Capstone Instructions

For the iDEW Data Analytics Cohort 2 students.

Note: The video refers to Cohort 1, but the same instructions apply to Cohort 2 for 2023 - 2024.

Objectives

Your capstone project will showcase your new skills in data analytics, and you will:

determine a topic and find an appropriate dataset
conduct an initial investigation of the data using a spreadsheet
frame a case study by identifying the specific problem to investigate and the project goals
prepare a full dataset, clean the data, and process the data using tools of your choice -- SQL in Big Query, R in R Studio, or Kaggle (having options for R, Python, and SQL).
prepare refined charts, whether using R, Tableau, Spreadsheets, or Python.
record a presentation that summarizes your capstone project process and conclusions
prepare your LinkedIn portfolio

Case Study Packets from Google Data Analytics Course

You may choose to follow one of these case study packets for your project.

Case Study 1 Case Study 2 Case Study 3

Option: Basic Data Analytics Capstone Example R Notebook

This file can be used as a starting point for a data notebook that also provides an example of a problem statement etc.

Guidance

Take this opportunity to dig deep into a project that you will be proud of. 🚀 It can be a key component of your emerging portfolio.

The following steps can be adapted as needed, and you may choose to follow the case study packets more closely. It is your choice. Keep in mind the knowledge and guidance from the Google Data Analytics course 8 - week 1.

Research potential topics for your capstone case study and choose one. (ASK) It is recommended that you initially consider three distinct ideas before narrowing down to one, unless you are already very confident about one topic. Ultimately, you want to choose a topic that interests you and that you can locate an appropriate dataset to study. Finding a workable dataset may prove to be one of the most challenging tasks. If you must, you can use one of the two prepared case studies from the Google Data Analytics course 8, but it is highly recommended that you find a unique topic of your own.
Develop a draft problem statement and the project goals (ASK) You will want to be as clear and concise as possible, but you can refine this as you learn more about the data. Be sure to consider the various stakeholders in the problem.
Identify your primary dataset and conduct a quick investigation of the data in a spreadsheet. (PREPARE)
- Import a subset of the larger dataset into a spreadsheet
- Sort, format, apply functions, and chart the data as needed to look for specific opportunities to investigate as well identifying likely challenges of working with the data. Can you find an interesting relationship in the data that may reveal a solution to a problem? Does the data need to be cleaned in particular ways to avoid errors or bias?
- Record notes on your findings and save your spreadsheet for reference later.
Prepare and clean the dataset as needed. (PREPARE) While you may be using R studio, you can optionally use an R notebook or Python notebook on Kaggle. This is an opportunity to use SQL in your notebook to demonstrate your new skills. You can find example notebooks for importing a CSV file, applying SQL, and creating charts here: 👉 R Notebook Template or Python Notebook Template Also, use the Markdown text blocks to document your process, as you will want to include this in your presentation and documentation.
Process the data for analysis. (PROCESS) Create new perspectives on the data that you may need by creating new columns of data or aggregating the data. For example, you may divide the values of one column of data by the values of another column to get a ratio, or you may find the average of a column of filtered data.
Analyze the data consistent with project objectives. (ANALYZE) Look for pertinent trends, relationships, and patterns that help inform your investigation. Generate quick charts as needed to visually analyze the data.
Produce well-formatted charts for displaying key relationships in your analysis. (SHARE) You may choose to continue using your R Studio or your R notebook (or Python) for generating these charts, but you may choose to export CSV files from your data cleaning step into Tableau for advanced visualization. Any of these directions can be a good choice. You want to make sure your charts are titled, labeled, and styled in a clear manner for communicating to a broad audience. You want at least two compelling charts, if not more.
Reflect on your results and determine your conclusions from the project. (ACT) What actions would do you recommend regarding the project goals based on the data analysis? What might you recommend for further investigation? Make sure your results and conclusion address (and are coherent with) your initial problem statement and goals.
The last step is to share your work, which is covered in the next section.

