module specification

CC5064 - Programming with Data (2025/26)

Module specification Module approved to run in 2025/26
Module title Programming with Data
Module level Intermediate (05)
Credit rating for module 15
School School of Computing and Digital Media
Total study hours 150
 
69 hours Guided independent study
36 hours Scheduled learning & teaching activities
45 hours Assessment Preparation / Delivery
Assessment components
Type Weighting Qualifying mark Description
Coursework 70% 40 Coursework - apply programming techniques in a real-world business problem (3000 words report with evidence of artefacts
In-Course Test 30% 40 MCQ assessment
Running in 2025/26

(Please note that module timeslots are subject to change)
Period Campus Day Time Module Leader
Autumn semester North Tuesday Afternoon

Module summary

The module is designed to introduce data programming through various programming concepts related to data. The module covers data structures, selection, iteration, data input and output with error handling. In particular the module focuses on creating data science solutions for business applications. Programming language Python is integrated into the module practical element to prepare, analyse, process and present data science solutions.

1. Students will gain knowledge and skills of the principles of data programming, design and coding.

2. Develop programming skills for data manipulation and presentation.

3. Develop skills to create data solutions for business applications using programming.

4. Enhance skills for logical reasoning, problem solving and evaluation.

Prior learning requirements

Pre-requisites for the module:

CS4051 Fundamentals of Computing.

Available for Study Abroad? YES.

Syllabus

· Programming principles for data processing (e.g. Python: NumPy, Panda)

· Sequence, selection, iteration, Data structures, files, databases, error and exception handling, search, sort, recursion (LO1, LO4)

· Programming for data preparation, manipulation presentation (e.g. Python: Plotly Matplotlib, geopandas for geo-spatial data) (LO2, LO4)

· Data loading, storing, transformation, visualisation

· Programming for Data analysis (e.g. Python: Scikit, Statsmodels):

· Descriptive (e.g. summarisation, clustering, association) and predictive statistics (e.g. classification and regression). (LO3, LO4)

Balance of independent study and scheduled teaching activity

Topics will be introduced through the medium of formal lectures, supported by tutorial and workshop sessions, and blended learning as follows:

- Lecture (1 hour / week): Introduction of the major topics identified in the syllabus, plus practical exercises, directed reading and other further studies

- Workshop (2 hour / week): Programming technical skills will be further developed through lab-based workshops. Specific practical exercises are set to support students and development of skills with relevant libraries.

- Blended learning:

Using the University’s VLE and online tools to provide deliver content, assessment and feedback, to encourage active learning, and to enhance student engagement and learning experience.

Students will be expected and encouraged to produce reflective commentaries on the learning activities and tasks that they carry out to complete their work.

Learning outcomes

[LO1] Demonstrate a sound understanding of programming principles for data processing.

[LO2] Present sufficient level of proficiency in problem identification, data preparation, manipulation and presentation in the context of defined data science scenario.

[LO3] Create well-constructed programmes with data analysis for business cases showing the judgment in the selection of libraries and techniques.

[LO4] Evidence logical reasoning, problem solving, and evaluation skills needed for high quality solutions.

Bibliography