MM1F28-Business in Practice: Data analytics

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:4
Terms in which taught: Autumn / Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2022/3

Module Convenor: Dr Markos Kyritsis
Email: m.kyritsis@henley.ac.uk

Type of module:

Summary module description:

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. The workshops will give students experience in using an industry standard programming language, as well as GUI-based tools, thus providing them with the opportunity to choose the most appropriate method for their own future employability needs.

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This module is delivered at the °ÄÃÅÁùºÏ²Ê¿ª½±¼Ç¼ and the °ÄÃÅÁùºÏ²Ê¿ª½±¼Ç¼ Malaysia

Aims:

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

Assessable learning outcomes:

On completion of this module, the student should be able to:

• Manage, clean, and filter data into data frames in order to support the analysis (LO1)


• Justify which statistical tools should be used to analyse data and discuss their findings (LO2)


• Demonstrate effective use of data visualisation techniques (LO3)


• Develop regression models to generate future predictions on continuous and categorical outcome variables (LO4)

Additional outcomes:

The students will:

• Become familiar with industry standard data analytics tools (e.g., R, Python, etc.)


• Learn to create simple scripts to support their analysis

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Outline content:

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Data Processing (LO1)

1. Data types and distributions

2. Data manipulation

Descriptive Analytics (LO2)

3. Parametric tests: Difference in means

4. Parametric tests: Correlations

5. Non-parametric tests

Visualising data (LO3)

6. Summarising data graphically

7. Creating confidence interval plots

Predictive Analytics (LO4)

8. Regression Models

Brief description of 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. Classes will be conducted in a partially flipped learning style, where students are expected to do workshops prior to the tutorials and demonstrate their understanding in class. It assumes no prior knowledge or experience in data analytics or statistics, therefore students are expected to do a fair amount of wider reading. U nmarked quizzes will be provided every week for both the theory, and the practical aspects of the course through Blackboard. Students can take these quizzes as many times as they like.

Contact hours:

Ìý	Autumn	Spring	Summer
Lectures		10
Tutorials		10
Practicals classes and workshops		10
Guided independent study:	Ìý	Ìý	Ìý
Ìý Ìý Wider reading (independent)		50
Ìý Ìý Wider reading (directed)		20
Ìý Ìý Advance preparation for classes		10
Ìý Ìý Preparation for tutorials		20
Ìý Ìý Preparation of practical report		30
Ìý Ìý Essay preparation		40
Ìý	Ìý	Ìý	Ìý
Total hours by term	0	200	0
Ìý	Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Report	50
Class test administered by School	50

Summative assessment- Examinations:

None

Summative assessment- Coursework and in-class tests:

The module will be assessed through one in-class test and one report, which are both worth 50% of the grade. For the report students will be given a case study accompanied by a data set and asked to (a) engage in exploratory data analysis using descriptive analytics, and/or (b) build regression models using predictive analytics. Students will be expected to show critical reasoning skills and justify their approach. The results of their analysis will lend themselves to the development of a consulting document that is (a) rich in data visualisation, and (b) will be the basis for data-driven recommendations to the stakeholders. The in-class test will be administered during the seventh week of term, while the report is due on the first week of the following term.

Formative assessment methods:

Students will be given feedback on the progress of their individual project through tutorials and practical sessions.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

40% overall grade (when combining the grades from both the in-class test and the assessment)

Reassessment arrangements:

By a coursework assessment worth 100%

Additional Costs (specified where applicable):

Required text books: Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage. Cost: £58.00

Last updated: 22 September 2022