UCL School of Management

Module Fact Sheet

MSIN0010: Data Analytics I

Taught by
Level
First
Prerequisites
None
Eligibility
1st year undergraduates from Management Science BSc/MSci only
Terms
Term 1
Delivery method
2-hour lecture (x 8 weeks) and 2-hour seminar (x 8 weeks)
Assessment
Unseen two-hour written examination 60%
Group coursework 20%
Coursework (3-4 problem sets) 20%
Previous Module Code
MSIN102P

Course overview

Data Analytics I introduces students to how organisations use data and analytics to create value and improve performance, trains them to use selected statistical data analytics and data mining tools, and introduces them to elements of the statistical theory and algorithms that underpin those tools.

The context for the module is management in complex, innovation-intensive, data-driven environments. The explosion in the volume and range of internal and external data available to managers and the development of new data analytics tools is having a major impact on how people identify, formulate and solve management problems.

During the module, students will manipulate example data sets and use basic data collection tools and APIs to source data from publicly available data sources.

Learning outcomes

Upon successful completion of the module, students will be able to:

  • Understand how organisations use data and analytics to create value and improve performance.
  • Understand and apply founding probability and statistical theory to data analysis.
  • Understand and apply information theory and data mining theory to data classification and data clustering problems.
  • Characterise and critically assess the quality of data sets and their limitations in the context of data-driven decision-making.
  • Use selected tools (Excel and R) to analyse and visualise data.
  • Understand key elements of the theory, technology and algorithms that underpin the tools used.

Topics covered

  • Data visualisation
  • Probability, random variables, Bayes’ theorem, probability distributions
  • Expectations, inequalities, central limit theorem
  • Confidence intervals and hypothesis testing
  • Linear regression, logistic regression, model selection
  • Classification and regression trees
  • Entropy and information gain
  • Clustering

Assessment summary

Unseen two-hour written examination 60% Group coursework 20% Coursework (3-4 problem sets) 20%

Current students should refer to Moodle for specific details of the current year’s assessment.

Essential reading

Complete Business Statistics (Seventh Edition), Aczel, A., & J. Sounderpandian, McGraw-Hill/Irwin 2008.

Data Science for Business: What You Need to Know About Data Mining and Data Analytic Thinking, Foster Provost & Tom Fawcett; O’Reilly Media (2013)

Past versions of this module

MSIN102P 17/18

MSIN102P 16/17

Last updated Monday, 13 August 2018