MM1F28: Business in Practice: Data analytics

MM1F28: Business in Practice: Data analytics

Module code: MM1F28

Module provider: Business Informatics, Systems and Accounting; Henley Business School

Credits: 20

Level: Level 1 (Certificate)

When you'll be taught: Semester 2

Module convenor: Dr Markos Kyritsis, email:

Module co-convenor: Dr Nico Biagi, email:

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: No placement specified

Academic year: 2024/5

Available to visiting students:

Talis reading list:

Last updated: 28 May 2024


Module aims and purpose

Acquiring, managing, and analysing data is an important business activity that allows organisations to make strategic use of their data assets. Analysing historical data can give companies insight on how to optimise a wide range of functions related to accounting and management. Furthermore, constructing predictive models can facilitate the process of classifying future events and making informed data-driven decisions. This introductory module aims to expose students to key concepts in data analytics by introducing two stages of data analytics (a) descriptive analytics and (b) predictive analytics, as well as visualisation techniques for qualitatively summarising data.

The focus of this module will be less on the underlying mathematical and statistical concepts and more on forming a working knowledge of the methods and assumptions for using statistical methods given certain parameters. Key concepts that will be covered include: types of data; types of distributions (with an emphasis on the normal distribution); analysing the differences between means using parametric and non-parametric tests; regression models; and data visualisation. Finally, the workshops will give students experience in using GUI-based tools for analysing data.

Ultimately, the aim of this course is to provide students with an understanding of how to manage, visualise, and analyse data. To satisfy this general aim, students will acquire key knowledge and skills in:

  • Accessing, storing, and handling multivariate data
  • Analysing data
  • Visualising data
  • Creating regression models

The module lead at the University of Reading Malaysia is Teck Yong Eng.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

  1. Manage data in order to support the analysis (LO1)
  2. Justify which statistical tools should be used to analyse data and discuss their findings (LO2)
  3. Demonstrate effective use of data visualisation techniques (LO3)
  4. Develop regression models to generate future predictions on continuous and categorical outcome variables (LO4)

Module content

Data Processing (LO1) 

  1. Data types and distributions 
  2. Data manipulation 

Descriptive Analytics (LO2) 

  1. Parametric tests: Difference in means 
  2. Parametric tests: Correlations 
  3. Non-parametric tests 

Visualising data (LO3) 

  1. Summarising data graphically 
  2. Creating confidence interval plots 

Predictive Analytics (LO4) 

  1. Regression Models


Teaching and learning methods

This module will be a combination of lectures, tutorials and practical workshops that will enable students to acquire key concepts and practical skills in data analytics. Seminar activities are more theory-driven, while workshops activities are more practical. Datasets will be given to students along with exercises that they will be expected to finish by the end of each weak. Solutions will be released at the end of the week. Classes will be conducted in a partially flipped learning style, and students are encouraged to engage with material prior to coming to the lectures.

It assumes no prior knowledge or experience in data analytics or statistics, therefore students are expected to do a fair amount of wider reading.

This module may be taught in a different semester if you are studying at our campus in Malaysia.

For students studying at our campus in Malaysia: This module may be taught in a different semester and the breakdown of study hours may differ to those set out in the Study Hours table (please refer to the Module Handbook for the correct breakdown). In addition, you will be required to complete an additional 40 hours of study, taking the total number of study hours to 240 for this module. This is to comply with the Malaysian Quality Agency (MQA).

Study hours

At least 20 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

 Scheduled teaching and learning activities  Semester 1  Semester 2  Summer
Lectures 10
Seminars 10
Project Supervision
Practical classes and workshops 10
Supervised time in studio / workshop
Scheduled revision sessions
Feedback meetings with staff
External visits
Work-based learning

 Self-scheduled teaching and learning activities  Semester 1  Semester 2  Summer
Directed viewing of video materials/screencasts
Participation in discussion boards/other discussions
Feedback meetings with staff
Other (details)

 Placement and study abroad  Semester 1  Semester 2  Summer
Study abroad

Please note that the hours listed above are for guidance purposes only.

 Independent study hours  Semester 1  Semester 2  Summer
Independent study hours 170

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.


Requirements for a pass

Students need to achieve an overall module mark of 40% to pass this module.

Summative assessment

Type of assessment Detail of assessment % contribution towards module mark Size of assessment Submission date Additional information
In-class test administered by School/Dept Class Test 50 70 minutes Semester 2, Teaching Week 7 8 hour window in which to complete a timed test online
In-class test administered by School/Dept Report 50 190 minutes Semester 2, Teaching Week 12 8 hour window in which to complete a timed data analytics report online

Penalties for late submission of summative assessment

For the report (assessment two), 1% is deducted from the final mark for each minute that the student is late when submitting the assignment. The first assignment will auto-submit once 70 minutes are up. Failure to sit the assessments by the deadline are automatically given a failed grade and are in line with university policy for in-class tests.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Exercises are given to students through Blackboard. The solutions will be covered in class. Revision sheets are also provided, and students are expected to complete them and ask questions.


Type of reassessment Detail of reassessment % contribution towards module mark Size of reassessment Submission date Additional information
In-class test administered by School/Dept Report 100 190 minutes During the University resit period August/September 8 hour window in which to complete a timed data analytics report online

Additional costs

Item Additional information Cost
Computers and devices with a particular specification
Printing and binding
Required textbooks Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London. £58
Specialist clothing, footwear, or headgear
Specialist equipment or materials
Travel, accommodation, and subsistence


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