|Course Title||Data Science – Level 3
|Prerequisites||Data Science – Level 2 course|
|Target Audience||Data Analysts; Computer Analysts; Professionals dealing with small or large amounts of data needing to apply Machine Learning Methods.|
|Dates||November 14, 19, 21, 26, 28; December 3, 5|
|Instructor||Diego Perea – Ph.D.|
|Schedule||Monday and Wednesday 6:30 p.m. – 9:30 p.m.|
|Gouvernement du Québec fee||$42.00|
|General public fee||$394.42|
Recommended textbook: An Introduction to Statistical Learning with Aapplications in R by G. James, D. Whitten, R. Tibshirani and T. Hastie.
NB: Certificate provided for all participants who have completed 80% of course hours
|Please note that this is a non-credit course.|
|This course deals with advanced methods in Machine Learning. The focus is in understanding the methods and applying them in practical data sets. At the end of the course the participant will have at their disposal a large set of methods to apply.
The course methodology is based on lectures led by the instructor, who will present the concepts using examples, followed by a lab using real data where the participants will complete specific tasks in R designed to reinforce the concepts introduced in the lecture. Students will complete a small data prediction project with data of their choice, where they will apply the methods explained in the course.
|Topics Covered in this Course|
Please note that the instructor reserves the right to modify this schedule
|Week 1||Topics 1 and 2|
|Week 2||Topics 3|
|Week 3||Topics 4 and 5|
|Week 4||Topic 6|
SOFTWARE TO BE USED
For the course, we will mainly use R, which is the industry standard for statistical learning and provides functions for most of the methods. We will compliment R processing capabilities with Tableau visual reporting capabilities. Tableau is a popular and friendly BI and reporting tool widely used in the industry. Other software will be addressed in the course to give the participant a holistic view of statistical learning.
LABS and DATASETS
In the labs, the participant will apply the prediction and classification methods seen in class using practical datasets. We will use datasets from the textbook and from public sources such:
The project presented by the students in the previous term are showcased here: