Welcome to Data Science IFT6758 Graduate level course on introduction to data science. The course focuses on the analysis of messy, real life data to perform predictions using statistical and machine learning methods.
The material of the course will integrate the five key facets of an investigation using data:
In this course, we focus on statistical methods and introduce techniques in different domains to make you familiar with various type of data. Our goal is to educate you to become not only knowledgable but also responsible data scientist by the end of this course!
Please use this form to provide feedback about the course.
Tuesday, 11:30AM-12:30PM, Z310 Pavillon Claire-McNicoll, Université de Montréal
Thursday, 4:30PM-6:30PM, G-415-511A Pavillon Roger-Gaudry, Université de Montréal
Basic knowledge of statistics, and Python programming is encouraged.
Your final score for the course will be computed using the following weights:
ATTENTION regarding fraud and plagiarism: The University of Montreal now has a strict policy in case of fraud or plagiarism. If an infraction is found, the professor is required to report to the director of the department. An administrative procedure is then automatically triggered with the following consequences: the offense is noted in your file, and a sanction is decided (which can be serious and go to dismissal in case of recidivism). It is important that you do the work yourself!
Jake VanderPlas, Python Data Science Handbook, O’Reilly Media; 1 edition (2016) - Free book
Russell Jurney, Agile Data Science 2.0: Building Full-Stack Data Analytics Applications with Spark, O’Reilly Media; 1st edition (2017).
Foster Provost and Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O’Reilly Media; 1st edition (2013)
A. Rajaraman, J. Leskovec and J. Ullman, Mining of Massive Datasets, Cambridge University Press, 3rd version.
James, Witten, Hastie, Tibshirani. An Introduction to Statistical Learning