Description:
The Foundations in Data Science is a non-credit course that provides a comprehensive introduction to key concepts, statistical techniques, and tools foundational to the field of data science.
Prerequisites:
Students should have a basic technical background. An R and Python programming refresher will be provided in the course.
Course Learning Outcomes:
Upon successful completion of this course, students will be able to:
- Articulate the significance of data in various domains.
- Perform exploratory data analysis.
- Make informed decisions based on statistical inference.
- Critically analyze ethical dilemmas arising in data-driven contexts.
- Identify agile methodologies to manage and execute data science projects.
Course Overview:
This is an instructor lead course. The course is eight weeks in length and runs Monday through Sunday. All course information, assessment details, due dates, grading policy and course expectations will be communicated through the course syllabus in the learning management system Brightspace. Learners are encouraged to access the course content regularly, engage in the course, and allow time to complete the course components.
The course begins with a programming refresher. You will learn both R and Python. For the course project, you may choose to use R or Python. You will choose a dataset to use for the project.
In addition to the course project, the course involves several assignments. Plenty of practice is built in with practice worksheets and practice quizzes. The quizzes are required for subsequent material to unlock. The Week 6 Lab Assignment may be worked on with a partner and serves as an opportunity for you to practice several skills necessary for your course project.
The course is video based. The videos often reference a textbook, which you may purchase (optional). There are also several required readings provided in Brightspace.
Textbooks and/or Course Materials:
Required Textbooks: None
Reference Textbook: Montgomery, D. C. (2020). Introduction to statistical quality control. John Wiley & Sons, Inc.
Additional Readings: Additional readings can be found in Brightspace.
Software/Web Resources: R and Python
Learners who earn a B or better may qualify for the earned admissions pathway for the Master of Science in Data Science. Information about this will be sent to registered learners one week before the course begings.