<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=409242&amp;fmt=gif">

Statistical Learning Using R

 

 

 Dates 

June 2021

 

 About this course 

 

This course aims to make participants proficient in the fundamentals of statistics learning, which brings together the fields of machine learning and statistics. Participants will learn how to use an array of computer-based techniques using the programming language R to make statistical predictions and inference on large data sets.

Iconos_new-09-1
This course is designed for professionals and students in technical fields who want to learn how to use R to make predictions and statistical inference from data. These participants can identify with profiles 2 and 3 (university and technical) of the 2-3-1-20-5 convocation.
Iconos_new-07-1
Participants need to be computer literate and have a laptop. A basic knowledge of statistics is necessary, at least the successful completion of an introductory undergraduate course. Prior knowledge of R for computer programming is not necessary but it is helpful. Participants need to understand and speak English, and must have a grade of at least A2 in the free test https://www.examenglish.com/leveltest/index.php
Iconos_new-08-1
The course will be taught online through Zoom for Education or MS Teams. It will consist of 10 meetings of 3 hours each. Classes will be held on weekday evenings in June 2021, from 5 PM to 8PM, for two consecutive weeks.

 

 Method of instruction 

Classes will be a mix of presentations to convey concepts, working through examples, and group exercises. Students will be randomly assembled each week into groups of 2 or 3, and must complete an in-class assignment based on the material covered. In addition, participants must take an individual quiz at the start of each class about the materials covered the previous day. All instruction and materials will be in English. The teacher for this course is Dr. Tim Matis.

 Evaluation 


Attendance is mandatory. Any student who does not attend at least 90% of the meetings will automatically fail the course. The participant’s grade will be based on in-class group work (60%) and individual quizzes (40%).

 Course Materials 

 

The software used in this class are free for personal use.

12

Class Schedule & Syllabus

DOWNLOAD SYLLABUS

DR. TIMOTHY MATIS

Associate Professor of Industrial, Manufacturing and Systems Engineering

Dr. Timothy Matis has a Ph.D in Philosophy in Industrial Engineering, and master's and bachelor's degree of Science in Industrial Engineering. He is currently a professor at Texas Tech University and provides periodic research and teaching assistance to the School of Engineering at the Pontífica Universidad Católica de Valparaiso as a visiting professor. 

For more information

 
 
Contact us here