stochastic Processes
MSS650 Applied Regression Models
MSS651 Theory & Practice of Forecasting Statistical
MSS652 Quality Control

Computer Concentration (6 credit hours)

MSS622 Fundamental of Algorithms
MSS635 Information Retrieval & Data Mining
MSS645 Decision Theory
MSS657 Machine Learning
MSS658 Pattern Recognition
MSS661 Simulation & Modeling

Thesis

MSS691 Thesis I
MSS692 Thesis II

 

 

Course Structure

Semester One Semester Two Semester Three Semester Four
Mathematical Statistics
Advanced Numerical Analysis
Advanced Numerical Computing
Statistical Modeling & Computing
Advanced Statistical Inference
Statistics Concentration I
Statistics Concentration II
Computation Concentration I
Thesis I
 Computation Concentration II
Thesis II
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