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Unit Title |
Data Mining |
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Unit Code |
B9DA103 |
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Unit Leader |
Terri Hoare |
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Level: |
9 |
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Assessment Title |
Application of Data Mining Tools&Techniques |
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Assessment Number |
2 |
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Assessment Type: |
Group (2-3 students) |
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Assessment Weighting |
30% |
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Issue Date: |
Week of 8 February 2021 |
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Hand in Date: |
Sunday 9 May 2021 (23:55) |
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Mode of Submission: |
On-line Moodle |
Working in groups of two-three students:- Select open data source(s); Determine what questions are to be answered; Apply the CRISP-DM methodology to analyse the data.
Reports to be completed and submitted in stages with the full report End Week 12
• End Week 6 (28/03 23:55) Project Proposal
• End Week 8 (11/04 23:55) CRISP-DM Business and Data Understanding
• End Week 10 (25/04 23:55) CRISP-DM Data Preparation, Modelling, Evaluation
• End Week 12 (9/05 23:55) CRISP-DM Deployment, Full Report All Stages
A Presentation should be prepared for the lectures during Week 12. It should include a ten-minute formal presentation followed by five minutes of questioning time from lecturers and the class. Have some slides prepared to cover any relevant back-up / detail for answering questions. Teams will be allocated time slots. Time limits will be enforced to allow all teams adequate time to present. The project will be evaluated as below based on the DBS Grading Scheme included with this document.
• Presentation /Artefact (.10) GROUP (2-3)
• Report (.20) INDIVIDUAL
Two files should be loaded to Moodle on or before Sunday 9th May 2021 (23:55).
1. A SINGLE pdf file named CA02_Surname_First-Name_Student-ID_lessons_learned_report.
2. A zipped file including your team artefact contents.
Key measurable objectives of the INFORMS Certified Analytics Professional exam are included. (Note:- for information only).
INFORMS CAP Certified Analytics Professional competencies Business Problem Framing
(Ability to understand a business problem and determine whether the problem is suitable for an analytics solution)
Objective 1: For the open data source selected, define the problem to be addressed Objective 2: Identify the stakeholders
Objective 3: Determine whether the problem is suitable for an analytics solution Objective 4: Refine the statement of the problem with any constraints
Objective 5: Define an initial set of business benefits
(Ability to reformulate a business problem into an analytics problem with a potential analytics solution)
Objective 1: Reformulate problem statement as an analytics problem Objective 2: Develop ABT attributes and outputs
Objective 3: State the set of assumptions related to the problem Objective 4: Define the key metrics of success
(Ability to work effectively with data to help identify quality issues and identify potential relationships that will lead to refinement of the business and analytics problem)
Objective 1: Identify data needs and sources Objective 2: Acquire data
Objective 3: Explore data visually
Objective 4: Harmonize, rescale and clean data
Objective 5: Document and report findings (e.g. quality report, data insights) Objective 6: Refine the business and analytics problem statements
(Ability to identify and select potential approaches/methods/algorithms for solving the business problem)
Objective 1: Identify potential problem solving approaches (methods) Objective 2: Select software tools
Objective 3: Test approaches (methods) Objective 4: Select approaches (methods)
(Ability to identify and build effective model structures to help solve the business problem)
Objective 1: Identify model structures Objective 2: Run and evaluate the models Objective 3: Calibrate models and data
Objective 4: Document and communicate findings (incl. assumptions, limitations, constraints)
(Ability to deploy the selected model to help solve the business problem)
Objective 1: Perform business validation of the model
Objective 2: Produce the report with findings and recommendations for deployment
PLEASE READ CAREFULLY
1. It is your responsibility to ensure your file is uploaded correctly.
2. Students are required to retain a copy of each assignment.
3. When an assignment is submitted, it is the student’s responsibility to ensure that the file is in the correct format and opens correctly.
4. Students should refer to the assessment regulations in their Course Guide.
5. DBS penalises students who engage in academic impropriety (i.e. plagiarism,
6. Collusion and / or copying). Please refer to the referencing guidelines on Moodle for information on correct referencing.
7. All relevant provisions of the Assessment Regulations must be complied with.
8. Penalties for late submission of assignments are as follows:
a. 25% penalty for assignments submitted within 5 working days of the deadline.
b. No marks for assignments submitted more than 5 working days after the deadline.
9. Extensions to assignment submission deadlines will be granted in exceptional circumstances only. The appropriate “Application for Extension” form must be used and supporting documentation (e.g. medical certificate) must be attached. Applications for extensions should be made directly to the Head of Year or Programme Leader in advance of the deadline date.
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