Unit 1, Project 1
We've provided a Jupyter notebook Project-1-CC.ipynb that contains the kinds of coding challenges that often come up in data science job interviews. In addition to preparing you for interviews, completing challenges like these is a fun way to develop your Python skills.
Some of these problems are well known, so it may be possible to find complete solutions online. Students should see these questions as an opportunity to challenge themselves; looking up answers limits the potential growth that comes from practice and repetition of these skills.
Create working solutions for all of the questions above.
Your notebook should include:
- Text for each question.
- A working solution to each problem. Do not include test, practice, or broken code unless you were unable to create a working solution.
- Comments for all of your code that explain your steps and describe any assumptions you made in order to solve these problems.
- Bonus: In programming (and life), there may be multiple ways to solve a problem. After completing our challenges, go back and see if you can think of any other valid methods!
For all projects, requirements will be evaluated on a simple point scale of 0, 1, or 2. Additionally, instructors will provide you with feedback on required portions of your project.
| Score | Expectations |
|---|---|
| 0 | Incomplete. |
| 1 | Does not meet expectations. |
| 2 | Meets expectations, good job! |
| 3 | Surpasses our wildest expectations! |
Note: Scores of
2mean that a requirement has been completely fulfilled, while3is typically reserved for bonus objectives.
Create a repository called unit_project1 in your github profile, push your materials to it, and then post the link using this Google Form: TK