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Online Postgraduate Award in Data-Driven Decision Making


The online Postgraduate Award in Data-Driven Decision Making programme consists of three modules. The first introduction module takes four weeks to complete and subsequent hands-on, practical modules take eight weeks to complete.


  1. Learning and Developing in the Digital Age
  2. Data Visualisation and Warehousing
  3. Predictive Analytics for Decision Making


To provide an understanding of the core concepts related to online education with University of Liverpool Online and to help you develop the skills required for successful progression within the programme. Successful completion of this module is required to continue on to subsequent modules.


This module is intended to prepare students for successful online study as well as to ensure they understand the academic requirements associated with the programme. As a student, you will learn how to make the best use of the learning platform, to navigate the online classroom and the University of Liverpool’s online library confidently. You will also learn how to produce high quality academic work that draws on classroom materials, collaborative activities with peers and your own professional experience.


  • The University’s model of online learning, with respect to independent learning and continuing professional development
  • Collaborative communication of professional and academic concepts to fellow students and independently within formal assignments
  • Engagement with topical literature to develop academic arguments, carry out library research and reference sources correctly in the appropriate style
  • Self-reflection and application of feedback to improve future academic work
  • Development of an effective and realistic study plan, to anticipate the time required to complete a module successfully


4 weeks


To provide a detailed understanding of the concepts and challenges of creating data warehouses. You will have an opportunity to create a data warehouse using open source technologies and free public data sets. You will also be introduced to why data visualisation is important and how to communicate these insights to tell a ‘data story’.


This module introduces students to the concept and challenges of creating data warehouses and how data visualisation tools can be used to ‘tell a story’. As a student, you will have the opportunity to gain practical experience of how to create data warehouses using data from multiple sources. The module will also introduce best practices in data visualisation and data storytelling, using state-of-the-art data visualisation technology.

The module is intended to help you understand the approaches and benefits of data warehousing, and show you how to quickly gain and communicate insights into the data stored in data warehouses using data visualisation techniques.


  • Review of the rational for, and benefits of, data warehousing, common challenges (integration, data cleansing) and different architectures (transactional, dimensional), the business case for data warehousing
  • Design of relational databases (normalising vs. de-normalising, defining dimensions and facts) using public data sources and an open source database platform
  • Data accuracy and data cleansing, defining rules for data warehousing
  • Loading data into a data warehouse and ensuring that relevant business case objectives are met
  • Benefits of visualisation and data storytelling, best practice for visualisation, how to avoid common mistakes and exploration of a selected data visualisation system
  • The process of building visualisations to answer common business questions illustrated using the visualisation system introduced in earlier in the module
  • Combining visualisations into a single dashboard to "tell a story" and provide insights, concerning data stored in a data warehouse and appropriate conclusions drawn
  • Expanding and building on existing insights by adding trends and forecasts; comparison with other types of data analysis, such as predictive and prescriptive data analytics.


8 weeks


To provide a systematic understanding of key predictive analytics techniques and an understanding of the types of business problems predictive analytics can solve. You are provided with an opportunity to apply the tools and techniques learned throughout the module to typical business problems.


This module provides students with insight into how predictive analytics can be used to help organisations and their customers make better decisions. As a student, you will have the opportunity to gain a comprehensive understanding of how results from predictive analytics can be used by organisations to grow their customer base and run operations more efficiently. This module is oriented towards practical applications of predictive analytics.


  • Common predictive analytics techniques (e.g. logistic regression, classification trees, collaborative filtering) and model development best practices
  • The end-to-end process of predictive analytics in the context of commercial environments: (i) problem identification, (ii) data assessment, (iii) data preparation, (iv) model development, (v) model deployment and (vi) making insights available to decision makers
  • Application of predictive analytics scoring models I: customer acquisition. Use case on optimising direct marketing campaigns utilising lists of score prospects
  • Application of predictive analytics scoring models II: customer retention. Use case on decreasing customer churn
  • Application of predictive analytics scoring models III: operational efficiency. Use case of optimising collection agent’s time by prioritising outreach to ‘high risk customers’
  • Application of predictive analytics scoring models IV: recommended systems. Use case on product recommendations in an online store
  • End-to-end application of predictive analytics techniques in business environments


8 weeks

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