Check 2 Ideas

For Check 2, all of the prompts from check 1 apply, plus the following additional prompts, since there are new skills.

If you have other ideas, you can also ask and those are likely posible.

Level 1 Achievement Catchup

To make up level 1 achievements, include a detailed introduction file to your portfolio and one of the following (per skill):

• minor extensions to what we did in class

• answers to problems from the notes

• additional glossary terms

• psuedocode for one of the other prompts

Extend Assignment 7, 8, or 9

Assignments 7-9 help you think through what machine learning tasks are. Extend those ideas by adding additional experiments based on your own questions or the questions in your feedback.

Tip

If you are short on time these achievements are easiest to combine with the optimize, compare, representation and workflow in the next portfolio

Build a data set for Prediction

Build a dataset that works for prediction (classification, regression, or clustering) from other sources.

Learn a new model

Repeat what you did in 7, 8, or 9, with a different model.

Create datasets that fail

Create datasets that violate assumptions of a model we have learned. The sklearn data generators are a good place to start.

Process level 3

Process level 3 is a little different than most of the others. You may be able to work it into an analysis notebook, but likely, you’ll need to do one of the following.

Data Science Pipeline Comparisons

Find two different sources that describe the data science pipeline or lifecycle. Write a blog style post that discusses their differences and hypothesizes about why they may be different? Are they for different audiences? Is one domain specific? How do they emphasize different modeling tasks? Include a Recommendation for when you think each one is better

Write a short story

Write a short story that explains the concepts of data science to demonstrate your understanding of process.

Media Review

Watch/listen/read to an episode of a high quality1 podcast or other type of media and write a blog style summary and review. Highlight what you learned and how it relates to topics covered in class.

Approved Media:

• Pod of Asclepius, Fall Series: The Philosophy of Data Science

• Chapter 1 & 2 of Think like a Data Scientist in particular, if you think these would be helpful to assign as reading or teach from at the beginning of the semester next year.

• Algorithms of Oppression (book)

• Weapons of Math Destruction (book)

• Coded Bias (film, available on netflix & PBS)

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approved Dr. Brown by creating a pull request to add it to the list on this page that is successfully merged. To create a PR, use the suggest an edit button at the top of this page.