Data Analytics 7BSP1361

Module Title:

Data Analytics

Module Code:

7BSP1361

Assignment Format & Maximum Word count

Individualised Excel Worksheet

Assignment weighting

30%

Coursework Submission:

Time: 23:59

Date: 28 Apr 2025

Method: Canvas

Coursework returnDate returned to students:

Within 4 weeks

Module Leader

Neil Spencer / Joe Zhao

First marker

Joe Zhao

Internal Moderator

Approved?

Date:

Module Board name

BAS

External Examiner

Approved

Date: N/A

Module Board date

TBA

Module eligible for an extension on submission date (subject to UPRs)

YES /

NO

Assessment Criteria

Learning Outcomes: Knowledge and Understanding assessed in this assignment:

Recommend which visualisation and summary techniques to apply to data in a business context.

Determine, critically evaluate and advise on which aspects of statistical methodology apply to problems occurring in a business context.

Learning Outcomes: Skills and Attributes assessed in this assignment:

Create summaries of data appropriate to the business problem that exists.

Communicate the nuances and ambiguities of statistical analyses they [the students] have undertaken and conclusions drawn from these.

Evaluate output produced by software packages used to implement statistical techniques.

Transformational Opportunities:

E.g. Use LinkedIn Learning to improve skills

Feedback /Marking criteria for this Assignment

Performance will be assessed using the Mark schemegiven below

Feedbackfor improvement will be given in writing viayourCanvasmodulesitewithin 4 weeks of submission

Lateness Penalty:For each day or part day up to five days after the published deadline, coursework relating to modules submitted late will have the numeric grade reduced by 10 grade points until or unless the numeric grade reaches the minimum pass mark (UG 40/PG 50). Where the numeric grade awarded for the assessment is less than the minimum pass mark no lateness penalty will be applied. If the coursework is submitted more than 5 days after the published deadline, it will not be marked and a grade of zero will be awarded.Please note:Referred coursework submitted after the published deadline will be awarded a grade of zero (0).

Extensions:Students do not have an automatic right to an extension. If you require an extension, this must be requested in advance of the submission deadline. Please give your reason(s) for needing an extension.

Detailed Brief for Individual Assignment

Assignment Title: Supervised Learning

Description of the assignment, task, content and structure:

The dataset classifies people described by a set of 20 attributes as good or bad credit risks. Details of the dataset can be found in the german_credit_description file.

Each student will be provided with a unique dataset which is a subset of the above main dataset, and should carry out statistical analysis using R. The analysismustbe carried out using the three supervised learning methods covered in the class. These three methods are decision trees, logistic regression, and discriminant analysis.

Students then need to answer questions in theSupervised Learning Assignment Workbook, and submit the workbook viaCanvas.

The deadline for submission is 28 April 2025.

Any specific instructions:

Datasets will be distributed to students in Semester B.

Academic Integrity, Plagiarism and Essay Mills

• You areNOTallowedtocopyany information into your assignment without using quotation marks and a reference this is plagiarism (a type of academic misconduct).

• You are NOT allowed to copy from other students (or allow other students to copy from you) this is collusion.

• You are NOT allowed to copy from your own assignments on other modules this is self-plagiarism.

• You must NEVER buy assignmentsfrom websites (essay mills) this is called contract cheating and it is now illegal in the UK.

• If you commit academic misconduct, your mark will be reduced, and you will face disciplinary action for repeat offences.

Contactacademic-skills@herts.ac.ukif you are unsure of the rules or how to avoid academic misconduct, and you will receive help.

Student Support and Guidance

For further help on module content and assignment details, contact yourModule Leaderin his or her drop-in / office hours or by email.

Use theLearning OutcomesandHBS Grading Criteria(Rubric) to help inform you of theexpectations of the assessment.

Use CASE (Centre for Academic Skills Enhancement) websiteresources:http://go.herts.ac.uk/CASE. AttendCASE workshops and drop-ins to develop academic skills to meet HBS expectations(see timetable on CASE website homepage or drop-in to CASE in L064, in the LRC).

Visit theAcademic English for Business Programme Sitefor tips on developing your academic English and contacthbsacademic-english@herts.ac.ukif you have any questions.

For help with Turnitin, look for the Check your work (Turnitin Originality Report) practice assignment in the Assignment section of all of your modules. For help with understanding plagiarism and how to make changes to your assignment, contacthbsacademic-english@herts.ac.uk.

Use theOnline Libraryto access quality business information resources:

oLibrary Searchwill help you find books, journal articles and more.

oUse theSubject Toolkit for Businessto access to industry standard databases.

oUseLibrary SkillUPfor guidance for searching and referencing.

oGet help:use SkilIUP module chat, visit theStudy Success Hub, orbook an 1:1 with a librarian.