Course image Introduction to Artificial Intelligence - MA
Mathematical Sciences

In this lecture material, you will learn the concepts of AI and be able to use these concepts practically. Also, you will explore use cases and applications of AI, and understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, and jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini-project.

Course image Numerical Algorithms (SRID)
Mathematical Sciences

The Objective of the course is to learn MATLAB skills in numerical methods, programming and graphics; apply MATLAB to Mathematical problems and obtain solutions; and to present these solutions in a coherent manner for assessment. 

The aims of this course are to introduce the elements and practicalities of computer programming through the MATLAB mathematical computing environment.

Course image Introduction to Database Systems
Mathematical Sciences

Good decisions require good information that is derived from raw facts. These raw facts are known as data. Data are likely to be managed most efficiently when they are stored in a database. In this course, you learn what a database is, what it does, and why it yields better results than other data management methods. Also, the course introduces the various types of databases and why database design is so important.

Course image Introduction to Data Science and Data Analysis
Mathematical Sciences


Data science is one of the fastest-growing disciplines at the university level. We see more job postings that require training in data science, more academic appointments in the field, and more courses offered, both online and in traditional settings. It could be argued that data science is nothing novel, but just statistics through a different lens. What matters is that we are living in an era in which the kind of problems that could be solved using data are driving a huge wave of innovations in various industries – from healthcare to education, and from finance to policy-making. More importantly, data and data analysis are playing an increasingly large role in our day-to-day life, including in our democracy. Thus, knowing the basics of data and data analysis has become a fundamental skill that everyone needs, even if they do not want to pursue a degree in computer science, statistics, or data science. 

This lecture material introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tool, Python, the lecture material offers many examples of real-life applications, with practice ranging from small to big data.


  • Introduction to Data Science
  • Data science and ethical issues 
  • Exploratory data analysis and the data science process. 
  • Feature generation and feature selection (extracting meaning from data). 
  • Three basic machine learning algorithms (Linear Regression, k-Nearest Neighbour ( -NN) classification, and k-Means clustering). 


The primary objective of this course is to introduce students to the basic concepts of data science and carry out statistical analysis and demonstrate how it can be utilised in finding solutions to scientific problems. 


At the end of the course, it is expected that students will appreciate the concepts of data science and be able to use these concepts in finding solutions to problems relating to science. 


It is assumed that the student has some background in Basic Statistics, Algebra and Computing.


The grade for the course will be based on class attendance, group homework/presentation, quizzes and a final end of term exams.

  • Attendance: All students should make it a point to attend classes. Random attendance will be taken to constitute 10% of the grade.
  • Group Homework: Two homework assignments worth 10% of the final grade. Homework will be assigned on regular basis and will be due exactly one week (before 5:00 pm) from the day the homework is issued to students
  • Group Presentation: A group presentation worth 5% of the final grade will be conducted where necessary. Students will be assigned to a group with a task to research into and present their findings in class to member.
  • Quizzes/ Class Test: Two quizzes worth 15% of the grade will be given during class. The quiz or test date will be announced one week in advance. 
  • Final End-Of-Term Exams: Final exam is worth 60% of the final grade.

Course image Programming with R and Python
Mathematical Sciences

OBJECTIVE: The primary objective of this course is to introduce students to statistical software such as R and Python and demonstrate how they can be utilised to solve scientific problems.

AIM: At the end of the course, it is expected that students will appreciate the use of statistical software and be able to use the software in finding solutions to problems relating to science.

PREREQUISITES: It is assumed that the student has some background in Basic Statistics, Algebra and Computing

Course image Numerical Methods and Scientific Computing
Mathematical Sciences

OBJECTIVE: The course is designed to help students develop a fundamental understanding to computer programming through the use of programming language.

AIM: At the end of the course, it is expected that students will understand the concept behind flowcharts and algorithms and be able to apply them in finding solutions to problems in numerical methods. 

PREREQUISITES: It is assumed that the student has some background in Computing and Numerical Methods.