Portfolio Dates and Key Facts

This section of the site has a set of portfolio prompts and this page has instructions for portfolio submissions.

Starting in week 3 it is recommended that you spend some time each week working on items for your portfolio, that way when it’s time to submit you only have a little bit to add before submission. The portfolio is your only chance to earn Level 3 achievements, however, you can also earn level 1 or 2. The prompts provide a starting point, but remember that to earn achievements, you’ll be evaluated by the rubric. You can see the full rubric for all portfolios in the syllabus. Your portfolio is also an opportunity to be creative, explore things, and answer your own questions that we haven’t answered in class to dig deeper on the topics we’re covering. Use the feedback you get on assignments to inspire your portfolio.

Important

Each submission should include an introduction and a number of ‘chapters’. The grade will be based on both that you demonstrate skills through your chapters that are inspired by the prompts and that your summary demonstrates that you know you learned the skills. See the formatting tips for advice on how to structure files.

In each chapter(for a file) of your portfolio, you should identify which skills by their keyword, you are applying.

You can view a (fake) example in this repository as a pdf or as a rendered website

Current: Check 3

The third submission will be graded on the following criteria* and due on December 20 :

	Level 3
keyword
python	reliable, efficient, pythonic code that consistently adheres to pep8
process	Compare different ways that data science can facilitate decision making
access	access data from both common and uncommon formats and identify best practices for formats in different contexts
construct	merge data that is not automatically aligned
summarize	Compute and interpret various summary statistics of subsets of data
visualize	generate complex plots with pandas and plotting libraries and customize with matplotlib or additional parameters
prepare	apply data reshaping, cleaning, and filtering manipulations reliably and correctly by assessing data as received
classification	fit and apply classification models and select appropriate classification models for different contexts
regression	fit and explain regrularized or nonlinear regression
clustering	apply multiple clustering techniques, and interpret results
evaluate	Evaluate a model with multiple metrics and cross validation
optimize	Select optimal parameters based of mutiple quanttiateve criteria and automate parameter tuning
compare	Evaluate tradeoffs between different model comparison types
representation	apply transformations in different contexts OR compare and contrast multiple representations a single type of data in terms of model performance
workflow	Independenltly scope and solve realistic data science problems OR independently learn releated tools and describe strengths and weakensses of common tools

• it can also be graded against the level 1 or 2 criteria from the syllabus/Data Science Achievements

Submision Checklist:

• [ ] update your gh action or precommit hook

• [ ] complete your KWL chart

• [ ] add notebooks or markdown files for your work