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Advanced Data Analytics

IT602

Course CodeIT602
ECTS

Course Description

The aim of this unit is to provide learners with the knowledge and skills for advanced data analytics. This unit introduces learners to applied analytical models used in business to discover, interpret and communicate meaningful patterns of data held in silos or data warehouses, and to derive knowledge for competitive advantage. Learners are assumed to have some programming knowledge.

Course Objectives

The aim of this unit is to provide learners with the knowledge and skills for advanced data analytics. This unit introduces learners to applied analytical models used in business to discover, interpret and communicate meaningful patterns of data held in silos or data warehouses, and to derive knowledge for competitive advantage. Learners are assumed to have some programming knowledge.

Key Topics

  1. LO1: Understand the theoretical foundation of data analytics used in business decision-making.
  2. LO2: Understand issues in preparing a large data set for use in an applied analytical model.
  3. LO3: Be able to apply a range of descriptive analytic and/or statistical techniques to convert data into actionable insight.

Course Outline

WeekTopics
1Foundations of Data Analytics
2Evaluation of Analytical Frameworks
3Data Ingestion and Pipeline Architecture
4Data Preprocessing Pitfalls and Data Quality
5Applied Descriptive Analytics
6Predictive Analytics for Trend and Demand Forecasting
7Advanced Predictive Classification Models
MidtermMidterm evaluation
9Prescriptive Analytics and Optimisation Modelling
10Model Output Visualisation and Visual Analytics
11Applied Model Synthesis, Governance, and Visual Interpretation
12Applying Advanced Data Analytics in practice: case studies
13Assignment workshop: research, planning and referencing
14Assignment workshop: drafting, feedback and improvement
15Review and coursework completion
FinalFinal assessment and submission

Learning Outcomes

  1. LO1 Understand the theoretical foundation of data analytics used in business decision-making.
  2. 1.1 Explain common terminology in 'data analytics'.
  3. 1.2 Critically evaluate the use of data analytic methods.
  4. 1.3 Summarise the importance of data analytics for businesses.
  5. LO2 Understand issues in preparing a large data set for use in an applied analytical model.
  6. 2.1 Evaluate analytical model data preparation processes.
  7. 2.2 Critically evaluate potential issues in the preparation of data for use in an applied analytical model.
  8. LO3 Be able to apply a range of descriptive analytic and/or statistical techniques to convert data into actionable insight.
  9. 3.1 Critically evaluate methods to visualise the output from an applied analytical model.
  10. 3.2 Apply an appropriate programming language or tool to demonstrate how descriptive analytic techniques contribute to decision-making.
  11. 3.3 Apply an appropriate programming language or tool to demonstrate how predictive analytic techniques are used in forecasting future events.
  12. 3.4 Apply an appropriate programming language or tool to demonstrate how prescriptive analytic techniques are used to find the best course of action in a situation.
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