Unit 33 Applied Analytical Models Assignment Brief 2026
| Qualification | Pearson BTEC Levels 4 and 5 Higher Nationals in Computing |
| Unit Number | 33 |
| Unit Title | Applied Analytical Models |
| Unit code | L/618/7448 |
| Unit type | Optional |
| Unit level | 5 |
| Credit value | 15 |
Introduction
Applied analytical modelling has become prevalent in many industries and has developed in terms of the mathematical techniques used and the diversity of modelling tools and techniques. Applied analytical modelling is carried out by a data scientist utilising modelling data, model building and model reporting skills. The aim of this unit is to give students knowledge of skills in analytical modelling skills, using computers to discover and interpret meaningful patterns in data by creating computer models.
This unit introduces students to applied analytical models used in business to discover, interpret and communicate meaningful patterns of data held in silos or data warehouses, and to derive knowledge to gain competitive advantage. Organisations may apply analytical methods and models to predict/prescribe business outcomes and improve performance in diverse areas such as stock control, financial risk and fraud analysis. Analytical models use mathematical algorithms and require extensive computation to process large amounts of data.
Among the topics included in this unit are: data preparation, fundamentals of applied analytical models and development of predictive or prescriptive models using a suitable algorithm and operating on a large data set.
Students will develop skills such as communication literacy, critical thinking, analysis, reasoning and interpretation which are crucial for gaining employment and developing academic competence.
Learning Outcomes
By the end of this unit students will be able to:
LO1 Examine applied analytical modelling methods
LO2 Prepare a large data set for use in an applied analytical model
LO3 Demonstrate the use of an analytical model with a large data set
LO4 Investigate improvements to an applied analytical model.
Essential Content
LO1 Examine applied analytical modelling methods
Decision or descriptive analytics:
Statistical look at data using visualisations, e.g. graphs, charts, reports, dashboards.
Prescriptive analytics:
Confirmatory data analysis (CDA).
Predictive analytics:
Forecasting or classification algorithms, machine learning, scoring, correlation, causation, regression analysis.
Algorithms:
Filtering, sorting clustering.
Data visualisation.
Business domains:
Behavioural analytics, cohort analytics, collections analytics, cyber analytics, enterprise optimisation, financial analytics, fraud analytics, marketing analytics, pricing analytics, retail analytics, risk analytics, supply chain analytics, talent analytics, telecoms analytic, transportation analytics.
LO2 Prepare a large data set for use in an applied analytical model
Identify and evaluate applied analytical model data requirements:
Data collection and data processing.
Semi-structured and unstructured metadata processing and cleaning.
Aggregation.
Exploratory data analysis (EDA).
Data product.
Data visualisation.
Information displays.
Dashboards.
LO3 Demonstrate the use of an analytical model with a large data set
Define analytic model requirements:
Data set selection.
Carry out cleaning, aggregation and EDA.
Identification of algorithm, selection and configuration of data mining software.
Model implementation.
Communication of results.
Data visualisation.
Graphical reports/dashboards.
LO4 Investigate improvements to an applied analytical model
Improvements:
The advantages and disadvantages of a range of investigative techniques.
Support the development of models for future state business situations.
Other considerations including data quality, data assumptions, sampling, segmentation, uplift data modelling, algorithm selection, pattern and relationship discovery, qualitative and quantitative use, validating results, output communication methods and tailoring data visualisation.
Learning Outcomes and Assessment Criteria
| Pass | Merit | Distinction |
| LO1 Examine applied analytical modelling methods |
D1 Using a case study example, critically evaluate the derived benefits from the use of an applied analytic model. |
|
| P1 Discuss the prescriptive and predictive analytical models, using examples.
P2 Illustrate three analytical methods, describing how they function. M1 Compare prescriptive and predictive analytical models, stating their advantages and disadvantages. |
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| LO2 Prepare a large data set for use in an applied analytical model |
D2 Review the primary reasons for carrying out data transformation before input to an applied analytical model. |
|
| P3 Describe the process of analytical model data preparation, describing data cleaning, discretisation, aggregation and data reduction stages.
P4 Suggest two methods to visualise the output from an applied analytical model, using illustrations. |
M2 Analyse three potential issues in preparation of data for use in an applied analytical model. | |
| Pass | Merit | Distinction |
| LO3 Demonstrate the use of an analytical model with a large data set |
D3 Review the results of the investigation, assessing the quality of the obtained knowledge. |
|
| P5 Select a suitable algorithm to analyse a large data set to meet a business need.
P6 Use an appropriate analytical modelling tool to carry out an investigation. |
M3 Propose how the data set will be prepared for the analytical model used in the investigation. | |
| LO4 Investigate improvements to an applied analytical model |
D4 Present the results of the investigation, promoting the benefits of using applied analytical models in a business. |
|
| P7 Investigate improvements to an applied analytical model. | M4 Propose three improvements to the approach used in the investigation.
M5 Discuss two ways to increase the performance and limits of the analytical model used in the investigation. |
|
Recommended Resources
Textbooks
Carlberg, C. (2016) Predictive Analytics: Microsoft™ Excel. QUE.
Marr, B. (2015) Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance. Wiley.
Runkler, T. (2020) Data Analytics: Models and Algorithms for Intelligent Data Analysis. Springer Vieweg.
Links
This unit links to the following related units:
Unit 8: Data Analytics
Unit 14: Maths for Computing
Unit 17: Business Process Support
Unit 18: Discrete Maths.
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