★ Final Submission Instructions

Develop a slide deck of key points and artifacts from your capstone project. (SHARE) Apply the presentation principles and guidelines from course 6 of the Google Data Analytics series. You can use the following outline.
- Project Title, Date, and Your Name
- Project Background: What is the context of your project and pertinent information? What are your data sources?
- Problem Statement and Objectives: Provide a clear and concise problem statement with a simple list of objectives for the project.
- Notes on Collecting and Reviewing the Data: What did you notice when importing your data into a spreadsheet or other tool? Was there any missing data or format issues? Did it look like you could process the data the way you would like?
- Processing and Visualizing the Data: Summarize the methods you used to process the data and show the resulting charts that reveal relationships in the data.
- Results: What did the study reveal? Could any conclusions be drawn or is a more in depth study needed? What are you recommendations going forward? Include any reflections on the process and how it has influenced your learning or career plans.
- Links Make sure all your links are accessible by others.
  - Link to your video screen share presentation of your project You can use a platform like Loom.com. Also remember that you will add this link to your slide deck after you record the video!
  - Link to your spreadsheet file on Google Drive Include this even if this wasn't your primary tool for evaluation.
  - Link to your data notebook on Kaggle Include this even if this wasn't your primary tool for evaluation.
  - Link to your LinkedIn Profile We want to stay connected.
  - Link to your Google Data Analytics Professional Certificate from Credly! 🎉
  - Attach anything else that may be part of your project, like Tableau work.
Record a screen captured video of your presentation. (SHARE) Include an on-screen video of you in the corner as you present your slides and demonstrations. You can use key screen shots of your spreadsheets and R notebooks in your slides, but you can also do quick demonstrations of the items directly and come back to the slides. Be sure to include your highlighted charts with a full explanation of the insight they bring. Aim to have a presentation of about 3 to 5 minutes.

CELEBRATE YOUR GREAT WORK 🎉

For Reference

Screenshot from https://www.coursera.org/learn/google-data-analytics-capstone/lecture/vDmXA/introducing-the-capstone-project

Excerpt from https://www.coursera.org/learn/google-data-analytics-capstone/supplement/J4RDY/your-portfolio-and-case-study-checklist 👇

What to include in a case study
During your interview process, you will very likely encounter the case study interview. In this interview, you will be provided with a business-related scenario where you analyze a problem and come up with the best solution. You will have a certain amount of time to solve this so it is best to be prepared for any scenario you are given. A great case study will include the following:
Introduction: 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.
Problems: 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.
Solutions: 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.
Conclusion: End your presentation by summarizing key takeaways of all of the problem-solving you conducted, highlighting what you have learned from this.
Next steps: 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.
Example Case Study Questions Etc.: https://www.holistics.io/blog/startup-data-analyst-interview-case-studies/

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Objectives

Your capstone project will showcase your new skills in data analytics, and you will:

determine a topic and find an appropriate dataset

conduct an initial investigation of the data using a spreadsheet

frame a case study by identifying the specific problem to investigate and the project goals

prepare a full dataset, clean the data, and process the data using tools of your choice -- SQL in Big Query, R in R Studio, or Kaggle (having options for R, Python, and SQL).

prepare refined charts, whether using R, Tableau, Spreadsheets, or Python.

record a presentation that summarizes your capstone project process and conclusions

prepare your LinkedIn portfolio

Guidance

Take this opportunity to dig deep into a project that you will be proud of. 🚀 It can be a key component of your emerging portfolio.

Research potential topics for your capstone case study and choose one. (ASK) It is recommended that you initially consider three distinct ideas before narrowing down to one, unless you are already very confident about one topic. Ultimately, you want to choose a topic that interests you and that you can locate an appropriate dataset to study. Finding a workable dataset may prove to be one of the most challenging tasks. If you must, you can use one of the two prepared case studies from the Google Data Analytics course 8, but it is highly recommended that you find a unique topic of your own.

Develop a draft problem statement and the project goals (ASK) You will want to be as clear and concise as possible, but you can refine this as you learn more about the data. Be sure to consider the various stakeholders in the problem.

Identify your primary dataset and conduct a quick investigation of the data in a spreadsheet. (PREPARE)

Import a subset of the larger dataset into a spreadsheet
Sort, format, apply functions, and chart the data as needed to look for specific opportunities to investigate as well identifying likely challenges of working with the data. Can you find an interesting relationship in the data that may reveal a solution to a problem? Does the data need to be cleaned in particular ways to avoid errors or bias?
Record notes on your findings and save your spreadsheet for reference later.

