Course Details
01:198:310 Data Science Capstone Project
- Course Number: 01:198:310
- Instructor: STAFF
- Course Type: Undergraduate
- Semester(s) Offered: Fall, Winter, Spring, Summer
- Credits: 1
- Description:
This is a 1-credit capstone course specifically for students pursuing the Data Science Certificate Program, or data science minor. Students must already have completed the three core prerequisite courses. The course is meant to test aspects of data science explored in the core and possibly in domain course, from identifying a data source, cleaning and organizing the data, conducting appropriate statistical analysis, to interpreting and reporting the results of the study in a standard scholarly form.
Students will be asked to clean a provided default data set. They will later be encouraged to find/propose a data set of their own choosing (subject to approval by the instructor) to further analyze in depth.
Students will have to demonstrate skills learned in DS certificate classes such as data wrangling, statistical data analysis including application of machine learning methods and database management skills, plotting and visualization as well as telling the story with the data.
- Prerequisite Information:
Prerequisites: 01:198:142/01:960:142 (Data 101/Data Literacy), 01:960:291 (Statistical Inference for Data Science) and one of the following: 01:198:210 (Data Management for Data Science), 01:960:295 (Data management and wrangling with R), or 04:547:221 (SCI Data management course).
- Expected Work: The class will meet once a week for 55 minutes during the first 4-6 weeks of the semester (this is the new accelerated course format, intending to complete everything before midterms for other classes roll around). The first three classes will be for discussing the three assignments. Final presentations will be held in the latter (up to) three weeks of the class. There will be no exams.
- Learning Goals:
The learning objectives of the course correspond to the broadly stated learning goals for the Certificate/Minor in Data Science. The completed project should exhibit an overall synthesis of these learning goals for a particular research topic:
Students will be able to:
1. Visualize (plot) data relationships in meaningful ways.
2. Transform and map data (otherwise called data wrangling) from one "raw" data form into another format for further analysis, querying, learning, and prediction.
3. Acquire data management skills such as database design and database querying.
4. Execute data analyses with professional statistical and machine learning software.
5. Understand the conceptual basis of analyses used in data science.
6. Apply data science concepts and methods to solve real-world problems.
7. Back up a story with data as well as critique conclusions which are not justified by the data.