Introduction to Data Science and Programming

Module title	Introduction to Data Science and Programming
Module code	INT3625
Academic year	2022/3
Credits	15
Module staff

Duration: Term	1	2	3
Duration: Weeks		10	2

Number students taking module (anticipated)	10

Description - summary of the module content

Module description

This module introduces you to data science and the use of programming to collate and manipulate data. The ability to extract information from data as a basis for evidence-based decision making is increasingly important in research and work-based situations. The general nature of this module makes it suitable for any international student from any discipline. The module will be essential for students planning to progress to further studies in data science or programming. You will develop basic skills in programming, using Python.

Module aims - intentions of the module

To gain awareness of the skills and significance of data science in research and the working environment. To develop initial skills in programming to be able to create, manage, interrogate, manipulate and visualise data to meet user needs. You will develop skills and confidence in using Python for relevant statistical techniques.

You will explore data visualisation techniques and their usefulness in communicating information successfully.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

1. Demonstrate a systematic understanding of straightforward programming structures.
2. Critically evaluate the effectiveness of a simple Python programme.
3. Apply standard techniques for data creation, data manipulation and data management used in data science.
4. Demonstrate the application of Python techniques for data collation, interrogation and visualisation.
5. Demonstrate the use of appropriate statistical techniques to interpret data.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

6. Accurately deploy simple established programming techniques in Python.

ILO: Personal and key skills

On successfully completing the module you will be able to...

7. Demonstrate the ability to make use of computer programming with data sets.

Syllabus plan

Introduction to data science
Data types and variable types
Programming concepts and structures e.g Input-Process-Output, Variables, Loops
Programming in Python
Data bases
Data visualisation
Statistical techniques including hypothesis testing, applying linear regression
Debug simple programmes written in Python

Learning and teaching

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
60	90

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Teaching Hours	20	Lecture
Scheduled Teaching Hours	40	Practical sessions in the computer labs
Guided Independent Study	90	Practising skills learned in computer labs by completing worksheets

Assessment

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Weekly exercise sheets/workshop activities	1 hour each	1 - 7	Verbal or written
Coursework workshops to plan how to carry out data analysis and report on findings.	2 hours each x 2	1 6	Verbal and written Peer review
Mock examination	2 hours	1,2,3,5	Written with exemplar answer on ELE

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
60	40

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Group Coursework - written report supported by relevant data	20	Project brief 1, 20 hours	3, 5, 6, 7	Written and verbal in class
Individual Coursework - written report supported by relevant data	40	Project brief 2:40 hours	2, 3, 4, 5, 6	Written and verbal individual feedback
Final Examination	40	2 hours	1, 2, 3,5	Written on formal application

Re-assessment

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Individual Coursework piece (Deferral) 60%	Resubmit coursework (Deferral)	2, 3, 4, 5, 6. 7	Next available assessment period
Final Examination (Deferral)40%	2 hours (Deferral)	1, 2, 3, 5	Next examination period
Final Examination (Referral) 100%	3 hours (Referral)	1 - 7	Next examination period

Re-assessment notes

Deferral –if you miss an assessment for reasons judged legitimate by the Mitigation Committee, the applicable assessment will normally be deferred.? See ‘Details of re-assessment’ for the form that assessment usually takes. When deferral occurs there is ordinarily no change to the overall weighting of that assessment.

Referral –if you have failed the module overall (i.e. a final overall mark of less than 40% achieved) you will be required to take a re-sit exam (open book).? Only your performance in this exam will count towards your final module grade.? A grade of 40% will be awarded if the examination is passed.

Resources

Indicative learning resources - Basic reading

Basic reading:

Downey A., (2015) Think Python ISBN: 9781491939369
Grus, J. (2019) Data Science from scratch: First principles with Python (2ndedn.) ISBN: 9781492041139
McKinney, W. (2022) Pythons for Data Analysis; wrangling with Pythons, NumPy and JuPyter (3rdedn.)

ISBN 9781098104030

Indicative learning resources - Web based and electronic resources

Web-based and electronic resources:

ELE – College to provide hyperlink to appropriate pages
Python for beginners https://www.python.org/about/gettingstarted/

Module has an active ELE page

Key words search

Python, Data science, data visualisation, data bases Computer programming

Credit value	15
Module ECTS	7.5
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	6
Available as distance learning?	Yes
Origin date	01/05/2022
Last revision date	06/09/2022