Grading::The grading information is given in the table below.
Grading component
Total weight
Quiz
20% (Best n-1 out of n)
Minor exam
20%
Major exam
30%
Lab assignments/projects
28%
Attendance/class participation
2% (Best n-2 out of n)
Contents:
Exploratory Data Analysis, Data Visualization, Dataframes and SQL,
Hypothesis Testing, Linear Modeling, Classification, Principal Component Analysis,
Clustering.
Books: (We will use a collection of sources most of which are available on the internet)
- [LDS] Learning Data Science, by Sam Lau, Joey Gonzalez, and Deb Nolan.
- [CIT] Computational and Inferential Thinking: The Foundations of Data Science by Ani Adhikari, John DeNero, David Wagner.
- [ML] Machine Learning by Tom Mitchell.
- [ISL] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani.
Note: All online discussions will take place on Piazza, and all
quizzes will be graded on Gradescope. The top tab on this page includes
the links for Piazza and Gradescope for this course.