2024/25 Taught Postgraduate Module Catalogue
OMAT5100M Programming for Data Science
15 creditsClass Size: 100
Module manager: Dr Jonathan Ward
Email: J.A.Ward@leeds.ac.uk
Taught: 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr), 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable
Year running 2024/25
Pre-requisite qualifications
Students are required to meet the programme entry requirements prior to studying the moduleModule replaces
N/AThis module is not approved as an Elective
Module summary
This module introduces the fundamental skills of programming in python. The aim is for students to develop the skills and experience to independently translate a broad range of data science related problems into functioning computer programs and communicate the results.Objectives
The module objective is to develop:a. Fundamental programming knowledge, including data types, control structures, functions and input-output.
b. Knowledge and understanding of programming tools and techniques for data science, such as data acquisition, plotting and the use of python packages.
The topics covered are described in text and videos, and there are examples and exercises that support learning and develop the ability to independently translate problems into python code.
Learning outcomes
On completion of this module students should be able to:
1. Apply fundamental programming concepts in python.
2. Formulate appropriate problems as algorithms and translate these into functioning python code.
3. Independently identify and assimilate new programming tools and techniques.
4. Undertake and communicate the analysis of data using python.
Skills outcomes
The following skills are developed in this module:
- Programming.
- Independent working.
- Communication in a data science context.
Syllabus
Indicative content for this module includes:
- Computer programming in Python: control structures, data-types, data structures, functions and classes, importing and using libraries/packages, implementing simple algorithms.
- Use of a Python development platform.
- Use of specific libraries/APIs providing data access and analysis functionality, such as: accessing information from the web or from databases, statistical analysis, ML algorithms, graphical display of data.
Students will undertake a sequence of programming exercises starting with the fundamentals of programming and building up to a system that performs significant data analysis on real data:
- Basic algorithms for representing and processing information.
- Importing, manipulating and displaying data.
- Implementation of a data analysis ‘pipeline’ in which data is extracted from some source, processed, analysed and visualised.
- Use of example data science software tools.
Teaching methods
| Delivery type | Number | Length hours | Student hours |
| On-line Learning | 1 | 1.50 | 1.50 |
| On-line Learning | 5 | 1.00 | 5.00 |
| Discussion forum | 6 | 2.00 | 12.00 |
| Independent online learning hours | 42.00 | ||
| Private study hours | 89.50 | ||
| Total Contact hours | 18.50 | ||
| Total hours (100hr per 10 credits) | 150.00 | ||
Opportunities for Formative Feedback
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.Two early formative assessments have been designed to enable students to develop and test basic programming skills (LO1) that they learn at the start of the module. This will build student confidence in basic programming concepts and in validating their code.
Methods of assessment
Coursework
| Assessment type | Notes | % of formal assessment |
| In-course Assessment | 20 questions | 20.00 |
| Computer Exercise | 200 lines of python code | 20.00 |
| Computer Exercise | 200-300 lines of Python code with text explanations [max 1,500 words] | 60.00 |
| Total percentage (Assessment Coursework) | 100.00 | |
Resits of any element of assessment will be available to students when the module next runs. This module will run twice in each year which limits the amount of time a student must wait for the resit.
Reading list
There is no reading list for this moduleLast updated: 24/05/2024 17:06:29
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