FUNDAMENTALS OF BUSINESS ANALYTICS - 2025/6

Module code: ECO2064

Module Overview

This module provides an overall introduction to Business Analytics explaining methods used for descriptive, predictive, and prescriptive analytics as the main building blocks and phases of a typical business project within management and business contexts using the Python programming language. In addition to generic introduction to business analytics phases, there will be more focus on the first two phases (i.e., descriptive and predictive data analytics). The module will also include general business skills needed to run a business analytics project such as how analytics professionals communicate with decision makers by using and interpreting analytic models.

Module provider

Economics

Module Leader

GOLSON Eric (Economics)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Overall student workload

Independent Learning Hours: 50

Lecture Hours: 9

Tutorial Hours: 22

Guided Learning: 36

Captured Content: 33

Module Availability

Semester 1

Prerequisites / Co-requisites

N/A

Module content

Indicative contents include: The business Analytics lifecycle; descriptive statistics; visualisation; probability theory; and descriptive data mining; and inferential statistics; Python programming may include: variables, in-built functions, data types, file/script writing, data types; insights into the structure of Python such as objects, instances, attributes, functions, classes, definitions, loops, logical statements and so on; the use of packages useful for data science; scientific code structuring; loading of python packages; loading, storing and manipulation of data files; plotting and debugging codes; data pre-processing; data visualisation; and data analysis; simple regression analysis; model validation; making projections Data Analysis; Introduction; data sets; data visualisation; data security; ethics; understanding trends and anomalies; reproducibility; using python for data analysis; regression analysis; model validation and projections.

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework COURSEWORK 1 40
Coursework GROUP COURSEWORK 2 60

Alternative Assessment

Coursework 2 is a group coursework which can be done individually in the resit period.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate: Their understanding of using Python as a scientific language to solve problems in data science. Their ability to extract valuable information out of the results of their data analysis. Their ability to write well-structured, reusable functional codes Their ability to work and communicate their results. Thus, the summative assessment for this module consists of:

- 40% python individual coursework - covers basic python e.g. for loops/if statements/etc. Written report and submission of code.
- 60% group project coursework - technical report based on open ended project.

Formative assessment and feedback
Students receive verbal feedback during lectures and seminars in which questions and real-world examples are discussed. In addition to this, they receive a number of problem sets designed to further their knowledge and prepare them for the summative assessments which are discussed in lab tutorials. Students receive guideline solutions against which they can compare their own results. 

Module aims

  • Introduce key theories and concepts relevant to the field of Business Analytics and how it is applied for business decision making.
  • Recognise the main stages of a typical business analytics and project how to successfully manage analytics projects.
  • Explore the different approaches and methods used for business analytics in terms of processes and types of output produced.

Learning outcomes

Attributes Developed
001 Demonstrate and apply the basic concepts of Business Analytics and explain their importance within the organisation CKPT
002 Apply statistical and probabilistic data analytics methods on business problems. CPT
003 Interpret and synthesise data to enhance business decision making and conclusions. CPT
004 Present data and analytics results. KPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to: encourage critical understanding of the role played by data analytics for business decision making. Learning will be directed, supported and reinforced through a combination of lectures, computer laps, and online discussion groups, plus directed and self-directed study. The module is research-led and offers a mix of theoretical insights and case study material that will be delivered in different formats where appropriate.

The learning and teaching methods include:

1 hour lecture per week X 9 weeks
2 hour lab tutorial per week X 11 weeks

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECO2064

Other information

Employability: The module is designed to give fundamental knowledge and skills in data analysis which is in high demand in business, government, and academia. Resourcefulness and resilience: Student will learn and practice python which is widely used for data analysis in business and public bodies. Global and Cultural Intelligence: Students will be able to collect and analyse data from various countries to observe differences and similarities between countries and regions. Sustainability: Doing the group coursework student will learn how to plan, coordinate, and complete projects. They will learn how to work in teams and evaluate teammate¿s contributions. Learned transferable skills can be applied to future employment or graduate study. Digital Capabilities: Student will learn how to use python and its packages. They will obtain general understanding of how programming languages work.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Business Economics and Data Analytics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module
Business Economics BSc (Hons) 1 Compulsory A weighted aggregate mark of 40% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2025/6 academic year.