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COMPGC27 - Programming for Business Analytics

This database contains the 2017-18 versions of syllabuses. Syllabuses from the 2016-17 session are available here.

Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).

CodeCOMPGC027
YearMSc
PrerequisitesNone
Term1
Taught ByDaniel Hulme, with guest lecturers (100%)
Aims

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.

Content

  • 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.

Assessment

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%.

Resources

Reading list available via the UCL Library catalogue.