Unit 24 Advanced Programming for Data Analysis (H/618/5723) Assignment Brief 2026

Unit 24 Advanced Programming for Data Analysis Assignment Brief

Introduction

The world of programming and software engineering is vast and includes many occupational pathways to pursue. Most areas of modern computing involve some form of data analysis. These range from enhanced reality development through to robotic control and communication systems, to medical imaging machines. All of these require significant management of data but the area with the most common requirements is in data analysis and manipulation for business intelligence. An analyst’s role is becoming increasingly complex. Experienced analysts use modelling and predictive analytics techniques to generate useful insights and actions, which they present to interested parties and decision makers in an appropriate, clearly understood way.

This unit is designed to develop the skills required to become a skilled data analyst. It includes investigation of a range of different programming languages, aimed at both data analytics and general use, good development guidelines and the design, development and testing of a sizeable tool to analyse and utilise a large data set.

These skills are especially relevant to today’s data analyst, data scientist, social researcher, market researcher and others who utilise large data sets in their work.

Learning Outcomes

By the end of this unit, students will be able to:

LO1 Explore the tools a programmer can use to manipulate large data sets for  data analysis

LO2 Design a software tool to analyse a large data set for a given scenario

LO3 Develop a software tool to analyse a large data set for a given scenario

LO4 Test a software tool used to analyse a large data set for quality of information produced.

Essential Content

LO1 Explore the tools a programmer can use to manipulate large data sets for data analysis

Data analysis languages:

Explore data analysis languages, e.g. R, SAS, SQL, Julia, Matlab.

General programming languages:

Explore general programming languages: C++, C, C#, Java, F#, Visual Basic, Python.

Identify interaction methods, R.Net, linking at runtime, direct manipulation of objects.

Proposal:

What dataset will be used, the language to be used, what outcomes are to be achieved and the method of interrogating and analysing the dataset.

Good coding techniques:

Simple design, e.g. keeping configurable data at high levels, consistency in methods, meaningful variable and constant name.

Create small functions and procedures by including single action, minimal parameters, descriptive names, comments to explain code functions and variables clearly.

Structure source code logically, declare local variables close to usage and keep lines short. Keep global variables together with comments on function and where used.

Develop objects and data structures for one action so that they are small.

Design tests to ensure they are readable, effective and test boundary conditions too.

Understand bad test design, e.g. over complex, repetitive, miss conditions.

Large datasets:

Investigate the availability of large public domain and other datasets suitable for use with your software tool, data.NASA.gov, data.gov.uk, etc.

LO2 Design a software tool to analyse a large data set for a given scenario

Software design:

Design to include details of acquisition, cleaning and analysis of digital data.

Dataset operations:

Use of operations in application development, e.g. hash functions and pointers, sorts, e.g. insertion, quick, merge and heap, searches, e.g. linear, binary tree and recursive. acquisition, cleaning and analysis of digital data.

Data analysis methods:

Apply an appropriate range of data analysis methods.

Qualitative methods, e.g. content analysis.

Quantitative analysis methods e.g. standard deviation, frequency, range and average and hypothesis testing and descriptive analysis.

Specific descriptive analysis techniques e.g. regression analysis, factor analysis, dispersion analysis, discriminant analysis and time series analysis.

LO3 Develop a software tool to analyse a large data set for a given scenario

Implementation:

Utilise an appropriate language and development tools.

Produce good quality program code that implements a design for a data analysis software tool.

LO4 Test a software tool used to analyse a large data set for quality of information produced

Types of testing:

Understand the uses of unit testing and integration testing of main application.

Understand the meaning of data driven capabilities, debugging and logging capabilities, platform independence, extensibility and customisability, email notifications, version control friendly.

Assessing effectiveness of the data analysis:

Evaluate how effective the data analysis tool is, e.g. level of detail, accuracy, validity, execution and clarity of outcomes.

Present results:

Methods, summary, e.g. charts, histogram, frequency polygon, imaginative use of diagrams, narrative, interpretation, tables, interpretation.

Learning Outcomes and Assessment Criteria

Recommended Resources

Textbooks

Clarke, J. (2020) Software Developer, BCS.

Fishpool, B. & Fishpool, M. (2020) Software Development in Practice, BCS.

Martin, R. C. (2017) Clean Architecture: A Craftsman’s Guide to Software Structure and Design. London Pearson, Addison-Wesley.

Links

This unit links to the following related units:

Unit 1: Programming

Unit 8 Data Analytics

Unit 20 Applied Programming and Design Principles

Unit 22 Application Development

Unit 26: Big Data Analytics and Visualisation.