COMPGC27 - Programming for Business Analytics

This database contains 2016-17 versions of the syllabuses. For current versions please see here.

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 tool 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 Outcomes

Upon 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

  • Data Science for Business: What you Need to Know about Data Mining and Data     Analytic Thinking, T. Fawcett and F. Provost, O’Reilly, 2013.

  • Thinking with Data: How to Turn Information into Insights, M. Shron, O’Reilly, 2014.