Code and reports submitted for the Master of Data Science course (2022-2023, University of Durham) All the working materials have been uploaded to the Durham University database and are protected by copyright.
This project is distributed under the MIT License.
When using or redistributing this work, please retain the following information:
- Original Author: Yan Lin
- Institution: Durham University
- This licence statement
ML-Python.ipynb, ML-R.R and Machine Learning.pdf
Received mark: 83. Overall mark: 90. First in my cohort.
Critical Perspective Assignment Yan.pdf
Received mark: 76. First in my cohort, Examples of the next cohort.
Programming.ipynb and Programming_Report.pdf
Received mark: 82.5. 'The visualization task in question 3 is perfect, as is the Bayesian network in question 4'
DEVUL-Assignment1-Code.R and DEVUL-Report1.pdf ,
DEVUL_Assignment2_Code.R and Assignment2-VULA.pdf
Received mark: 85.
ICS_Coursework.ipynb
Received mark: 76. 'There are essentially no deductions for the code section'
ISDS_Assignment2.R and ISDS_Assignment 2(Yan Lin).pdf
Received mark: 74. 'A very clear and proven report'
2.5 hours online exam mark: 98%
IMDS_group_project_python.ipynb and IMDS_group_project_report.pdf Received mark: 78% & 73%
Average score in courses other than dissertation: 78%
| Module Title | Credits | Mark |
|---|---|---|
| Introduction to Mathematics for Data Science | 15 | 92% |
| Introduction to Computer Science | 15 | 76% |
| Introduction to Statistics for Data Science | 15 | 82% |
| Strategic Leadership | 15 | 70% |
| Ethics and Bias in Data Science | 15 | 54% |
| Programming for Data Science | 15 | 83% |
| Machine Learning | 15 | 90% |
| Data Exploration, Visualization and Unsupervised Learning | 15 | 80% |
| Critical Perspectives in Data Science | 15 | 76% |