Week 15 — What Next? (12/5–9)#

This is the last week of class. We’re going to recap, and talk about what’s next, both for learning and for putting what you’ve learned to practical use.


  • Dec. 5: clarified that there is no week 15 quiz — there was previously a disagreement between the heading and the text.

🧐 Content Overview#

Element Length

🎥 Recap


🎥 Drift


🎥 Time Series Operations


🎥 Correlated Errors


🎥 Publishing Projects


🎥 Production Applications


🎥 Topics to Learn


🎥 General Tips


This week has 1h13m of video and 0 words of assigned readings. This week’s videos are available in a Panopto folder.

📅 Deadlines#

🎥 Recap#

This video reviews the concepts we have discussed this term and puts them into the broader context of data science.

🚩 Makeup Exam#

The makeup midterm due December 10 and operates on the same schedule as the other midterms. It covers the entire semester, with an emphasis on material since the 2nd midterm. The rules and format are the same as for Midterm A.

I encourage you to come and take the exam, as it is the single best way to prepare for the final. You can at any time leave the exam, and take it with you; I will only grade it if you turn it in, and there is no problem with showing up, looking at it, even filling it out, and deciding you like your current grades well enough to not turn it in.


If you turn in the makeup exam to be graded, its grade will replace the lower of your Midterm A and B grades, even if that lowers your final grade. Only turn it in if you think you did better than your worst normal midterm!

🎥 Data and Concept Drift#

This video introduces a fundamental assumption of predictive modeling and the way drift can affect it.


🎥 Time Series Operations#

Time is an important kind of data that we haven’t spent much time with — this video discusses the fundamental Pandas operations for working with time-series data.


📓 Time Series Example#

The MovieLens Time Series notebook demonstrates basic time series operations in Pandas.

🎥 Correlated Errors#

Regression models require that the data be independent. This video introduces two kinds of non-independence and methods for addressing them: grouped observations addressed with a mixed-effects model and temporal auto-correlation addressed with ARIMA models.


🎥 Publishing Projects#

This video talks about going from an analysis and its notebooks to a publishable paper.


  • PlotNine is a good plotting library for preparing consistent, publication-ready graphics.

  • The book gender example also demonstrates the current evolution of my own practices for preparing for publication.

🎥 Production Applications#

How do you put the results of your data science project into product?

🎥 Topics to Learn#

This video goes over some useful topics to learn to fill out more of your data science education.

🎥 General Tips#

Some final closing tips and suggestions for you to think about as you take the next steps in your data science career.

🚩 No Quiz 15#

There is no quiz 15 — makeup midterm instead.


We have had a few weeks without quizzes (3, by my count). When setting the final grade, I am going to ensure that you are held harmless for weeks without quizzes (basically, give you the quiz grade you would have received if we had a full 15 quizzes, and you had a perfect score on the skipped quizzes).

📃 Farewell#

It’s been grand!

I would love to hear feedback on how to further improve the course; I have tried to make some corrections as a result of the midterm assessment process, and will be keeping those notes for next year, but further suggestions either in the course evaluations or by Piazza are welcome.

I hope to see many of you in future courses!

📩 Assignment 7#

Assignment 7 is due Sunday, December 11, 2022.

🚩 Final Exam#

The final exam will be on Canvas. It will be available for 72 hours beginning on Monday, December 12, 2022.


The exam follows the same format as the midterms.

Study Tips#

  • Review the previous quizzes, assignments, and midterms.

  • Read through the makeup midterm, even if you do not plan to turn it in.

  • Review lecture slides to see where you are unclear on concepts and need to review.

  • Skim assigned readings, particularly the section headings to remind yourself what was in them.

  • Review the course glossary, keeping in mind that it does contain terms we haven’t gotten to yet.