Teaching & Learning

Teaching & Learning

All the material for courses are available at official UMT Integrated Learning Management system at the url below. Feel free to login and download the materials. Online teaching are conducted using Cisco Webex. Extra Materials will be uploaded in the section below.

  1. Integrated Learning Management System UMT

  2. Cisco Webex

  3. Google Classroom

SEMESTER I: Geometric Modelling (MKP4300)

Please login : Oceania for materials.

  1. Create an Wolfram Cloud account for online Mathematica usage: https://www.wolframcloud.com/

  2. Do introductory programming lab at: https://www.wolfram.com/programming-lab/

  3. Read and try coding using this book: https://www.wolfram.com/language/elementary-introduction/2nd-ed/

  4. Examples Mathematica projects: Wolfram Demonstrations Project demonstrations

  5. New: Mathematica Notebooks: https://www.notebookarchive.org/


SEMESTER I: Systematic Thinking and Programming (MTK3704)

Please login : Oceania for materials.

  1. Online C++ compiler: https://www.programiz.com/cpp-programming/online-compiler/

  2. Dev C++ Installer: https://sourceforge.net/projects/orwelldevcpp/

SEMESTER II: Scientific Computation (MKP3303)

Please login : Oceania or my Github for materials.

Wolfram Mathematica for Symbolic and Numerical Computing

  1. Create a Wolfram Cloud account for online Mathematica usage: https://www.wolframcloud.com/

  2. Do introductory programming lab at: https://www.wolfram.com/language/fast-introduction-for-math-students/en/

  3. Read and try coding using this book: https://www.wolfram.com/language/elementary-introduction/2nd-ed/

  4. Examples Mathematica projects: Wolfram Demonstrations Project demonstrations

  5. New: Mathematica Notebooks: https://www.notebookarchive.org/


Python SciPy for Numerical Computing.

  1. Online Python Compiler: https://www.w3schools.com/python/python_compiler.asp

  2. Complete details on SciPy: https://scipy.org/

  3. SciPy reference notes: for the hands-on lab: https://scipy-lectures.org/

  4. Jupyter Notebook Online: https://jupyter.org/try

  5. Google Collab for Python execution with Jupyter notebook: https://colab.research.google.com/notebooks/intro.ipynb


SEMESTER I: Introduction to Data Science (MDA3003)

Please login : Oceania or my Github for materials.

Python SciPy for Numerical Computing.

  1. Online Python Compiler: https://www.w3schools.com/python/python_compiler.asp

  2. Jupyter Notebook Online: https://jupyter.org/try

  3. Google Collab for Python execution with Jupyter notebook: https://colab.research.google.com/notebooks/intro.ipynb


Data Science resources:

  1. Introduction to Data Mining by Pang-ning Tan, Michael Steinbach, Vipin Kumar

  2. Book: Data Science from Scratch First Principles with Python

  3. Github: Data Science from Scratch First Principles with Python

  4. Jesus Rogel-Salazar, Data Science and Analytics with Python, CRC Press, Year: 2017

  5. Jesus Rogel-Salazar, Advanced Data Science and Analytics with Python, CRC Press, Year: 2020

  6. online ML work by Jean de Dieu Nyandwi


SEMESTER I & II: Final Year project (MTK4998)

Undergraduate students carry out one-year project before graduating. Current projects are related to Topological Data Analysis and clustering methods.

  1. WIP

Topological Data Analysis

  1. WIP