CIS7031 – Programming for Data Analysis

 

 

 

 

CIS7031 – Programming for Data Analysis 20 Credit Hours

 

 

Semester

 

Assessment Brief

 

 

Assessment Title:

 

Develop and evaluate a prediction model using various data science techniques

 

WRIT1 100 %

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Contents

 

Learning Outcomes…………………………………………………………………………………………………. 3

 

EDGE……………………………………………………………………………………………………………………… 3

 

Assessment Requirements / Tasks (include all guidance notes)…………………………….. 4

 

Assessment Criteria………………………………………………………………………………………………… 4

 

Submission Details…………………………………………………………………………………………………. 4

 

Feedback………………………………………………………………………………………………………………… 6

 

Marking Criteria……………………………………………………………………………………………………….. 6

 

Additional Information…………………………………………………………………………………………….. 7

 

Referencing Requirements (Harvard)………………………………………………………………….. 7

 

Mitigating Circumstances…………………………………………………………………………………….. 7

 

Unfair Practice…………………………………………………………………………………………………….. 7

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Learning Outcomes

 

This assessment is designed to demonstrate a student’s completion of the following

Learning Outcomes:

 

• Critically analyse and evaluate various statistical and computational techniques for analysing datasets and determine the most appropriate technique for a business problem;

 

• Critically evaluate, develop and implement solutions for processing datasets and solving complex problems in various environments using relevant programming paradigms;

 

• Evaluate and apply key steps and issues involved in data preparation, cleaning, exploring, creating, optimizing and evaluating models;

 

• Evaluate and apply aspects of data science applications and their use.

 

EDGE

 

The Cardiff Met EDGE supports students in graduating with the knowledge, skills, and attributes that allow them to contribute positively and effectively to the communities in which they live and work.

 

This module assessment provides opportunities for students to demonstrate development of the following EDGE Competencies:

 

ETHICAL	Students will be required to consider Ethical implication of
	their analysis and follow the necessary ethical approval
	processes while addressing problems associated with the
	assessment.
DIGITAL	Students will be required to demonstrate digital skills in the
	collation of data and analysis for their project.
GLOBAL	Students  will  demonstrate  an  awareness  of  the  global
	context and apply this to their assessment
ENTREPRENEURIAL	Students   will   also   demonstrate   their   developed
	entrepreneurial through working under their own initiative,
	formulating and presenting recommendations in order to
	solve an authentic and complex problem associated with
	the module.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Assessment Requirements / Tasks (include all guidance notes)

 

For this assignment you are asked to analyse, develop and evaluate a prediction model using any dataset of your choice and critically analyse the various techniques used. Although you can choose the dataset, it needs to be approved by the teaching team. The work will involve steps for data preparation, cleaning and exploring the dataset. You also need to compile a report containing the following sections.

 

• Analysis and interpretation of results

 

• Critical review of the techniques used (3000 words approx.)

 

• Reflect upon your experience

 

• A short (less than 5 minutes) video summarizing the key points of your coursework. This video will be a useful addition to your Personal Development Portfolio and will help you when applying for jobs. You can create this video using CAM Studio or some other screen capture software such as recordMyDesktop (works only on Linux). Please note that you need to upload your video to Moodle and include a youtube link in report.

 

• Include relevant references to the source materials, techniques and tools used.

 

Assessment Criteria

 

The Academic Handbook identifies the appropriate level as displaying mastery of a complex and specialised area of knowledge and skills and demonstrating expertise in highly specialised and advanced technical, professional and/or research skills.

 

Criteria	Marks
Data preparation and exploration	15%
Develop  and  evaluate  prediction  model.  This  should  include	40%
analysis and interpretation of evaluation results.
Critical review and analysis of techniques used	35%
Video presentation	10%

 

 

Submission Details

 

Please see Moodle for confirmation of the Assessment submission date.

 

Any assessments submitted after the deadline will not be marked and will be recorded as a Non-Attempt.

 

The assessment must be submitted through the Turnitin submission point in Moodle

 

Your assessment should be titled with your Student ID Number, module code and assessment id, e.g. st12345678 CIS4000 WRIT1

 

 

 

 

 

 

 

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Submit the following files via Moodle.

