Data Science – Level 1

Return to schedule

register-button24

NEW!

Course Title Data Science – Level 1
Course number 900-080-EQ
Platform Windows desktop
Duration 24 hours
Prerequisites Python I, Python 2 (optional), programming experience
Target Audience All those interested in an introduction to the world of data science. Students must have programming background. See prerequisites.
Dates September 5, 12, 19, 26; October 3, 10, 17
Instructor José Rafael Porras
Room BH-210
Schedule Wednesday: 6 p.m. – 9:30 p.m.; class of October 3: 6 p.m. – 9 p.m.
Gouvernement du Québec fee $48.00
General public fee $394. 42

Recommended textbook: Class Notes

NB: Certificate provided for all participants who have completed 80% of course hours

Course Description
Please note that this is a non-credit course.
This course is the first in a series of three courses on the subject of data science. The course will introduce the participant to the concepts of data extraction, transformation and loading using the Python Pandas data science library as well as the R programming environment and SQL.

The course will also explore concepts of data gathering, APIS, JSON and basic web scraping. Beyond ETL concepts, the course will introduce the basics of data visualization and statistical concepts as applied to both R and Python. The course will end with an introduction to regression and other data exploration techniques.

Prerequisites: Python I
This is a programming intensive course. The student must be comfortable working with text files (opening, reading and writing text files using a programming language). Knowledge of conditionals, loops, functions, indexing, slicing, lists, dictionaries and arrays is essential.

 

Topics Covered in this Course
  1. Introduction to ETL processes and data wrangling
  2. Using Pandas and NumPy modules in Python to explore data
  3. Introduction to the R programming environment
  4. Working with Databases and SQL
  5. Introduction to APIS and web scraping
  6. Intro to statistical and probability concepts
  7. Overview of linear regression and other techniques

 

TOP