COMP13212
Data Science
10
Semester 2
Undergraduate
Level 1
Ainur Begalinova
Unit coordinator
1
FHEQ level (Framework for Higher Education Qualifications) ' First part HE study/Bachelors '
European Credit Transfer & Accumulation System Rating : 5.0
<p><span style="font-size:14px;">This course unit has two objectives. The first is to introduce the student to a range of fundamental, non-trivial algotithms, and to the techniques required to analyse their correctness and running-time.</span></p><p><span style="font-size:14px;">The second is to present a conceptual framework for analysing the intrinsic complexity of computational problems, which abstracts away from details of particular algorithms.</span></p>
<p style="color: rgb(34, 34, 34); font-family: Arial,Verdana,sans-serif; font-size: 12px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">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.</p><p style="color: rgb(34, 34, 34); font-family: Arial,Verdana,sans-serif; font-size: 12px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">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.</p>
<div> •<span style="white-space:pre"> </span>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.</div><div> •<span style="white-space:pre"> </span>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, </div><div> •<span style="white-space:pre"> </span>To demonstrate methods for exploring and visualising data, and give students practice in using these methods.</div><div> •<span style="white-space:pre"> </span>To give students understanding of uncertainty in data, in particular, methods for measuring uncertainty, and when to use appropriate measures.</div><div> •<span style="white-space:pre"> </span>To give students an introduction to statistical thinking and Bayesian reasoning.</div><div> •<span style="white-space:pre"> </span>To give students an introduction to ethical considerations when analysing data and drawing responsible conclusions.</div><div> •<span style="white-space:pre"> </span>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. </div><div> •<span style="white-space:pre"> </span>To demonstrate some practical application of basic machine learning methods, including the Bayesian classifier, the naive Bayes classification, linear regression, and logistic regression.</div><div> </div>
<ul> <li> <span style="font-size:14px;">Demonstrate awareness of the “Data Science Process” by describing qualitatively how it would apply in a given situation. </span></li> <li> <span style="font-size:14px;">Demonstrate awareness of need for data cleaning descriptively and by doing elementary data cleaning and preparation in the laboratory. </span></li> <li> <span style="font-size:14px;">Demonstrate ability to measure and express uncertainty from a set of data and quantities derived from that data. </span></li> <li> <span style="font-size:14px;">Demonstrate ability to choose and build appropriate models of different datasets. </span></li> <li> <span style="font-size:14px;">Demonstrate ability to evaluate the quality of a model of a dataset. </span></li> <li> <span style="font-size:14px;">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. </span></li> <li> <span style="font-size:14px;">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.</span></li></ul><p> </p>
<p><span style="font-size:14px;">Lectures and coursework reported via Jupyter notebooks in Python.</span></p>
1
Written exam
80%
8
Practical skills assessment
20%
Y
<p dir="ltr"><span style="font-size:14px;">To be determined</span></p><div> </div>
Lectures
22
Practical classes & workshops
12
0
66