|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.|
|Schedule||Tuesday & Thursday 6 :30 pm – 9 :30 pm|
|Dates||May 21, 23, 28,30, June 4, 6,11|
|Instructor||Diego Perea – Ph.D.|
|Room||Brittain – BH- 214|
|Gouvernement du Québec fee||$42.00|
|General public fee||$304.71|
Recommended textbook: An Introduction to Statistical Learning with Applications 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 of Machine Learning for data science. The focus is on understanding the methods and applying them to practical data sets. At the end of the course the participant will have at disposal a large set of methods to apply: regression, supervised and unsupervised classification.
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.
Examples of previous students projects are listed below:
|Topics Covered in this Course|
Please note that the instructor reserves the right to modify this schedule
|Week 1||Topic 1|
|Week 2||Topic 2|
|Week 3||Topic 5|
|Week 4||Topic 4|
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 complement 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: