1. Intro to the capstone
1.1. Show off your **thought process** so that the interviewers can understand how you approach the problem
1.2. Tips for a good case study
1.2.1. Choose your projects
1.2.1.1. Select projects that resonate with your skills and career goals
1.2.2. Capture your process
1.2.2.1. Describe your strategies; discuss the tools you used; and explain the decisions you made along the way, as well as why you made them.
1.2.2.2. This insight gives potential employers a glimpse into your problem-solving abilities and thought processes.
1.2.3. Aesthetics matter
1.2.3.1. Your portfolio should be easy to navigate and visually appealing. Professionalism, simplicity, and engagement are key.
1.2.4. Tell your story
1.2.4.1. Clearly explain the background of each project. What challenges did you face? What steps did you take to overcome them? And what impact did your work have? Consider all of the people who you are communicating with. Who are they and what do they need to know about you?
1.3. In any occupation, across the globe, across various industries, having a knack and understanding of data is going to be crucial for everyone
2. Sample cases for data professionals
2.1. Important tips
2.1.1. Make sure your case study answers the question being asked
2.1.2. Make sure that you're communicating the steps you've taken and the assumptions you've made
2.1.3. The best porfolios are personal, unique, and simple
2.1.4. Make sure that your porfolio is relevant and presentable
2.2. Career paths in data
2.3. What to include in a case study
2.3.1. Introduction
2.3.1.1. Make sure to state the purpose of the case study. This includes what the scenario is and an explanation on how it relates to a real-world obstacle. Feel free to note any assumptions or theories you might have depending on the information provided.
2.3.2. Problems
2.3.2.1. You need to identify what the major problems are, explain how you have analyzed the problem, and present any facts you are using to support your findings.
2.3.3. Solutions
2.3.3.1. Outline a solution that would alleviate the problem and have a few alternatives in mind to show that you have given the case study considerable thought. Don’t forget to include pros and cons for each solution.
2.3.4. Conclusion
2.3.4.1. End your presentation by summarizing key takeaways of all of the problem-solving you conducted, highlighting what you have learned from this.
2.3.5. Next steps
2.3.5.1. Choose the best solution and propose recommendations for the client or business to take. Explain why you made your choice and how this will affect the scenario in a positive way. Be specific and include what needs to be done, who should enforce it, and when.
3. Case Study Roadmap
3.1. Ask
3.1.1. Guiding questions
3.1.1.1. What is the problem you are trying to solve?
3.1.1.2. How can your insights drive business decisions?
3.1.2. Key tasks
3.1.2.1. Indentify the business task
3.1.2.2. Consider key stakeholders
3.1.3. Deliverable
3.1.3.1. A clear statement of the business task
3.2. Prepare
3.2.1. Guiding questions
3.2.1.1. Where is your data located?
3.2.1.2. How is the data organized?
3.2.1.3. Are there issues with bias or creditability in this data?
3.2.1.4. How are you addressing licensing, privacy, security, and accessibility?
3.2.1.5. How did you verify the data's integrity?
3.2.1.6. How does it help you answer your question?
3.2.1.7. Are there any problems with the data?
3.2.2. Key tasks
3.2.2.1. Download data and store it appropriately
3.2.2.2. Identify how it's organized
3.2.2.3. Sort and filter the data
3.2.2.4. Determine the credibility of the data
3.2.3. Deliverable
3.2.3.1. A description of all data sources used
3.3. Process
3.3.1. Guiding questions
3.3.1.1. What tools are you choosing and why?
3.3.1.2. Have you ensured your data's integrity?
3.3.1.3. What steps have you taken to ensure that your data is clean?
3.3.1.4. How can you verify that your data is clean and ready to analyze?
3.3.1.5. Have you documented your cleaning process so you can review and share those results?
3.3.2. Key tasks
3.3.2.1. Check the data for errors
3.3.2.2. Choose your tools
3.3.2.3. Transform the data so you can work with it effectively
3.3.2.4. Document the cleaning process
3.3.3. Deliverable
3.3.3.1. Documentation of any cleaning or manipulation of data
3.4. Analyze
3.4.1. Guiding questions
3.4.1.1. How should you organize your data to perform analysis on it?
3.4.1.2. Has your data been properly formatted?
3.4.1.3. What surprises did you discover in the data?
3.4.1.4. What trends or relationships did you find in the data?
3.4.1.5. How will these insights help answer your business questions?
3.4.2. Key tasks
3.4.2.1. Aggregate your data so it's useful and accessible
3.4.2.2. Organize and format your data
3.4.2.3. Perform calculations
3.4.2.4. Indentify trends and relationships
3.4.3. Deliverable
3.4.3.1. A summary of your analysis
3.5. Share
3.5.1. Guiding questions
3.5.1.1. Were you able to answer the question of how annual members and casual riders use Cyclistic bikes differently?
3.5.1.2. What story does your data tell?
3.5.1.3. How do your findings relate to your original question?
3.5.1.4. Who is your audience? What is the best way to communicate with them?
3.5.1.5. Can data visualization help you share your findings?
3.5.1.6. Is your presentation accessible to your audience?
3.5.2. Key tasks
3.5.2.1. Determine the best way to share your findings
3.5.2.2. Create effective data visualizations
3.5.2.3. Present your findings
3.5.2.4. Ensure your work is accessible
3.5.3. Deliverable
3.5.3.1. Supporting visualizations and key findings
3.6. Act
3.6.1. Guiding questions
3.6.1.1. What is your final conclusion based on your analysis?
3.6.1.2. How could your team and business apply your insights?
3.6.1.3. What next steps would you or your stakeholders take based on your findings?
3.6.1.4. Is there additional data you could use to expand on your findings?
3.6.2. Key tasks
3.6.2.1. Create your portfolio
3.6.2.2. Add your case study
3.6.2.3. Practice presenting your case study to a friend or family member
3.6.3. Deliverable
3.6.3.1. Your top three recommendations based on your analysis