BTEC Level 4 Unit 8 Data Analytics (F/618/7415) Assignment Brief 2026
| University | Pearson Qualifications |
| Subject | Unit 8 Data Analytics (F/618/7415) |
Unit 8 Data Analytics Assignment Brief 2026
| Qualification | Pearson BTEC Levels 4 and 5 Higher Nationals in Computing |
| Unit Number | 8 |
| Unit Title | Data Analytics |
| Unit code | F/618/7415 |
| Unit type | Core |
| Unit level | 4 |
| Credit value | 15 |
Introduction
Like the physical universe, the digital universe is enormous and is doubling in size every two years. By 2020, the digital universe – the data we create and copy annually – is projected to reach 44 zettabytes or 44 trillion gigabytes.
Data is everywhere in the world. Without knowing how to interpret this data it would be difficult to understand its meaning or make use of the data to increase the productivity of an organisation. Data analytics is a range of processes that converts data into actionable insight using a range of statistical techniques. Data analytics is a relatively new term – it is an overarching term for all decision support and problem- solving techniques. Most of the time the terms ‘data analytics’ and ‘business analytics’ are used interchangeably.
This unit introduces students to the theoretical foundation of data analytics and a range of data analytic processes and techniques to provide hands-on experience to enhance their skills. Topics included in this unit are data analytic terminologies, types of data analytics, data exploration and visualisation, understanding data with descriptive, predictive and prescriptive analytics.
On successful completion of this unit, students will understand the theoretical foundation of data analytics, data analytic processes and techniques. They will also gain hands-on experience of implementing data analytic processes and techniques using a programming language such as Python, R, or a tool such as Weka, KNIME, Power BI, Excel etc. As a result, 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 Discuss the theoretical foundation of data analytics that determine decisionmaking processes in management or business environments
LO2 Apply a range of descriptive analytic techniques to convert data into actionable insight using a range of statistical techniques
LO3 Investigate a range of predictive analytic techniques to discover new knowledge for forecasting future events
LO4 Demonstrate prescriptive analytic methods for finding the best course of action for a situation.
Essential Content
LO1 Discuss the theoretical foundation of data analytics that determine decision- making processes in management or business environments
Data analytics terminologies:
Population, sample, categorical data, nominal data, ordinal data, continuous data, discrete data.
Types of data analytics:
Descriptive data analytics, predictive data analytics and prescriptive data analytics.
Exploratory data analysis (EDA):
Variable identification, univariate and bi- variate analysis, missing values treatment.
Data visualisation, e.g. graphs, charts, plots.
LO2 Apply a range of descriptive analytic techniques to convert data into actionable insight using a range of statistical techniques
Data analysis lifecycle:
Implement the stages of the data analysis lifecycle, including discovery, data preparation, model planning, model building, operationalise, communicate results.
Descriptive statistics:
Measures of central tendency, measure of position and measures of dispersion.
Probability distribution:
Cumulate distribution, discrete distribution, continuous distribution.
Sampling and estimation:
Random sampling, systematic sampling, point estimate, interval estimate.
Statistical inferences:
Models and assumptions.
LO3 Investigate a range of predictive analytic techniques to discover new knowledge for forecasting future events
Regression analytics:
Linear regression, multiple linear regression and logistic regression.
Forecasting techniques:
Qualitative, average approach, naive approach, time series methods, causal relationship etc.
LO4 Demonstrate prescriptive analytic methods for finding the best course of action for a situation
Optimisation:
Classical optimisation, linear programming techniques, non- linear programming techniques, dynamic programming.
Decision analysis:
Models, justifiable decisions and defensible decisions.
Learning Outcomes and Assessment Criteria
| Pass | Merit | Distinction |
| LO1 Discuss the theoretical foundation of data analytics that determine decision-making processes in management or business environments |
LO1 and LO2 |
|
| P1 Identify data analytic activities, techniques, and tools.
P2 Demonstrate an ability to use a popular programming language or tool used in the data analytics industry. |
M1 Investigate the three types of data analytic methods and their use in industry. | D1 Evaluate the importance of data analytical techniques to the decision-making process. |
| LO2 Apply a range of descriptive analytic techniques to convert data into actionable insight using a range of statistical techniques | ||
| P3 Investigate descriptive analytic techniques and explain with appropriate examples.
P4 Apply an appropriate tool or programming language to demonstrate these descriptive analytics techniques. |
M2 Show how these descriptive analytic techniques contribute to decision making.
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| Pass | Merit | Distinction |
| LO3 Investigate a range of predictive analytic techniques to discover new knowledge for forecasting future events | ||
| P5 Identify predictive analytic techniques and describe them with examples.
P6 Apply an appropriate tool or programming language to demonstrate these predictive analytic techniques. |
M3 Compare a range of predictive analytical techniques for forecasting purposes.
|
D2 Evaluate how predictive analytic techniques can be used for forecasting purposes. |
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| LO4 Demonstrate prescriptive analytic methods for finding the best course of action for a situation | ||
| P7 Analyse prescriptive analytic methods with appropriate examples.
P8 Demonstrate these methods using an appropriate programming language or tool. |
M4 Describe how these prescriptive analytic methods are used to find the best course of action in a situation. | D3 Apply an appropriate programming language or tool to demonstrate how these prescriptive analytic methods are used to find the best course of action in a situation. |
Recommended Resources
Textbooks
Evans, J. (2016) Business Analytics. 2nd edn. Pearson.
Runkler, T. (2016) Data Analytics: Models and Algorithms for Intelligent Data Analysis. 2nd edn. Vieweg+Teubner Verlag.
Links
This unit links to the following related units:
Unit 17: Business Process Support
Unit 26: Big Data Analytics and Visualisation.
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