Teaching
My teaching philosophy is led by the simple idea of continually maximising student engagement.
I have had the delight of developing and teaching mathematical, statistical, and programming content for undergraduate and postgraduate mathematics, computing, and data analystics students.
The following are my qualifications and places where I have had the opportunity to contribute to students' learning, together with some teaching materials.
You can also see my teaching statement for a detailed account of my teaching engagements, peer observations, and student feedback.
Qualifications:
- Fellow of the Higher Education Academy and Postgraduate Certificate for Teaching in Higher Education, Jun 2019
- Associate Fellow of the Higher Education Academy, Feb 2018
- Learning and Teaching in Higher Education Certificate 1 and 2, Oct 2015
Experiences:
University of Greenwich
For 2019-22 academic years, I developed and have been delivering the modules listed below.
During 2019-20 academic year I was also pleased to have been nominated by our capable students for the Extra Mile and Faculty Star categories in the
Student Led Teaching Awards.
- MATH1172 Vector Calculus and Number Theory (Level 5 Mathematics BSc, term 2), Slides, Exercises
This course builds on level four studies and enables students to explore and develop an understanding of modern applications of vector calculus in science and engineering.
Furthermore, it teaching students the key classical techniques and results in number theory and increases their awareness of contemporary developments in number theory.
"I have found the module engaging and have felt the content has been delivered well.
I like that we are engaged more during lectures than some of our other modules. Proud of you."
- MATH1180 Computational Methods and Numerical Techniques (Level 5 Mathematics and Computing BSc, term 2), Slides
I will be responsible for developing probability and statistics for this module.
This course aims to provide computer science students with the knowledge and understanding of mathematical methods, numerical, and statistical techniques required to solve problems and analyse data throughout their undergraduate studies as well as in their further studies of computer science or in the work place.
"Very nice lecture. Modern and young approach."
- MATH1185 Vectors and Matrices (Level 4 Mathematics BSc, term 1), Slides
This module aims to introduce students to fundamental ideas and techniques in Linear Algebra.
The materials will make students proficient in computation, emphasise the applications of linear algebra, and incorporate the usage of programming packages including R and Python in performing the computations.
"The presentations are available to us before the lectures,
allowing us to make notes beforehand and give us extra revision time to learn and enhance knowledge on the week's topics.
Also, as a visual learner, being able to see the PowerPoint next to me and follow through at my own pace if necessary is a great help."
- MATH1166 Problem Solving and Mathematical Thinking (Level 4 Mathematics BSc, term 1), Slides
I will be responsible for developing the programming with Python for this module.
This course will introduce the computer skills necessary for professional mathematicians; logical skills; mathematical modelling; problem-solving in the area of mechanics; mathematical modes of thinking around problem-solving and proof; and communication and teamwork skills.
"Based on people that I communicate there are some comments on MATH1166 module. The main strength of your lecture people tend to say is
your communication with the audience and use of mentimeter. Students feel fully engaged and interested, as it not only "dry" theory, but practice for students as well. The
usage of examples in the lectures and application of them, that lecturer do, makes theory
more understandable."
- Data Analytics Software Development
via RShiny and RMarkdown (Skills Week Workshop), Slides
This is an extra curriculum event for enhancing students industrial programming and data scienece skills.
Abstract: If you are considering a career in exciting areas of data science and software development, this is an event for you.
The programming language R was developed around 1993 and has become a powerful tool used by statistician for data analysis.
Recently two packages of R Shiny and Markdown have revolutionised the way for developing data analytics applications in an easy and convenient manner with minimal amount of general programming technicalities.
RShiny and RMarkdown are currently widely used in industry to develop ah-hoc applications for capture, visualisation, and analysis of data.
In this session I will show two applications developed in order to solve problems in the industry and then through exercises show how to create basic RShiny applications.
Learning Outcome: By the end of the session students will learn how to use the Shiny package to create basic statistical applications.
Prerequisites: Some knowledge of programming, particularly R.
"I think Kayvan is great at making data look pretty, I knew this when I took his module last year but now I get the chance to learn how he is doing it and replicate it myself."
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Oxford Brooke's University
As a teaching fellow I lead the following modules for 2018-19 academic year.
I was also involved in the delivery of the MSc in Data Analytics for Government, teaching students in Oxford Brookes Univeristy as well as in ONS data science campus.
- P08802 Survey Fundamentals (Level 7 Data Analytics MSc, term 2), Slides
The aim of the module is to provide an overview of sampling and estimation fundamentals.
This course was also delivered at the Office for National Statistics for the professional data scientists in a week.
- P08803 Statistical Programming (Level 7 Data Analytics MSc, term 1), Some useful R demos Rpub
The aim of the module is to introduce core programming techniques in R essential for performing data manipulation, data processing and data analyses of traditional and alternative data sources through practical sessions.
More advanced programming techniques for example software development with RShiny and interactive reports with RMarkdown as well as and Web scraping are briefly visited.
- U08631 Numerical Analysis I (Level 5 Mathematics BSc, term 2)
The module aims to be an introduction to concepts of numerical analysis and numerical problem solving using software packages. The module is a prerequisite for later modules which involve more specialised aspects of the subject.
- U08687: Modelling with MATLAB (Level 6 Mathematics BSc, term 1)
This module introduces core programming techniques in MATLAB.
- U08424 Quantitative Research Methods (level 5 Mathematics BSc, term 1)
This module introduces analytical methods related to both survey data and experimental designs, leading to independent or repeated samples data, advantages and disadvantages. Analysis of these data using parametric and non-parametric techniques in SPSS.
- P08800 Statistics in Government (Level 7 Data Analytics MSc, term 2)
The aim of this module is to provide a sound overview of the issues and challenges for Official Statistics in the UK.
I also offered and supervised final year student projects in statistics, R programming, and pure mathematics.
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University of Exeter
Previously, I held various teaching assistant roles from Sep 2012 - Mar 2018.
I was engaged with the tutorials and assessment for many modules including Analysis, Differential Equations, Linear Algebra, Vector Calculus and Applications, and Mathematics for Natural Sciences.
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