COMPGC27 - Programming for Business Analytics

This database contains 2017-18 versions of the syllabuses. For current versions please see here.

Taught ByDaniel Hulme, with guest lecturers (100%)

Increasingly firms are using the data to develop new insights about their customers and their behaviours.

Beyond consumer markets, (big) data-driven decision making is increasingly making its presence felt in the business-to-business and business-to-government sectors.

Some organisations are even innovating their whole business models, creating new services through the novel application of data. As data-driven strategies take hold, they will become an increasingly important point of competitive differentiation.

This module will provide you with a broad understanding of Data, you will utilise cutting-edge tools that will emphasizes the importance of developing adequate business models providing appropriate incentives for private-sector actors to share and use data for the benefit of the individual, firm and society.

Learning OutcomesUpon successful completion of the module, a student will be able to:
  • Define data, and understand how to scrape, cleanse and de-dupe data
  • Characterise and criticality assess good and bad data in the context of data-driven decision making.
  • Utilise cutting-edge tools for Data, Information and Knowledge management.
  • Understand Big Data, as well as be able to extract insights from large data-sets.
  • Appreciate the technological and social challenges of managing Public and Private data, as well as the importance of Open Data to stimulate innovation.
  • Identify the challenges associated with the Storage, Security, Processing and Governance of organisational data both internally and externally, batched and real-time.
  • Recognise the differences between Structured, Unstructured and Semi-structured data, as well as the opportunities surrounding Linked-Data, Semantic Technologies and the Internet of Things.
  • Utilise tools to Manipulate and Visualise data, and appreciate the challenges and opportunities of using data-mining and visualisation technologies.
  • Critically assess the differences between Descriptive, Predictive and Prescriptive data analytics in the context of data-driven decision making, and apply cutting-edge tools to real-world data-analytics problems.


  • Data storage, security, processing, governance

  • Data scraping, cleansing and de-duping
  • Characteristics of useful Data
  • Big Data, Linked Data and the Semantic Web

  • Issues surrounding Public and Private Data

  • DIKUW Pyramid

  • Structured, Unstructured, Semi-structured

  • Data Visualisation and Manipulation

  • Descriptive, Predictive and Prescriptive data

  • Extensive use of Data tools

Method of Instruction

Lectures, seminars and workshops, with heavy emphasis on independent learning.


The course has the following assessment components:

  • 1 Group Coursework (50%)
  • 1 Individual Coursework (50%)

To pass this course, students must:

  • Obtain an overall pass mark of 50%.


Reading list available via the UCL Library catalogue.