Master of Science [M.Sc] [Applied Statistics and Datamining] From University of St Andrews

Academic Eligibility:

• Students should have a bachelor's degree from a recognized institution or university.
• International Baccalaureate (IB): 36 (HL 6,5,5) to 38 (HL 6,6,6 plus SL 6,6,6).
• Students must be qualified in IELTS or TOEFL and PTE to get admission to this program.
• University of St Andrews does not require the GMAT or the GRE.

Indian Student Eligibility:

Postgraduate candidates will be expected to hold a good second division (2.1) or first division UK Bachelor's degree with Honours, or the overseas equivalent, in a relevant subject, or a relevant postgraduate qualification.

• Typical Indian degree equivalents with first-class grades of 60% to 80% (minimum) or 7.0-9.0 CGPA, depending on the institution attended, include:
  • Bachelor of Commerce (B.Com/BComm/B.Comm.) or Bachelor of Commerce and Administration (BCA) awarded from a recognised Higher Education, Professional or Specialist institution.
  • Bachelor of Engineering/Technology, when awarded from an institute of national importance, centre of advanced study, an Indian Institute of Technology (IIT), or the Indian Institute of Science in Bangalore.
  • Bachelor of Science Engineering/Engineering/Technology, when awarded from a central university, a recognised state university, or a regional Engineering college.
    Indian Institute of Engineers examinations A and B.

In the course of this programme students will develop programme-specific skills.

• Demonstrate original thought
• Construct a coherent argument or debate by demonstrating logical processing of (complex) information and deductive reasoning
• Apply critical analysis, evaluation and synthesis to solve complex problems
• Test hypotheses, theories, methods and evidence within their proper contexts
• Reason from the particular to the general
• Identify relevant techniques and concepts to solve advanced and complex problems
• Demonstrate use of an appropriate range of resources to the task at hand
• Evaluate relevant best practices for the task at hand
• Engage directly with current research, developments and skills in the discipline
• Engage with primary and secondary material and differentiate between them
• Demonstrate active learning
• Demonstrate reflective learning, including the ability to engage with and learn from feedback
• Demonstrate creativity and curiosity
• Demonstrate independence of thought and reasoning
• Demonstrate skills in time management, self-discipline and self-motivation
• Demonstrate skills in close textual and comparative analysis
• Demonstrate skills in close analysis of visual material
• Demonstrate advanced IT skills
• Demonstrate quantitative and methods of analysis
• Demonstrate expertise in the use of statistical software packages for recording, manipulation and analysis of data
• Convey statistical results and methods in a manner understandable to the lay-person via written or oral reports
• Work independently
• Work as part of a team
• Communicate with clarity and accuracy, orally (including presentation) and in writing
• Engage with the views and opinions of others
• Present work and findings in a professional manner, with attention to detail

          Learn and use research skills