Programme And Module Handbook
 
Course Details in 2023/24 Session


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Module Title LM Data Analytics and Predictive Modelling
SchoolBirmingham Business School
Department Management
Module Code 07 38157
Module Lead Devon Barrow
Level Masters Level
Credits 20
Semester Semester 1
Pre-requisites
Co-requisites
Restrictions None
Contact Hours Lecture-11 hours
Tutorial-11 hours
Practical Classes and workshops-18 hours
Guided independent study-160 hours
Total: 200 hours
Exclusions
Description This module introduces to students the fundamental methodologies and methods in data analytics and wider data science which are used to extract value from their information assets, and improve organisations’ decision making and performance. Students are initially introduced to the broad concepts of, and relationship between data analytics, data mining and data science, followed by the main elements of the data analytics project workflow, including data preparation, understanding and analytical modelling.

Students cover the breadth of methods from descriptive analytics to those used to generate predictions. They develop the ability to describe, analyse and interpret data to improve business understanding, identify key business drivers and decision factors. As part of the advanced analytical modelling stage, the module introduces students to the most important and fundamental methods in analytics including linear and logistic regression, K-nearest neighbours, decision trees, and artificial neural networks, as well as other new and emerging methods from statistics, machine learning and artificial intelligence. These methods are introduced in an applied context using a number of case examples drawn from predictive classification, cluster analytics, text mining, and time series forecasting to name a few. The skills, tools and methods learned on this module underpin many of the other modules on the programme. The role of data visualisation in the data analytics process is also discussed.
Learning Outcomes By the end of this module students should be able to:
  • Critically discuss business analytics and related fields, and the application of the business analytics project life-cycle including the ability to determine whether the principles and concepts of business analytics apply to a business venture.
  • Critically evaluate for a given business analytics project the various project life-cycle choices and considerations including those related to project scope, data collection, understanding, and analytical modelling.
  • Critically review a range of analytics algorithms, methods and models, and key considerations in the approach to modelling and implementation including feature selection, parameter estimation, and heuristics.
  • Apply a range of analytics modelling techniques, algorithms, and methods to a range of domains including predictive classification, cluster analysis, text mining and time series forecasting.
Assessment 38157-01 : Individual Proposal (2000 words) : Coursework (40%)
38157-02 : Individual Assignment (3000 words) : Coursework (60%)
Assessment Methods & Exceptions Assessment: 2000-word Individual assignment – Individual Proposal (40%) and 3000-word Individual assignment – Report (60%)

Reassessment by failed component.
Other
Reading List