Introduces students to foundational programming concepts for data analysis and visualization and fundamental concepts in probability and mathematics (random variables, set theory, simulations, series and integration), serving as preparation for follow-up major gateway courses in PSTAT. The material is organized into distinct modules and includes a brief introduction to R, Python, tidy data framework, data science ethics, and exploratory data analysis. Students will also explore research areas in the discipline, departmental and campus study resources, undergraduate research opportunities, and diverse career tracks available to Statistics & Data Science graduates.
2
UnitsPass no pass
Grading1, 2, 3
PasstimeNew student only
Level LimitLetters and science
College223C with Ludkovski was a great class. It balanced computational methods and the underlying theory extremely well. While the homeworks were very difficult and strictly graded, I learned immensely from his criticisms. They were great practice at testing both my understanding of the theory and my skill with implementing the methods.
The grading criteria was confusing to me personally.
rubric on the final was super punishing and unfair
Worst prof in this department. DO NOT TAKE ANY COURSES FROM HIM!!!!! Hadn't seen such a tough grading criteria in my life.
honestly the worst teacher I've had in my whole life. grades super hard, his answer keys are wrong half the time and he doesn't respond to emails. genuinely do not take this professor. i hate this guy with a passion
The issue is not in the way she teaches or the organization of the course, but rather the structure of tests themselves which were weighted so highly. She tries to trip you up on syntax and tests little things you can only get from memorizing things in lectures. It felt more-so like a memorization test than anything.
V. PALANIAPPAN
2.9
Ian Duncan
3.0