Assignment 1 Fintech Fun

Assignment 1: Fintech Fun

For this assignment, students use Google Sheets to perform an EDA and clean two different datasets. They need to write up their findings in a document called "Final Report".

The assignment asked them to do the following:

1. An EDA and cleaning of the Debt Repayment Plan spreadsheet from Task 2.
2. Select three home pricing factors from the dataset on Kaggle and load their chosen three into a Google Sheet in Task 3.
3. Perform an EDA and clean all three chosen home pricing factors in Task 3.
4. Answer all questions in the report document based on what they discovered during EDA and data cleaning.

Task 2 presents the learners with Mia's Debt Repayment Plan. This spreadsheet is intentionally quite small. We wanted learners to be able to navigate the spreadsheet simply by reading the chapters on EDA with spreadsheets and cleaning data with spreadsheets. This way, they could get started early on the assignment. Therefore, the data in the repayment plan is visibly dirty and simpler to attempt to double-check calculations if they so choose. The data also looks more similar to what learners may have encountered before the class to hopefully increase their comfort level. We wanted to give them the opportunity to first experiment with techniques covered in the textbook such as calculating the mean or visualizing and begin to develop their own analytical thought process. If a student tries a technique and it doesn't work, but they write about the experience in the final report and offer up their thoughts on why it didn't work, that is great! We are just beginning to learn about EDA and cleaning data in the class, so we know there is a lot more to learn and we hope that we are beginning to get students thinking about these topics.

Task 3 asks the learners to select three housing price factors from the Kaggle dataset and experiement once again with EDA and cleaning data. We chose this dataset because it gives learners the opportunity to question outliers. Many of the factors have data points that look like an outlier, but are actually due to a stock market crash or other historical event. We don't want learners to get in the mindset of deleting everything that feels like it shouldn't fit and instead approaching outliers with caution and curiousity.

They are submitting their folder with their two datasets in Google Sheets and their report document. To get started grading, first check to make sure that they submitted all three of those items and that the housing prices data has three factors in three separate tabs in their workbook. If they didn't submit their folder and just submitted their final report, you may give them a chance to re-submit the whole folder. If they removed items or refuse to re-submit, then they do not pass the assignment.

If they have submitted all their work, you should look for the following in the final report:

1. Answering all the questions. No blank questions or answers that are irrelevant!
2. Sharing with you what they tried even if their idea didn't pan out.
   1. If it did pan out, did they share what they learned by looking at the result? For example, if they tried calculating summary statistics and found some dirty data, did they share that in their report?
   2. If it did not work out, did they share what happened and an idea as to why? For example, if they tried a chart style and couldn't get it to work, do they have an idea as to why?
3. Grounding their results in their work. For example, if they think the repayment plan is too dirty and therefore is inaccurate, do they share why they believe that to be the case?

Note for the May 2024 class: Based on the phrasing of some questions, some students may just answer with a yes or no. Since we did not specify for those questions that they should go beyond a simple yes or no, you may need to be more lenient.

If you see these items in their final report, then go ahead and give them some feedback and a passing grade!

Feedback and Grades

If a student's work passes all the requirements listed above, assign them a score of 1/1 in Canvas.

As with all graded assignments, we encourage you to provide feedback about your students' analytical approach. The instructions for this assignment are intentionally nebulous. There is little guidance on what Google Sheets actions they have to do to meet the requirements and therefore students' analytical approach may benefit from some advice.

If a student's work does not meet one or more of the requirements, provide them with detailed feedback about which parts need to be improved.