Data Management and Analytics

Overview

Framework:
RQF
Level:
Level 2
Unit No:
D/618/3677
Credits:
3
Guided learning hours:
24 hours

Aim

Learners will explore big data and how it is analysed and manipulated by organisations to predict patterns and trends. Learners will also learn about data analysis techniques and how they can be used to collect, cleanse, manipulate and present findings.

Unit Learning Outcomes

1

Understand data management and analytics.

AC 1.1:

  • Big data and how it is collected and used for a business purposes.
  • Sources of big data include: social media, online gaming, loyalty cards, online commerce, questionnaires, government records, subscriptions, research, healthcare, e.g. heart disease, infectious diseases, doctor’s performance, financial sector, politics weather.
  • The safety and security implications of big data, including the types of data individuals provide online for a variety of purposes, information organisations may hold about consumers.

Big data analytics and analysis

  • Use of software tools to gather and analyse big data for decision making, strategic planning, productivity, product/service benchmarking, marketing, advertising, forecasting.
  • Categories of data and how these are gathered and analysed, such as: medical criteria, e.g. blood group, medical conditions, personal details, e.g. date of birth, address, phone number, financial information, e.g. salary, credit rating, debt, mortgage, fraud, environmental, e.g. temperatures, rainfall, sunlight hours, wind speeds, tides, retail habits, e.g. preferred shops, spend, shopping patterns.
  • Predictive analytics techniques for a range of purposes, including: defining data, producing statistics, modelling data, data mining.
  • Using analytics software to analyse and present big data including: cleansing, coding data, assessing validity, checking integrity of the data.
  • Using software tools to process big data for a given purpose: questioning the data, e.g. multiple tables, multiple criteria, formulaic functions, graphical information.
  • Evaluation of the results of data processing to identify: whether the solution meet requirements, whether it meets customer need, the strengths of big data analytics, whether it could be improved.
  • Legal, ethical and security issues: data protection legislation, individual rights, security of commercial and personal data.
  • Storing data: Structured/unstructured, security, data warehouse
  • Accessing data: Security, sharing.

AC 1.2:

Levels of data gathered, stored and used in organisations, including strategic and operational data, stages of data analysis in an organisational context, including:

  • Information requirements, e.g. how it will be used, why it is needed
  • What the problem is and how the information will solve it
  • Data collection, e.g. observations, interviews, review of existing data
  • Data organisation, e.g. how the data will be organised, stored and who will have access
  • Data storage, e.g. in-house or external and the requirements
  • Data cleansing, e.g. errors, missing elements, duplicates
  • Data manipulation, e.g. arranging, collating, aggregating, interpreting, correlation
  • Presentation of findings, e.g. tables, charts, graphs, dashboard, reports.

Assessment Criteria

  • 1.1

    Explain the purpose of big data analytics and analysis and how organisations can use it to improve productivity and performance.

  • 1.2

    Investigate how data analysis techniques can be used to collect, cleanse and manipulate data.

2

Be able to manipulate data to produce findings for a range of purposes and audiences.

AC 2.1:

  • Statistical and probability techniques:
  • Discrete data
  • Continuous data
  • Spreadsheets, statistical software (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: bar charts, pie charts, histogram etc.

AC 2.2:

Present findings: prepare the data for analysis, analyse the data, check for validity, accuracy, relevance, is presentation appropriate for purpose and audience, visualisation tools and techniques, graphical and numerical data, reports, presentations, verbal communication.

Assessment Criteria

  • 2.1

    Use a range of data analysis techniques to manipulate data using statistical and probability operators.

  • 2.2

    Produce and present findings in appropriate formats.