NCFE Level 4 Diploma Unit 06 – Data Mining and Statistical Analysis Assignment Brief

Unit 6 Assignment Brief

NCFE Level 4 Diploma: Data Analyst

Learning Outcomes:

The learning outcomes that are assessed by this coursework are:

1.Examine the purpose of common statistical methodologies and theirapplication
2.Explore the purpose and application of statistical analysis
3.Demonstrate the selection and use of appropriate tools for statisticalanalys is

Purpose of this assessment

This assessment evaluates your ability to use statistical analysis and data mining concepts to extract meaningful insights from a real-world dataset.

You will demonstrate that you can:

• Select appropriate statistical methods based on the business question
• Justify your choices and reject unsuitable approaches
• Prepare data so it is suitable for analysis
  Use Python to produce evidence (tables/visuals) that supports your conclusions
• Communicate findings clearly and professionally for non-technical stakeholders

You are not expected to use advanced mathematics or complex machine learning. The focus is on correct method selection, reliable evidence, and high-quality interpretation.

2. Company Scenario

You are working as a Junior Data Analyst for a UK-based retail organisation that operates stores across multiple regions and sells a range of products in different categories.

The organisation records sales transactions over time and has recently identified differences in performance across regions, product categories, and customer types. Senior management wants to move away from ad-hoc reporting and instead make data-driven decisions supported by statistical analysis.

You have been provided with a retail sales dataset containing transaction-level information such as order details, sales values, product categories, store regions, customer characteristics, and order dates. The dataset may contain data quality issues that need to be identified and addressed before analysis.

Your role is to analyse this dataset using Python and appropriate statistical and analytical methods in order to:

• summarise and compare sales performance
• select suitable analytical approaches for different business questions
• identify meaningful patterns or trends in the data
• communicate insights clearly to non-technical stakeholders

You are not expected to build complex models. The emphasis is on choosing appropriate methods, providing evidence for decisions, and explaining results in clear, professional language.

3. Dataset Information

You have been provided with a retail sales dataset containing transaction-level data for a UK-based retail organisation. The dataset represents sales activity across multiple store regions and product categories over a defined time period.

The dataset includes information such as:

• order identifiers and order dates
• store regions
• product categories
• units sold and pricing information
• total sales values
• customer type indicators
• discount and return information
• The dataset is not perfectly clean. It contains:
• a small number of missing values in selected columns
• potential data inconsistencies that may affect analysis results

Before carrying out any statistical analysis, you are expected to inspect the dataset and consider data quality issues that could influence your findings. Where appropriate, you should apply basic data preparation steps and clearly explain any decisions you make.
All analysis and evidence must be generated using Python, and any outputs included in your submission should directly support your conclusions.

4. Assessment Tasks

You must complete all three tasks using the same provided dataset.

All analysis must be carried out using Python, and all outputs must be used as evidence to support your explanations.

The focus of all tasks is on:

• choosing the correct approach
• rejecting incorrect approaches
• explaining decisions clearly and professionally

Task 1 - Statistical Method Selection & Interpretation (LO1)

What this task is about

This task assesses your ability to choose appropriate statistical methods to summarise and compare data, and to explain your choices.

What you need to do

1. Use Python to summarise Total Sales using appropriate descriptive statistics.
2. Decide whether it is appropriate to compare sales across Store_Region.
3. Choose the most suitable statistical approach for this comparison.
4. Clearly reject one statistical method that would not be suitable and explain why.
5. Explain your findings in professional language, including one limitation.

What to submit

• One summary output (e.g. descriptive statistics table)
• One group-based comparison output

A short written explanation covering:

• chosen method
• rejected method
• interpretation
• limitation

Task 2 - Analytics Type & Model Selection (LO2)

What this task is about

This task assesses your understanding of which type of analysis and which model approach is appropriate for different business goals.
What you need to do

1. Decide whether the business need is best addressed using:

• descriptive analytics,
• predictive analytics, or
• prescriptive analytics.

2. Identify the most suitable model type:

• classification,
• regression, or
• clustering.

3. Explain why your chosen analytics type and model type are appropriate.

4. Clearly reject at least one alternative analytics or model type, explaining why it would not fit the task.

5. Use simple Python-generated outputs to support your reasoning

What to submit

• One or two supporting Python outputs (table or chart)

A clear written justification explaining:

• chosen approach
• rejected approach

Task 3 - Pattern or Trend Identification & Insight Communication (LO3)

What this task is about

This task assesses your ability to prepare data, identify a meaningful insight, and communicate it clearly.

What you need to do

1. Identify and address one data quality issue in the dataset (for example, missing values or duplicates).

2. Use Python to identify either:

• a pattern (e.g. differences across regions or categories), or
• a trend over time.

3. Explain what the pattern or trend suggests about business performance.

4. Clearly state one limitation or uncertainty in your analysis.

5. Provide a short, professional recommendation based on your findings.
What to submit

• Evidence of data preparation
• One grouped output or time-based visual
  .A written interpretation including:
• insight
• limitation
• recommendation

5. Evidence Requirements

For all tasks, you must provide clear and relevant evidence to support your analysis and explanations. Evidence should demonstrate both your practical use of Python and your ability to interpret results correctly.

Required evidence

Across the three tasks, your submission must include:

• Python-generated outputs, such as:
• tables (e.g. summaries or grouped results)
• charts (e.g. bar charts or line charts)
• Written explanations that interpret the outputs and justify your decisions.
• Screenshots, only where they are needed to clearly show Python outputs or results.

Output limits (to control workload)

To keep the assessment focused and manageable:

Task 1: Maximum of 2 Python outputs
Task 2: Maximum of 2 Python outputs
Task 3: Maximum of 2 Python outputs

Additional outputs beyond these limits will not be marked.

Captions and labelling

All tables, charts, screenshots, and figures included in your submission must have clear captions.

Each caption should briefly explain:

• what the output shows, and
• why it is relevant to the task.

Key reminder

Outputs on their own are not sufficient. Credit is awarded for:

• selecting appropriate outputs
• explaining what they show
• linking them directly to your analytical decisions

6. Submission Instructions & Word Count Guidance

• This is an individual assessment. Group submissions are not permitted.
• You must submit one single file in Word (.docx) or PDF format.
• A report template will be provided and must be used for your submission. File naming convention