Data Science Experience
Completion of the DS Experience counts as a replacement for one lab from Lab 01 to Lab 04. Lab 05 need to be completed with your project team.
The world of data science is vast and continually growing! The goal of the data science experience assignments is to help you engage with the data science communities outside of the classroom.
There will be one optional data science experience assignment that will replace up to one lab grade (among labs 01-04). The submission for the statistics experience is due on Friday, December 06 at 11:59pm AZ time on GitHub (you will have an assigned repo). No late work will be accepted for the data science experience. You may submit the data science experience assignment anytime between now and the deadline.
Each experience has two parts:
1️⃣ Have a data science experience
2️⃣ Make a slide summarizing on your experience
You must complete both parts to receive credit.
Part 1: Experience data science outside of the classroom
Complete an activity in one of the categories below. Under each category are suggested activities. You do not have to do one these suggested activities. You are welcome to find other activities as long as they are related to data science and they fit in one of the six categories. You can email Professor Chism if there is an activity you’d like to do but you’re not sure if it qualifies for the data science experience.
Category 1: Attend a talk, conference, or workshop
Attend an talk, panel, or conference related to data science. If you are attending a single talk or panel, it must be at least 30 minutes to count towards the statistics experience. The event can be in-person or online.
Category 2: Talk with a data scientist
Talk with someone who uses data science in their daily work. This could include a professor, professional in industry, graduate student, etc.
Category 3: Listen to a podcast / watch video
Listen to a podcast or watch a video about statistics and data science. The podcast or video must be at least 30 minutes to count towards the data science experience. A few suggestions are below:
This list is not exhaustive. You may listen to other podcasts or watch other data science videos not included on this list. Ask your professor if you are unsure whether a particular podcast or video will count towards the data science experience.
- For reference, here are some other podcasts.
Category 4: Participate in a data science competition or challenge
Participate in a statistics or data science competition. You can participate individually or with a team. Information for an upcoming data challenge is linked below.
Category 5: Read a book on statistics/data science
There are a lot of books about statistics, data science, and related topics. A few suggestions are below. If you decide to read a book that isn’t on this list, ask your professor to make sure it counts toward the experience. Many of these books are available through University of Arizona library.
The Signal and the Noise: Why so many predictions fail - but some don’t by Nate Silver
Weapons of Math Destruction by Cathy O’Neil
How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo
The Art of Statistics: How to learn from data by David Spiegelhalter
Part 2: Summarize your experience
Make one slide summarizing your experience. Submit the slide as a PDF on GitHub.
Include the following on your slide:
Name and brief description of the event/podcast/competition/etc.
Something you found new, interesting, or unexpected
How the event/podcast/competition/etc. connects to something we’ve done in class.
Citation or link to web page for event/competition/etc.
Click here to see a template to help you get started on your slide. Your slide does not have to follow this exact format; it just needs to include the information mentioned above and be easily readable (i.e. use a reasonable font size!). Creativity is encouraged!
Submission
Submit the reflection as a PDF under the Data Science Experience assignment on GitHub by Friday, December 06 at 11:59pm. It must be submitted by the deadline on GitHub to be considered for grading.