Project Milestones

The project milestones are opportunities to present your project’s progress and get feedback from The Data Mine teams. There are 4 milestones that will progress with the project throughout the academic year.

The information for the different milestones is included below.

Due Dates

The TA for each team should be responsible for scheduling the milestone reviews using the link below.

Due to the number of reviews, the timeframe for the review will be a window of time. Each team must make sure to complete the review before the end of the time window.

If you have questions, email datamine-help@purdue.edu.

Schedule: work with your assigned contact from the Corporate Partners Team to schedule the meeting.

Milestone 1: Review must be completed before the end of September.

Milestone 2: Review must be completed before the final presentation.

Milestone 3: Review must be completed by the end of February.

Milestone 4: Review must be completed before the symposium.

Format

  • Presentation Length: 20-30 minutes.

  • Presentation Format: Purdue PowerPoint Template

  • Presentation Style: While business attire is not required, these are formal presentations and should be complete and ready for review and questions.

Content

Milestone 1: This should be an overview of all the planning and research at the start of your project.

Milestone 2: This will cover the same content as your final presentation and will be an opportunity to practice the presentation before meeting with your mentors.

Milestone 3: This presentation is focused on how the model or application is progressing.

Milestone 4: This will cover the same content as the symposium and poster presentation and will be an opportunity to practice before the event.

Core Content: The questions below should be answered for every milestone. Feel free to add additional information that you feel is relevant to the project.

  1. What are your research goals?

  2. What findings or progress do you have that is related to your goals?

  3. What technologies or applications were used?

  4. Why did you make the decision to use those tools?

  5. What are the next steps for the team?

  6. What challenges are you facing and how are you addressing them?

  7. How is the project being documented?

  8. Do you have questions for the data science team?