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Cohort 2 - Academic Year 2023 - 2024
Apply here 👉 https://iu.co1.qualtrics.com/jfe/form/SV_cAWDQ8G7Mduxi9o
iDEW from the IU Luddy School of Informatics, Computing, and Engineering in Indianapolis is offering an opportunity for high school students to accomplish the following four objectives in the 2023-24 academic year – nearly a $4,000 value for FREE! 💰
"Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are 380,000 U.S. job openings in data analytics with a $74,000 median entry-level salary." — Google on Coursera
https://www.coursera.org/professional-certificates/google-data-analytics
By successfully completing this program students will earn 12 credits in the following four courses at Luddy. https://luddy.iupui.edu/
* Individual courses may change before the beginning of the Fall semester.
While each high school may approach this differently, credit will be provided for the class time allocated for this program.
Students participating in the iDEW Data Analytics Cohort become IU student through the SPAN program and receive an IU Crimson Card, the IU community ID card, providing access to many resources. While the coursework is not completed on the IU campus, students will visit the campus and have access to resources throughout the year, if they choose.
Students will largely work independently as they complete a series of eight courses on the Coursera platform. They will have ordinary class time available to complete the work, but they will most-likely need to complete some work outside of this class time.
The Coursera content is based on videos, reading, practice activities, and quizzes. IU Luddy will also require the submission of an assignment at the end of each Coursera course demonstrating their knowledge and skills learned. The final submission to Luddy will be a comprehensive project that will also serve as a great portfolio piece for internships or entry-level opportunities.
While students can self-pace their work, the iDEW team will providing pacing guidelines and coaching support as needed. The iDEW team is committed to every student succeeding and will closely monitor progress. We have found that the primary challenge for students is allocating time to complete the material and staying engaged consistently throughout the program.
Students will earn six IU Luddy credits for successfully completing the first four Google Data Analytics courses (the first half of the Google certification) in the Fall semester and submitting the four associated Luddy assignments. Students will earn six more IU Luddy credits for completing the last four Google Data Analytics courses (the second half of the Google certification) in the Spring semester and submitting the four associated Luddy assignments.
Successful Application There is limited space in the program and the following criteria will be important during the selection process.
Short Essay on Interest in Program
Minimum 3.0 GPA (or equivalent)
Teacher Recommendation
Statement of Commitment to Complete the Program
Completion of the Program Orientation During the summer an orientation to the program will be offered online and in-person to accommodate busy schedules, but it will be important for student success.
Consistent Participation, Progress and Communication This program has a very ambitious schedule, and students need to be committed to consistent participation, progress, and communication.
A few more useful notes:
The Google Data Analytics Professional Certificate courses are available to the public, but the iDEW coaching and Luddy Credit is only offered through this cohort program at this time.
Student progress will be evaluated at key intervals to ensure the appropriate progress is being made to justify enrollment in Luddy courses. We want to do everything possible to prevent any student recording a non-passing grade at IU.
Students may decide to begin the Google Data Analytics Courses on Coursera over the summer to get ahead, leaving room for further exploration and learning in the regular academic semesters.
For the iDEW Data Analytics Cohort 1 students.
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
You may choose to follow one of these case study packets for your project.
Case Study 1 Case Study 2 Case Study 3
This file can be used as a starting point for a data notebook that also provides an example of a problem statement etc.
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.
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 on LaunchBoard. https://launchboard.app/cohort/xZo8qiayeig5jRdU4MhR Remember, your slide deck should include all the links listed in item 9 above.
What to include in a case studyDuring 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/
Summer Cohort - 2023
iDEW is offering an accelerated path to the Google Data Analytics Certification and IU dual-credit during the summer months of 2023. Here are some quick facts.
Limited to 10 dedicated students from iDEW partner schools.
Covers the same material as outlined in our year-long program, but it will be completed outside of high school at very quick rate.
Earn a Google Data Analytics Professional Certificate: "Prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. There are 380,000 U.S. job openings in data analytics with a $74,000 median entry-level salary." — Google on Coursera
https://www.coursera.org/professional-certificates/google-data-analytics
Earn Credit at the IU Luddy School of Informatics, Computing, and Engineering in Indianapolis (Luddy): By successfully completing this program students will earn 12 credits in the following four courses at Luddy. https://luddy.iupui.edu/
* Individual courses may change before the beginning of the Summer semester.
Students will need a computer and internet access. This is a remote course.
Students will be able to complete work on their own schedule.
Students will have coaching support available online for help and pacing guidelines.
Students should expect to dedicate about 30 hours a week to the program over two months or about 20 hours a week over 3 months.
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.
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
You may choose to follow one of these case study packets for your project.
This file can be used as a starting point for a data notebook that also provides an example of a problem statement etc.
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.
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.
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.
What to include in a case studyDuring 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.
from the is offering several opportunities for high school students at partner schools to earn a Google Data Analytics Professional Certificate while they earn IU credit – up to a $4,000 value for FREE! 💰
This program is made possible by the with JPMorgan Chase Summer Youth Employment Program providing the support for the accelerated summer cohort program.
iDEW Data Analytics Cohort 1 This cohort of 19 students will complete the program in the Fall and Spring semesters of the 2022-23 academic year.
iDEW Data Analytics Cohort 2 This cohort of students from partner schools will participate in the program in the Fall and Spring semesters of the 2023-24 academic year.
iDEW Data Analytics Accelerated Summer 2023 Program This program is a fast-paced pathway to certification and dual-credits.
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: 👉 or Also, use the Markdown text blocks to document your process, as you will want to include this in your presentation and documentation.
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.
Example Case Study Questions Etc.:
