Assignment Description

In this assignment, you will be asked to identify a real-world problem that could be solved using data science techniques and the CRISP-DM framework we defined in Week 1. Please note that this assignment follows the CRISP-DM process for data mining, which is a widely used methodology for approaching data science problems. It does not require programming or technical skills, but it requires you to think critically about real-world problems and potential solutions using data science techniques. It also allows you to develop communication skills by summarizing technical concepts for a general audience.

Please note: this is an individual assignment.

Assignment Structure

To complete the assignment, please follow the steps below.

Steps

1. Business Understanding: Identify a real-world problem that could be solved using data science techniques and define the objectives of the analysis. Examples could include predicting customer churn for a telecommunications company or identifying fraudulent activity in financial transactions.
2. Data Understanding: Research potential data sources for the problem and evaluate their suitability for analysis. Consider the size and complexity of the data as well as any limitations or biases that may exist.
3. Data Preparation: Identify potential problems that may emerge in collecting, cleaning, and preprocessing the data. You can get a sense of this by researching similar case studies to the one you have selected. Remember that data preparation includes removing missing or irrelevant data, converting categorical variables to numeric variables, and addressing any other issues identified during the data understanding phase.
4. Modeling: discuss potential approaches to model the problem and evaluate potential solutions. This may involve using predictive modeling, natural language processing, or machine learning, depending on the problem and data sources. Think about what analytical approach you will use in analyzing your data (high code versus low and no code) and justify your choice.
5. Evaluation: In this stage, you only need parameters that might be used to assess the accuracy and precision of your model and whether you need to use alternative models to compare.
6. Deployment: Prepare a report summarizing the problem, data sources, and potential solutions, and communicate the results to stakeholders. Consider the implications of the analysis for the business or industry, and identify any further steps that may be necessary. Also, make sure you use the elements of effective communication you learned in Week 3.
7. Process documentation: You will need to provide process documentation (see page here) and upload this as part of your assignment. (Check below the details)
8. Bibliography: Please provide a bibliography using an accepted reference style of your choice (e.g., APA, MLA, etc.) You may use this page as a guide. You must also cite work within your project.

As part of this project, you may utilize ChatGPT or other generative AI tools. Adhere to the following ethical guidelines:

• Attribution: Clearly credit any AI-generated content or assistance in your project.
• Originality: Use AI as a tool for ideation and problem-solving, not to replace your original thought process and contribution.
• Integrity: Do not use AI to generate misleading or falsified content or data.
• Fair Use: Use AI-generated outputs within the scope of fair use, avoiding copyright infringements.
• Privacy: Ensure that the use of AI respects privacy, does not disclose sensitive information, and complies with applicable data protection laws.
• Bias Awareness: Be critical of potential biases in AI outputs and address them in your methodology.
• Transparency: When presenting your findings, be transparent about the extent of AI usage and its impact on your results.

Assignment Format

You must submit a 3-5-page Word or PDF document. To get full credit for this assignment, please be sure to address all of the points mentioned above in the "deployment" section.

(Related to Point 7)

Process Documentation in this Course

In the previous section, you saw that you will need to provide process documentation for the final assignment. In this section, you will see what this entails.

What Should You Include in Process Documentation?

When carrying out research and completing in the assignment, you should engage in:

• Initial Brainstorming: Record your initial ideas, questions, and thoughts about the assignment prompt. This could include mind maps, concept sketches, conversations with artificial intelligence, or freewriting exercises.
• Drafts and Revisions: Save multiple drafts of your assignment as you progress. Highlight changes, revisions, and improvements made during each iteration.
• Research Notes: Document your research process, including sources consulted, key findings, and ideas synthesized from the literature.

How Should You Organize Process Documentation?

Organize your process documentation in a clear and systematic manner. Consider creating a folder or digital workspace dedicated to each assignment, where you can store all relevant documents and materials. Label files with dates and descriptions to track your progress effectively. You will need to upload this documentation along with your final assignment.

By actively engaging in process documentation, you can enhance your learning experience and produce high-quality work that reflects your academic growth.

If you have any questions or concerns about process documentation, please don't hesitate to reach out to your learning facilitator for clarification and guidance.

Criteria

Ratings

Pts

This criterion is linked to a Learning OutcomeUsing the CRISP-DM frameworkFollow the steps in the CRISP-DM framework

20 ptsFull Marks You show a thorough understanding of the CRISP-DM framework and have followed the steps it lays out in an organized and systematic manner.

12 ptsPass You have followed most of the steps in the CRISP-DM framework in an organized manner.

4 ptsFail You have not followed the CRISP-DM framework or you have done so insufficiently and in a disorganized manner.

20 pts

This criterion is linked to a Learning OutcomeProblem UnderstandingExtent of your understanding of the problem

15 ptsFull Marks You have shown a comprehensive understanding of the problem, leveraging data science techniques.

9 ptsPass You understand the real-world problem sufficiently, but there may be some confusion or lack of depth in your explanation.

3 ptsFail You do not clearly understand the problem and your solution does not leverage data science. There is significant confusion and a lack of clarity.

15 pts

This criterion is linked to a Learning OutcomeCritical thinkingAbility to think critically about the problem and how data science techniques could be applied to it

25 ptsFull Marks Excellent critical thinking that proposes a solution that effectively leverages data science techniques that fit the identified problem. Your solution is very granular and goes into detail on how and why the solution fits the problem at hand.

15 ptsPass Strong critical thinking that proposes a solution that leverages data science techniques that fit the identified problem. However, there may be some areas for improvement or lack of clarity. Your solution is somewhat granular and touhes on how or why the proposed solution fits the problem at hand.

5 ptsFail You have not shown critical thinking in your data science-based solution and there is some confusion and lack of clarity. Your solution is too general does not go into detail on how or why it fits the problem at hand.

25 pts

This criterion is linked to a Learning OutcomeCommunicationAbility to communicate with a non-technical audience

25 ptsFull Marks Communication is clear, concise, and appropriate for a non-technical audience. You have used simple language and easy-to-understand examples.

15 ptsPass Communication is mostly clear and appropriate for a non-technical audience, although there may be some areas where technical concepts are not explained well.

5 ptsFail Communication is neither clear nor appropriate for a non-technical audience, and there is a lot of confusion and lack of clarity when it comes to technical concepts.

25 pts

This criterion is linked to a Learning OutcomeProcess documentation and personal reflection providedProviding drafts, notes, or other forms of process documentation along with a personal reflection on the project and processes involved

10 ptsFull Marks You provide comprehensive process documentation, including drafts, notes, or other relevant materials that clearly demonstrate your progression of thought and engagement with the material. You have provided a deep reflection on your work and the process of carrying it out.

6 ptsPass You provide comprehensive process documentation, including drafts, notes, or other relevant materials that clearly demonstrate your progression of thought and engagement with the material. You have provided a reflection on your work and the process of carrying it out.

2 ptsFail You provide limited process documentation, with minimal drafts, notes, or other relevant materials that only partially demonstrate your progression of thought and engagement with the material. You have not provided a reflection on your work and the process of carrying it out.

10 pts

This criterion is linked to a Learning OutcomeBibliographyBibliography provided in an accepted reference style with correct citations

5 ptsFull Marks Your bibliography is impeccably organized and adheres to the required reference style with all citations correctly formatted.

3 ptsPass Your bibliography is well-organized and mostly adheres to the required reference style, with minor formatting errors in citations.

1 ptsFail Your bibliography lacks organization and does not consistently adhere to the required reference style, with several formatting errors in citations.

5 pts

Total Points: 100