NCFE Level 4 Diploma Unit 06 Data Mining and Statistical Analysis (K/651/0929) Assignment Brief 2026

University	Northern Council for Further Education (NCFE)
Subject	Unit 06 Data Mining and Statistical Analysis (K/651/0929)

This unit allows learners to embark on a journey into the realm of data mining and statistical analysis, acquiring indispensable knowledge and skills to extract meaningful insights from complex datasets.

Learning Outcomes

Learning outcomes (LOs)	Mandatory teaching content
1. Examine the purpose of common statistical methodologies and their application to meet requirements	Knowledge: Statistical types and methods: • descriptive statistics • probability distributions • hypothesis testing • analysis of variance (ANOVA) • correlation analysis Skills: Apply statistical methods to meet data analysis requirements
2. Explore the purpose and application of statistical analysis	Knowledge: The purpose and application of descriptive, predictive and prescriptive analytics Factors that influence the selection of an analytical model: • classification • regression • clustering Statistical programming languages and tools used when manipulating and processing data (for example, SQL, Python, R) The use of data preparation techniques for statistical analysis (for example, sorting, grouping, filtering)
Learning outcomes (LOs)	Mandatory teaching content
3. Demonstrate the selection and use of appropriate tools for statistical analysis	Skills: Apply data preparation techniques for the collation and use of data within a predictive analytical task Select and apply appropriate statistical programming language and tools to manipulate and process data Apply data analytical and modelling techniques to predict trends and patterns in data: • data mining • time series forecasting

Grading Criteria

Learning outcomes (LOs)	Pass	Merit	Distinction
LO1: Examine the purpose of common statistical methodologies and their application to meet requirements	P1: Explain the purpose of common statistical methodologies	M1: Examine a range of statistical methods and illustrate how they can be used to meet data analysis needs	D1: Compare and contrast different statistical methods and assess pitfalls of each method
	P2: Define how to use statistical methods to meet data analysis requirements
LO2: Explore the purpose and application of statistical analysis	P3: Outline the purpose and application of descriptive, predictive and prescriptive analytics and identify factors that influence the selection of a model	M2: Discuss descriptive, predictive and prescriptive analytics and provide guidelines for choosing the best technique and tool for varied problems	D2: Evaluate the use of descriptive, predictive and prescriptive analytics, explaining how these can be used together to inform business decisions
	P4: Explain the purpose of statistical programming languages and tools used for manipulating and processing data
	P5: Describe the techniques used to prepare data for analysis
LO3: Demonstrate the selection and use of appropriate tools for statistical analysis	P6: Explain how statistical programming languages can be used for predictive analytics	M3: Apply appropriate statistical programming language and tools to a collated dataset to manipulate data and	D3: Compare and contrast different statistical programming languages and tools and justify use cases for each
	P7: Describe how to use analytical and
Learning outcomes (LOs)	Pass	Merit	Distinction
	modelling techniques to predict trends and patterns in data	perform predictive analytics

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Related Questions

Qualification	NCFE Level 4 Diploma: Data Analyst (603/7751/3)
Unit Reference Code	K/651/0929
Unit Title	Data mining and statistical analysis
Unit Number	6
Unit Level	4

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NCFE Level 4 Diploma Unit 06 Data Mining and Statistical Analysis (K/651/0929) Assignment Brief 2026

NCFE Level 4 Unit 06 Assignment Brief 2026

Unit Summary

Learning Outcomes

Grading Criteria

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