Teaching Assistant for GSB MBA Class (OIT 367)
We are looking for a few teaching assistants for an exciting MBA data science class that will be offered during the winter quarter of 2023 at GSB (the first lecture is on Thursday, Jan 12 2023).
- Familiarity with basic machine learning and statistics concepts
- Working knowledge of Python and Jupyter notebook environment (knowing some SQL would be a plus)
- Very good command of English
- Very good communication skills
- Attending (in-person) lectures to assist the professor in answering students' possible Python/SQL questions and evaluating their work (student work on data science exercises during class time). The class has two sections and both meet on M/Th, one section is 8:15-9:35 am and the other one is 10:00-11:20 am (the first lecture is on Thursday, Jan 12)
- Each CA will need to attend only one section (i.e., 2 x 80 minutes of class time per week).
- This is the bulk of CA work in this course.
- Holding office hours (about 1 hour per week for each CA)
- Running review sessions (each CA will do this 1 time during the quarter, and the session is <=1 hour)
- Each CA could expect at least 5-7 hours per week time commitment on average.
- Grading the midterm, Kaggle competition, and the project
- We will have a dedicated grader that does the bulk of the grading
- All grading is done virtually
Compensation: GSB only pays salary (at a rate of $50.23/hour) but does NOT provide tuition support.
If you are interested, please send a CV to email@example.com
About the course:
OIT 367: Business Intelligence from Big Data
The objective of this course is to analyze real-world situations where significant competitive advantage can be obtained through large-scale data analysis, with special attention to what can be done with the data and where the potential pitfalls lie. Students will be challenged to develop business-relevant questions and then solve for them by manipulating large data sets. Problems from advertising, eCommerce, finance, healthcare, marketing, and revenue management are presented. Students learn to apply software (such as Python and SQL) to data sets to create knowledge that will inform decisions. The course covers fundamentals of statistical modeling, machine learning, and data-driven decision making. Students are expected to layer these topics over an existing facility with mathematical notation, algebra, calculus, probability, and basic statistics.