Prepare and clean the dataset as needed. (PREPARE) While you may be using R studio, you can optionally use an R notebook or Python notebook on Kaggle. This is an opportunity to use SQL in your notebook to demonstrate your new skills. You can find example notebooks for importing a CSV file, applying SQL, and creating charts here: 👉 R Notebook Template or Python Notebook Template Also, use the Markdown text blocks to document your process, as you will want to include this in your presentation and documentation.

Process the data for analysis. (PROCESS) Create new perspectives on the data that you may need by creating new columns of data or aggregating the data. For example, you may divide the values of one column of data by the values of another column to get a ratio, or you may find the average of a column of filtered data.

Analyze the data consistent with project objectives. (ANALYZE) Look for pertinent trends, relationships, and patterns that help inform your investigation. Generate quick charts as needed to visually analyze the data.

Produce well-formatted charts for displaying key relationships in your analysis. (SHARE) You may choose to continue using your R Studio or your R notebook (or Python) for generating these charts, but you may choose to export CSV files from your data cleaning step into Tableau for advanced visualization. Any of these directions can be a good choice. You want to make sure your charts are titled, labeled, and styled in a clear manner for communicating to a broad audience. You want at least two compelling charts, if not more.

Reflect on your results and determine your conclusions from the project. (ACT) What actions would do you recommend regarding the project goals based on the data analysis? What might you recommend for further investigation? Make sure your results and conclusion address (and are coherent with) your initial problem statement and goals.

The last step is to share your work, which is covered in the next section.

★ Final Submission Instructions

Develop a slide deck of key points and artifacts from your capstone project. (SHARE) Apply the presentation principles and guidelines from course 6 of the Google Data Analytics series. You can use the following outline.

Project Title, Date, and Your Name
Project Background: What is the context of your project and pertinent information? What are your data sources?
Problem Statement and Objectives: Provide a clear and concise problem statement with a simple list of objectives for the project.
Notes on Collecting and Reviewing the Data: What did you notice when importing your data into a spreadsheet or other tool? Was there any missing data or format issues? Did it look like you could process the data the way you would like?
Processing and Visualizing the Data: Summarize the methods you used to process the data and show the resulting charts that reveal relationships in the data.
Results: What did the study reveal? Could any conclusions be drawn or is a more in depth study needed? What are you recommendations going forward? Include any reflections on the process and how it has influenced your learning or career plans.
Links Make sure all your links are accessible by others.
- Link to your video screen share presentation of your project You can use a platform like Loom.com. Also remember that you will add this link to your slide deck after you record the video!
- Link to your spreadsheet file on Google Drive Include this even if this wasn't your primary tool for evaluation.
- Link to your data notebook on Kaggle Include this even if this wasn't your primary tool for evaluation.
- Link to your LinkedIn Profile We want to stay connected.
- Link to your Google Data Analytics Professional Certificate from Credly! 🎉
- Attach anything else that may be part of your project, like Tableau work.

Record a screen captured video of your presentation. (SHARE) Include an on-screen video of you in the corner as you present your slides and demonstrations. You can use key screen shots of your spreadsheets and R notebooks in your slides, but you can also do quick demonstrations of the items directly and come back to the slides. Be sure to include your highlighted charts with a full explanation of the insight they bring. Aim to have a presentation of about 3 to 5 minutes.

Submit a shareable link to your slide deck for your capstone project presentation in the survey below. Link Coming Soon... Remember, your slide deck should include all the links listed in item 9 above.

For Reference

What to include in a case study

During your interview process, you will very likely encounter the case study interview. In this interview, you will be provided with a business-related scenario where you analyze a problem and come up with the best solution. You will have a certain amount of time to solve this so it is best to be prepared for any scenario you are given. A great case study will include the following:

Introduction: 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.
Problems: 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.
Solutions: 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.
Conclusion: End your presentation by summarizing key takeaways of all of the problem-solving you conducted, highlighting what you have learned from this.
Next steps: 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.

Example Case Study Questions Etc.: https://www.holistics.io/blog/startup-data-analyst-interview-case-studies/