NUST eLearning

This course will prepare students for advanced research by examining how to plan, conduct and report on empirical investigations with an emphasis on data science within an application domain (e.g., NLP, Finance, Healthcare, Agriculture, and Telecommunications). It will also introduce students to practical but scientific research work, and to encourage independent academic and/or commercial research among students. The overall outcome is to develop novel solutions to data science-related problems and communicate the findings (concepts, designs and techniques) effectively and professionally in accordance with NUST requirements.

Students have to submit three (3) Progress Reports during the year of Thesis, using the link provided.

Due dates are: 17 April, 17 July and 13 November  for Report No1, No2 and No3 respectively.

This course introduces the students to Mathematical modelling process from formulation to solution. Specifically, it takes the students through essential aspects of Modeling Change, Modelling using Proportionality and Geometric Similarity techniques, Model Fitting and Experimental Modelling.

This course is designed for students pursuing a career in Land Management and related fields. There is no doubt that to be a reliable Land Manager (in whatever aspect of it), basic competencies in Mathematics and Statistics are essential. This course, therefore, deals with those crucial areas of Mathematics and Statistics that are applicable to efficient Land Management and Spatial Science skills.

Today, computers are used in almost all fields of human endeavor wherever data are collected and

analysed. For this reason, certain mathematical topics related to the computer and information

sciences are now being widely studied. To this end, topics covered in this course include the binary

number system, relations and logic circuits, set theory and relations, Boolean algebra and logic gates, combinatorial Analysis.