 

1. Source code in a single zip file.

 

2. Dataset in a single zip file. If the dataset is too big you can submit a link to it as well.

 

3. Single document containing the report. This document should be submitted in both word and PDF versions.

 

4. The report should contain link to the video presentation.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Feedback

 

Feedback for the assessment will be provided electronically via Moodle, and will normally be available 4 working weeks after initial submission. The feedback return date will be confirmed on Moodle.

 

Feedback will be provided in the form of a rubric and supported with comments on your strengths and the areas which you improve.

 

All marks are preliminary and are subject to quality assurance processes and confirmation at the Examination Board.

 

Further information on the Academic and Feedback Policy in available in the Academic Handbook (Vol 1, Section 4.0)

 

Marking Criteria

 

70 – 100%	Addressed all sections and provided correct answers with elegant
(1st)	presentation of results. Applied correct data analysis approaches and
	provided excellent interpretation on each section.
60-69%	Addressed  all  sections  and  provided  correct  answers  with  good
(2:1)	presentation  of  results.		Applied  mostly  correct		data  analysis
	approaches and provided very good interpretation on each section.
50-59%	Addressed most of the sections and provided mostly correct answers
(2:2)	with	average  presentation	of  results.   Applied  some  correct  data
	analysis approaches and provided an average interpretation on each
	section.
40-49%	Addressed  few  sections  with  few  correct  answers  with/out  any
(3rd)	presentation  of  results.		Applied  mostly  incorrect		data  analysis
	approaches and provided poor interpretation on each section.
35-39%	Addressed few sections and provided mostly incorrect answer with
(Narrow	poor	presentation  of  results.		Applied  incorrect	data  analysis
Fail)	approaches and provided poor interpretation.
<35%	Very poor report missing one or more required parts.
(Fail)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Additional Information

 

Referencing Requirements (Harvard)

 

The Harvard (or author-date) format should be used for all references (including images).

 

Further information on Referencing can be found at Cardiff Met’s Academic Skills website.

 

 

Mitigating Circumstances

 

If you have experienced changes or events which have adversely affected your academic performance on the assessment, you may be eligible for Mitigating Circumstances (MCs). You should contact your Module Leader, Personal Tutor or Year Tutor in the first instance.

 

An application for MCs, along with appropriate supporting evidence, can be submitted via the following link to the MCs Dashboard

 

Applications for MCs should ideally be submitted as soon as possible after circumstances occur & at the time of the assessment. Applications must be submitted before the relevant Examination Board.

 

Further information on the Mitigating Circumstances procedure is available in the Academic Handbook (Volume 1, Section 5)

 

 

Unfair Practice

 

Cardiff Metropolitan University takes issues of unfair practice extremely seriously. The University has distinct procedures and penalties for dealing with unfair practice in examination or non-examination conditions. These are explained in full in the University’s Unfair Practice Procedure (Academic Handbook: Vol 1, Section 8)

 

Types of Unfair Practice, include:

 

Plagiarism, which can be defined as using without acknowledgement another person’s words or ideas and submitting them for assessment as though it were one’s own work, for instance by copying, translating from one language to another or unacknowledged paraphrasing. Further examples include:

 

• Use of any quotation(s) from the published or unpublished work of other persons, whether published in textbooks, articles, the Web, or in any other format, which quotations have not been clearly identified as such by being placed in quotation marks and acknowledged.

 

• Use of another person’s words or ideas that have been slightly changed or paraphrased to make it look different from the original.

 

• Summarising another person’s ideas, judgments, diagrams, figures, or computer programmes without reference to that person in the text and the source in a bibliography or reference list.

 

• Use of services of essay banks and/or any other agencies.

 

• Use of unacknowledged material downloaded from the Internet.

 

 

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• Re-use of one’s own material except as authorised by the department.

 

Collusion, which can be defined as when work that that has been undertaken with others is submitted and passed off as solely the work of one person. An example of this would be where several students work together on an assessment and individually submit work which contains sections which are the same. Assessments briefs will clearly identify where joint preparation and joint submission is specifically permitted, in all other cases it is not.

 

Fabrication of data, making false claims to have carried out experiments, observations, interviews or other forms of data collection and analysis, or acting dishonestly in any other way.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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