Discussion Overview
The discussion centers around which undergraduate computer science courses would be beneficial for students planning to pursue a master's degree in statistics, particularly in the context of data science. Participants explore various course options and their relevance to future careers in data science, data analysis, and statistics.
Discussion Character
- Exploratory
- Technical explanation
- Debate/contested
- Homework-related
Main Points Raised
- Some participants suggest taking a course in SQL, emphasizing the importance of writing efficient queries for data stored in databases.
- Others recommend a course in Algorithms to understand dynamic programming and problem tractability.
- Machine Learning is frequently mentioned as a key course, with some questioning its availability at the undergraduate level.
- Visualization and Theory of Computing are proposed as beneficial courses for developing abstract thinking and proof construction skills.
- Bioinformatics is mentioned as a potential option if the participant has the necessary biology prerequisites, due to its heavy reliance on statistics.
- General programming classes are suggested as a follow-on to introductory programming courses, with a focus on becoming familiar with applied statistics tools like R, SAS, and MATLAB.
- There is a discussion about the relevance of SAS, with some participants questioning its obsolescence while others defend its continued utility in the field.
Areas of Agreement / Disagreement
Participants express a variety of opinions on the most useful computer science courses for statistics, with no clear consensus on specific recommendations. There is also disagreement regarding the status of SAS and the availability of Machine Learning courses at the undergraduate level.
Contextual Notes
Some participants inquire about the prerequisites for courses and the participant's programming experience, indicating that recommendations may depend on individual readiness and course offerings.