DATA SCIENCE PRINCIPLES AND PRACTICES - 2023/4

Module code: COMM054

Module Overview

The module provides for coverage of a variety of statistical methods, including descriptive statistics and validating formulated hypotheses, as well as predictive analytics. The computational foundations and methods of importance to data science are also covered, along with consideration for relevant supporting software and tools, and the need for data science governance.

Module provider

Computer Science

THORNE Tom (CS & EE)

Module cap (Maximum number of students): N/A

Independent Learning Hours: 106

Lecture Hours: 22

Tutorial Hours: 11

Laboratory Hours: 11

Semester 1

Prerequisites / Co-requisites

Co-requisite: COMM055, Machine Learning and Data Mining

Module content

The module includes:

• An introduction to machine learning, covering the basic concepts of Machine Learning and Data Science, and supervised and unsupervised learning problems.

• Edison descriptors KU1.02.01, KU1.02.02, KU1.02.03, KU1.02.05

• The basics of probability theory necessary for data science, covering discrete and continuous random variables and common probability distributions used.

• Edison descriptors KU1.01.01, KU1.01.05

• Fundamentals of hypothesis testing, and consideration of multiple testing.

• Edison descriptors KU1.01.01, KU1.01.02, KU1.01.04

• Statistical inference for statistical models, applying maximum likelihood approaches.

• Edison descriptors KU1.01.01-05

• Bayesian statistics, Bayesian inference, and example applications.

• Edison descriptors KU1.01.01-05

• Linear regression and validation, including the bootstrap and crossvalidation.

• Edison descriptors KU1.01.01-05, KU1.05.04

• Python programming and Python libraries for data science.

• Edison skills DSDALANG02, DSVIZ01

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework INDIVIDUAL COURSEWORK 30
Examination 2 HOUR INVIGILATED EXAM 70

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate the ability to critically appreciate and apply statistical methods. Thus, the summative assessment for this module consists of:

• A coursework project evaluating the practical application of LOs 1-3.

• An examination, evaluating all learning objectives, with respect to both principles and practices of Data Science Principles and Practices

Formative assessment - Students will be guided to work on weekly tasks through lab exercises, the solutions to which will provide for feedback on understanding and practice, which will feed forward into the class test and the exam.

Module aims

• This module aims to introduce students to the necessary background material in statistics and probability that underlie modern data science and machine learning, with applications to real world problems, and to provide students with practical experience in working with these tools.

Learning outcomes

 Attributes Developed 001 Understand the different classes of data science and machine learning problems, and be able to explain which class a problem belongs to. Edison skill SDSDA01 KCPT 002 Understand and apply probability theory as it relates to data science, including classes of random variables, independence, conditional probability and common probability distribution. Edison skill SDSDA02 KCPT 003 Choose and execute standard methods from existing Python statistical libraries to analyse data and visualise results. Edison skills DSDALANG02, SDSDA10, DSVIZ01 CPT 004 Be able to explain the difference between Bayesian and frequentist approaches, and interpret the output of a Bayesian data analysis. Edison skill SDSDA02 CPT 005 Apply linear regression and classification with appropriate validation. Edison skills SDSDA04, SDSDA08, SDSDA09 CPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to provide students with the knowledge, skills, and practical experience covering the module aims and learning outcomes.

The learning and teaching methods include: 11 teaching weeks with each week comprising:

2 hours of lectures, to convey and discuss the key concepts and principles

1 hour tutorial, to discuss the material covered in lectures

1 hour lab session, to put key concepts and principles into practice.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Upon accessing the reading list, please search for the module using the module code: COMM054

Other information

Digital Capabilities
This foundational maths for data science module teaches students to use computer to solve mathematical problems. This involves learning to programming and learning to apply these skills to solve technical problems.

Employability
The ability to draw meaning from large data sets is currently an area that is in high demand in industry. This module teaches the mathematical foundations to allow students to work with large and complex real world datasets. These skills are highly valuable to employers.

Global and Cultural Skills
Computer Science is a global language and the tools and languages used on this module can be used internationally. This module allows students to develop skills that will allow them to reason about and develop applications with global reach and collaborate with their peers around the world.

Resourcefulness and Resilience
This module involves practical problem-solving skills that teach a student how to work with complex and unstructured data sets. The foundational maths taught in this can be applied to a wide range of different scenarios, giving students new techniques for solving problems.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Data Science MSc 1 Compulsory A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.