R Programming Language – Level 3

Return to schedule

register-button24

 

Course Title R Programming Language – Level 3
Course number 900-088-EQ
Platform R
Duration 21 hours
Gouvernement du Québec fee (taxes incl.) $42.00
General public fee (taxes incl.) $344.79
Schedule TBA
Dates Winter 2019
Prerequisites A clear understanding of Math and Linear Algebra. Basic on algorithm design. Familiarity with R concept in levels 1 and 2 courses.
Target audience Software developers, data analysts, consultants on informatics and statistics, professionals in any area
Instructor Diego Perea PhD
Location TBA
NB: This is a non-credit course.  Certificate provided for all participants who complete 80% of course hours.
   
Course description
Please note that this is a non-credit course.
 In this course, we use R to solve data analytics problems in real world applications. We use R data analysis and machine learning methods learned in the previous courses, placing the emphasis on the regression and classification methods for data prediction, and the Natural Language Processing (NLP) algorithms for text processing.

At the end of the course, participants will present a small project using data of their choice and will use R descriptive and predictive data analytics, and NLP methods.

Some of the fields where the methods can be applied include: telecommunications, IT, health sciences, financial industries, logistics, education, social media, etc.

   
Topics to be covered include:
  1. Advanced data analysis methods in R
  2. Advance regression and classifications methods in R
  3. NLP methods in R
  4. Applications to the telecommunications and logistics industries
  5. Applications to the health sciences and financial industries
  6. Students project presentation
   
Weekly Topics
Please note that the instructor reserves the right to modify this schedule.
Week 1 Topic 1 and 2
Week 2 Topic 3
Week 3 Topic 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.

LABS and DATASETS
In addition to the prepared datasets for the labs, participants will present a small project using data of their choice at the end of the course and will use R descriptive & predictive data analytic and NPL algorithms.

The following are some website with good data sources.
www.kaggle.com
www.open.canada.ca/data/en/dataset

TOP