| Date | Assignment Due |
|---|---|
| January 21 | Data Wrangling |
| January 29 | Git/GitHub |
| February 5 | Missing Data |
| February 26 | Independent Analysis 1 |
| March 5 | Random Forest |
| March 19 | Matching |
| April 2 | Independent Analysis 2 |
You can find the detailed descriptions for all assignments below or in the assignments folder.
| Assignment | Grade % |
|---|---|
| Data Wrangling and Visualization | 10% |
| GitHub | 10% |
| Missing Data | 15% |
| Independent Analysis – Part 1. | 15% |
| Random Forest. | 15% |
| Matching. | 15% |
| Independent Analysis – Part 2. | 20% |
| Total | 100% |
Value: 10% of final grade
Description: In this assignment, you will complete a data wrangling assignment that will involve data cleaning, descriptive statistics, understanding missing data, and joining datasets together.
Value: 10% of final grade
Description: In this assignment, you will create a GitHub account, install Git on your local computer, create a GitHub repository, and commit and push your work to that GitHub repository.
Value: 15% of final grade
Description: In this assignment, you will apply and compare different methods for imputing missing data on a large health administrative dataset.
Value: 15% of final grade
Description: This is part 1 of the independent analysis. You will need to find a dataset, develop an analysis plan that includes the major components of the course (i.e., GitHub, Scientific Computing), and conduct descriptive statistics and data wrangling on your chosen dataset.
Value: 15% of final grade
Description: In this analysis, you will complete a Random Forest analysis using the Can Path student dataset. You will need to run the analysis, conduct detailed hyperparameter tuning, and conduct model comparisons.
Value: 0% of final grade
Description: In this assignment, you will use the USask Plato High Performance Computing to run large-scale machine learning on a large (~1GB) dataset.
Value: 15% of final grade
Description: In this analysis, you will complete a machine-learning-based matching analysis using the Can Path student dataset.
Value: 20% of final grade
Description: This is part 2 (final part) of the independent analysis. You will need to conduct a complete analysis including data wrangling, missing data handling, and applying at least 2 different machine learning methods to your data.
Type: Written report
Description: Complete the student self-evaluation form.
All assignments should be submitted to the appropriate place in Canvas or GitHub. All assignments are due at 5 PM (CST) on the due date. Please don’t stay up until midnight to get the work done. Remember there are no late penalties, so just take an extra day if you need and get some sleep.
There is no penalty for late assignments. However, because many assignments have two parts, it is critical to submit the first assignment of the sections around the due date. Missing assignments that are not submitted by the end of the course will receive a grade of zero.