Module Code and Title

INMR95 Business Data Analytics

Academic Year

2024/25

Type of Assessment

Individual Data Analytics Report

Weighting of Assessment

100%

Individual or Group Assessment

☒ Individual                   ☐ Group

Module Convenor Office Hours/Opportunities for advice and feedback

Workshops are dedicated to answering all questions related to the course. E.g., worksheet exercises, R workshops, understanding the material, and understanding the assignment. Contact the module convenor if there are any issues that cannot be solved in the workshops.

Submission deadline

31/01/2025

Submission point

☒ Blackboard    ☒ Turnitin     ☐ Other:  Enter text here

Item(s) to be submitted

Compulsory: One document with three chapters (as described in the assignment description document found on Blackboard).

Optional: CSV files as R-scripts

NOTE. If you are not submitting your CSV files and R-scripts, then add those to the appendix of the document.

File type

☒ PDF      ☒ Word       ☐ PPT      ☐ Excel       ☐ Video     

☒ Other:  .R and .CSV files are optional

Formatting guidelines

Harvard-style. formatting

Structure (e.g. required sub-sections)

Three chapters are expected, as described in the assignment description document.

Size of assessment (word limit or length) and penalty applied

4000 words. Standard university penalties apply.

Referencing style

☒ Harvard                   ☐ Other:  Enter text here                     

Module learning outcomes to be assessed

The general aim of this coursework is for students to apply descriptive, predictive, and prescriptive analytics (supported by visualisation techniques) in order to explore data, as well as develop models, which ultimately contribute to data-driven decision-making for two different problem domains and data sets (LO1, LO2, LO4). Students are expected to document and present their findings in a 4,000 word report that is worth 100% of their grade. All data, as well as the saved R scripts that highlight data manipulation and management must be stored and submitted along with the documentation (LO3). In more detail, the documentation should be split into three sections:

Section one requires the use of inferential statistics and dimension reduction techniques in order to extract components from a survey and use the component scores in several analyses (LO1). Students are expected to critically analyse their results, manage and manipulate the data (LO3), as well as illustrate their findings using visualisation techniques (LO2).

Section two requires students to train at least two types of machine learning algorithms (or regression models) in order to support data-driven decision making (LO4).

Section three requires students to report on the results of sections one and two in a document that presents the findings for a layman audience, with explicit recommendations based on the analyses (LO4). This section needs to be particularly rich in visualisation (LO2).

Task
(attach an assignment brief if required)

 See assignment details document

Guidelines/details of how to prepare your submission

See assignment details document

Expectations for group work

(if applicable)

Self-regulation: Make sure that you…

Include all your R script, so keep a log of what you’re doing so you don’t have to repeat yourself. Make sure to add comments in order to highlight what you are doing in each step.

Three key pieces of advice based on the feedback given to the previous cohort who completed this assignment

1. Do not miss seminars and workshops

2. Ask the instructors for help if you get stuck

3. Do not be put off by the steep learning curve of R. It gets easier as you work through the workshops.

Formative assessment opportunities/activities

Online quizzes, seminars, and workshops. These are NOT marked and you can repeat them as many times as you like.

Data analytics is an extremely popular field of study. As such you will have no problems finding

information from various sources. The lecture slides, worksheets, R workshops, videos, and audio

information that I provide should be your starting point. From there I recommend you look at books  (e.g., the Field et al., 2012, which is the other recommended textbook), online tutorials (particularly for examples in R), journal and conference papers (the last two are particularly important in order to understand how we present results of the analysis).