DSGA 1002: Probability and Statistics for Data ScienceInstructor: Carlos FernandezGranda (cfgranda@cims.nyu.edu) Teaching assistants:
This course introduces fundamental concepts in probability and statistics from a datascience perspective. The aim is to become familiarized with probabilistic models and statistical methods that are widely used in data analysis. Announcements
Syllabus
See the schedule for more details PrerequisitesCalculus and linear algebra at the undergraduate level. NotesThe course will follow these notes: Probability and Statistics for Data Science Please read the corresponding chapter before every lecture. The notes will be changed during the course, so we recommend that you don't print them out. Let us know if you find any typos or have any comments about them. General InformationLecture
Recitation
Office hours
Grading policyHomework (40%) + Midterm (20%) + Final (40%) HomeworkHomework will be posted each Wednesday and is due a week later on Thursday at 11 pm. We will not take into account the assignment with the worst grade. The homework assignments should be submitted as a pdf through NYU classes. The solutions and the grades will be available also on NYU classes. Feel free to collaborate and discuss in person or on Piazza, but do not share specific answers and make sure that you write your assignment yourself. Always explain your thought process. If you use results from the notes or a book reference them adequately. PiazzaWe will be using Piazza to answer questions and post announcements about the course. Please sign up here. BooksAdditional references that could be useful include:
