Data Management and Analytics

Overview

Framework:
RQF
Level:
Level 2
Unit No:
T/617/6718
Credits:
3
Guided learning hours:
24 hours

Assessment Guidance

Pass/Merit/Distinction - refer to Grading Criteria within specification

Aim

To enable learners to understand data management and analytics.

For the full unit details, please download the latest version of the qualification specification. This is available for download on the relevant qualifications page.

Unit Learning Outcomes

To view the assessment criteria and any additional content, please expand the relevant learning outcome below.

1 — Understand data management.
  • Purpose of data analytics and processes involved
  • Examine large amounts of data to identify patterns, correlations or insights
  • Data can be used to reduce costs
  • Decisions can be made quicker
  • New products and services can be tested through analytics
  • Improving health care
  • Machine Learning, Data Management, Data Mining

Data management decision-making:

  • Strategy planning
  • Productivity
  • Product/service benchmarking

Legal, ethical and security issues:

  • Data protection legislation
  • Individual rights
  • Security of commercial and personal data

Storing data:

  • Structure/unstructured
  • Security
  • Data warehouse

Accessing data:

  • Security
  • Sharing

Assessment Criteria

  • 1.1

    Outline the purpose of data analytics and the processes involved.

  • 1.2

    Explain how an organisation can use a data management system to improve performance.

2 — Be able to carry out data analytics for a given purpose.
  • Statistical Techniques and Probability:
  • Discrete data
  • Continuous data
  • Spreadsheets, statistical software eg SPSS
  • Mean, median and mode
  • Measures of dispersion, variance, standard deviation, range, interquartile and inter-percentile ranges
  • Normal distribution
  • T-Test
  • Linear relationship
  • Equality of the line of regression and correlation coefficient
  • Regression line for non-linear relationship
  • Presentation of data eg – bar charts, pie charts, histogram

Present findings:

  • Prepare the data for analysis
  • Analyse the data
  • Validity, accuracy, relevance
  • Presentation appropriate format to meet brief and audience
    Graphical and numerical data
    Reports, presentations, verbal communication

Assessment Criteria

  • 2.1

    Analyse relevant data using appropriate statistical and probability operations.

  • 2.2

    Summarise and present relevant findings from the data analysis in an appropriate format.