Numerical Analysis was the third module for my Master of Science. I’ve always enjoyed maths and programming, so I was looking forward to this module, and I wasn’t disappointed!
Here are the main concepts I’ve learned.
I have used SQL a lot in my career in international schools to manage data, and R seems like a natural next step - I am enjoying using it. I previously completed a course called “Introduction to Inferential Statistics”, so I already had a good basic knowledge of statistics tests - this really helped me with many of the concepts in this module.
Binomial regression was a new concept for me, and I found this a bit of a challenge to understand at first, but the final assignment exploring the Pima diabetes dataset helped me use and clarify the concepts of binomial regression.
I enjoyed the discussion forum about improving a table of data - it was great to interact with other students in this way. I commented on several of my peers’ posts, and it was very useful to get feedback from them to improve my initial post.
The seminars were another great way to interact with other students and the tutor. We had some excellent discussions about interpreting test results, and the tutor was very kind and helpful.
I think my favourite part of the module was the article we read called Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. This was a very sensible and objective discussion of what p-values and confidence intervals really mean, and they can be used well - I definitely want to use this in my future career as a data scientist!
| SKILLS MATRIX (0 low, 10 high) | ||
|---|---|---|
| learning outcome | proficiency | interest |
| Develop a systematic understanding of foundational mathematical principles and methods, as well as core and specialised concepts underpinning computing logic. | 8 | 10 |
| Understand the foundation for the development and application of programming and data-driven techniques, from both a theoretical and practical viewpoint. | 8 | 10 |
| Facilitate the ability to interpret the results generated when using these data science and ai tools. | 7 | 8 |
| Gain an understanding of the real-world applications of these computational tools, and contemporary issues related to these computational techniques. | 7 | 8 |
| The opportunity to take a reflective and independent approach to the learning process. | 8 | 9 |
| Demonstrate systematic understanding of the key mathematical and statistical concepts and techniques which underpin mechanisms in Data Science and AI. | 8 | 9 |
| Apply mathematical and statistical methods in these fields to help in the decision-making process. | 7 | 8 |
| Critically evaluate the use of statistical analysis and the numeric interpretation of results as aids in the decision-making process. | 7 | 8 |
| Critically appraise and present results of a statistical analysis to a diverse audience. | 8 | 8 |
I have made many notes about the most useful R functions, particularly for making graphs. These notes have helped me with many tasks during the module.
The many different kinds of statistical tests were very useful to know when I was working on the final assignment exploring the Pima diabetes dataset. The assignment itself helped to clarify the uses and assumptions of these different tests.
The article about the sensible use of p-values and confidence intervals was excellent, and so were the text books. I found R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists particularly helpful, and I made a lot of notes on the techniques used in the book. The section on defining subsets of a dataset has been particularly useful.
My goal in the next 6 months is to start a career in data science. After learning and applying skills in R programming and statistical tests, I feel much more ready for data science work.
I’m very glad I kept notes on the R functions I found most useful. As I made more notes, I used them more and more in the tasks in this module, and I’m certain I’ll find my notes invaluable for data science work.
Thanks to Dr Tahir for all your encouragement, and for making the seminars interesting and interactive. I’m very glad you have been our tutor for this module!