Analytics in Action I

Module overview

Companies today collect large volumes of data from a variety of sources. This module introduces the key concepts of using ‘Big Data’ to support organisational decision-making. The module begins by defining the concept of data analytics. We then focus on predictive analytics techniques (e.g., regression and classification) and descriptive analytics techniques (e.g., clustering, association analysis, and sequence analysis). The module also illustrates how data analytics can be applied successfully in a range of areas, including marketing, retail credit risk, healthcare, and fraud detection. Theoretical concepts are supported by real-world case studies and demonstrations using commercial software.

Linked modules

Prerequisites: MANG1019 or MANG1047

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

interpret the output of statistical techniques used for the main data analytics applications.
explain and evaluate concepts and tools needed to evaluate, analyse, and interpret large volume of data in different industries;
plan the resources needed to evaluate and analyse data, critically apply findings, and disseminate the outcomes;

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

the use of advanced analytics methods on large volume of data to derive actionable insights in different industries.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

communicate effectively.
produce integrated project deliverables at an appropriate intellectual and practical level;
plan and control effectively for successful completion of a personal workload;

Learning and Teaching

Teaching and learning methods

Teaching methods include: The basic principle of the teaching and learning strategy for this module is to encourage you to actively engage in the subject matter through guided self-discovery of the material which will include: Reading; lecture slides; practical classes/computer lab sessions; exercises; discussion and debate. As with all programmes in the Faculty, student learning will also be supported by published course materials and the University's virtual learning environment (VLE).

Study time
Type	Hours
Independent Study	116
Teaching	34
Total study time	150

Resources & Reading list

Textbooks

Baig, M.R., Govindan, G. and Shrimali, V.R. (2021). Data science for marketing analytics: a practical guide to forming a killer marketing strategy through data analysis with Python.. Birmingham, UK: Packt Publishing.

Han, J., Pei, J. and Tong, H. (2024). Data Mining: Concepts and Techniques. Morgan Kaufmann.

VanderPlas, J. (2022). Python Data Science Handbook.. Sebastopol, CA: O’Reilly Media.

Delen, D (2021). Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners.. Old Tappan, New Jersey: Pearson Education.

Provost, F. and Fawcett, T. (2013). Data science for business. O’Reilly.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Class participation

Assessment Type: Formative
Feedback: There will be exercises and computer laboratories/class sessions for this module. The lecturer will provide verbal feedback during all sessions as well as during office hours.
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Project	100%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Project	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Project	100%

Repeat Information

Repeat type: Internal & External