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This is an archived syllabus from 2020-2021

COMP13212 Data Science syllabus 2020-2021

COMP13212 Data Science

Level 1
Credits: 10
Enrolled students: 418

Course leader: Jonathan Shapiro

Additional staff: view all staff

Assessment methods

  • 80% Written exam
  • 20% Practical skills assessment
Sem 2 ONLINE Lecture Fri 10:00 - 11:00 -
Sem 2 ONLINE Lecture Wed 11:00 - 12:00 -
Sem 2 B ONLINE LabORATORY Wed 09:00 - 11:00 Z
Sem 2 B ONLINE LabORATORY Fri 11:00 - 13:00 X
Sem 2 B ONLINE LabORATORY Thu 11:00 - 13:00 M+W
Sem 2 B ONLINE LabORATORY Fri 15:00 - 17:00 Y


This course unit is about extracting knowledge and information from data. Topics include: measuring uncertainty in data, and interpreting data using visualisation, statistical methods, probabilistic Bayesian methods, and basic machine learning techniques. Students will also gain an introduction to the Jupyter notebook, and techniques in Python to implement data science methods.

This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact.  Please see Blackboard / course unit related emails for any further updates.


To give students awareness of the elements of the “The Data Science Process”. Many of the elements of this process will be studied in finer detail, although not all.
To give students practice using python tools for data processing and analysis, through practical computer laboratory exercises.  Tools include: numpy, scipy.stats, pandas, and Jupyter notebooks, 
To demonstrate methods for exploring and visualising data, and give students practice in using these methods.
To give students understanding of uncertainty in data, in particular, methods for measuring uncertainty, and when to use appropriate measures.
To give students an introduction to statistical thinking and Bayesian reasoning.
To give students an introduction to ethical considerations when analysing data and drawing responsible conclusions.
To give a brief introduction to concepts from machine learning, including: classification/regression, overfitting/underfitting, the need for independent testing data, and cross-validation, including leave-one-out validation. 
To demonstrate some practical application of basic machine learning methods, including the Bayesian classifier, the naive Bayes classification, linear regression, and logistic regression.

Teaching methods

Lectures and coursework reported via Jupyter notebooks in Python.

Study hours

  • Lectures (22 hours)
  • Practical classes & workshops (12 hours)

Learning outcomes

On successful completion of this unit, a student will be able to:

  • Demonstrate awareness of the “Data Science Process” by describing qualitatively how it would apply in a given situation. 
  • Demonstrate awareness of need for data cleaning descriptively and by doing elementary data cleaning and preparation in the laboratory.  
  • Demonstrate ability to measure and express uncertainty from a set of data and quantities derived from that data. 
  • Demonstrate ability to choose and build appropriate models of different datasets. 
  • Demonstrate ability to evaluate the quality of a model of a dataset. 
  • Demonstrate the ability compare different models of a dataset and models of different dataset in order to draw statistically sound conclusions about hypotheses or claims from the data. 
  • Demonstrate ability to use python tools to: read and write data sets to and from files, produce descriptive statistics and draw conclusions from these, produce graphical visualisation and draw conclusions, perform basic statistical tests including the difference between means, and perform a simple machine learning experiment by building an email spam filter using a naive Bayes classifier.

Reading list

A first course in machine learning [electronic resource] Rogers, Simon,9781498738545 (PDF ebook) :; 9781498738576 (ePub ebook) :; 9781498738569 (ePub ebook) :Chapman & Hall/CRC2016.
Measurements and their uncertainties : a practical guide to modern error analysis Hughes, Ifan,019956633X; 9780199566334; 0191576565; 9780191576560Oxford University Press2010.