Professional Development Certificate in Data Science and Machine Learning

Language of delivery: English

The program aims to prepare candidates who are able to work through a complete data science pipeline, whether for the simple purpose of data exploration and extracting knowledge, or to build ML models from the data for specific goals such as prediction or classification. Participants have the opportunity to work in cross-functional teams to translate their learnings into business insights to help guide business decisions.

Whether you are considering embarking on a career in data science or simply want to enhance your professional portfolio with new skills, this program will help you meet your goals.  

Program Toolkit & Languages:

  • Apache Hadoop
  • Apache Spark
  • Tableau 
  • Github 
  • Python 
  • Scala
  • SQL

Learner Outcomes

This program is designed to enable you to:

  • Demonstrate solid understanding of relevant statistical, mathematical concepts and computational tools
  • Apply essential data science tools to ingest, clean, process and analyze various large data sets using batch and streaming modes.
  • Work through all the phases of a complete data science pipeline with structured and unstructured data 
  • Test and evaluate different machine learning techniques, and learn how to select the proper one in order to solve a business problem
  • Formulate a business need or problem into a data science project and select the proper tools and algorithms needed. 
  • Interpret and effectively communicate data insights by using data visualization and storytelling techniques and translate them into business-specific knowledge. 

Who Should Attend

Both established professionals and recent university graduates seeking to acquire essential technical data science and machine learning knowledge and skills or wishing to embark on a career in data science.

Those planning to pursue INFORMS Certified Analytics Professional (CAP®) certification or other similar designations.


Outline

Required courses

CBUS 255

Computational Applied Statistics

30 hours class + at least 10 hours of assignments/readings

4 CEUs

CBUS 256

Data Science for Business Decisions

30 hours in class + at least 10 hours of assignments/readings

4 CEUs

 

CBUS 257

Data at Scale

35 hours in class + at least 25 hours of assignments/readings

6 CEUs

CBUS 258

Practical Machine Learning

35 hours in class + at least 25 hours of assignments/readings

6 CEUs

CBUS 299

Data Science Capstone Project

30 hours in class + at least 30 hours of assignments

6 CEUs

Total  26 CEUs

Pre-requisites

  • Strong quantitative background
  • Proficiency in Excel, Access or other data analysis tools
  • Essential programming skills, preferably in Python

Students may take the following self-assessment online tests to establish their level of proficiency in Python. 
https://www.pluralsight.com/paths/python
https://www.testdome.com/tests/python-online-test/45
https://tests4geeks.com/python
https://www.techgig.com/skilltest/python

Students without prior knowledge of Python should complete the following Python courses online and provide proof of completion:

 

Admission & Registration

The program should be completed within 2 years.

Applicants must hold a minimum of one of the following degrees: 

  • Bachelor of Engineering (B.Eng.)
  • Bachelor of Science (B.Sc.)
  • Bachelor of Commerce in MIS (B.Com MIS)

Note:
Applications from mature students* who do not meet the above criteria but have extensive and relevant experience in software programming or data analytics and have previously completed relevant coursework in calculus, statistics, or computer science will be evaluated on a case by case basis.

*Applicants who are 21 years of age or older 

To be admitted to the program: 

A non refundable application fee must be paid online by credit card (Visa or MasterCard).

The application fee for admission to Winter 2018 and onwards is $84.14.

Once your application has been submitted online, you will receive an email message confirming receipt.
If you wish to register for an individual course without being admitted to the program, please click here for registration instructions.

If you miss the admissions deadline, you can still register for courses offered as an independent (special) student and then apply for admission for the next semester.


Contact Information

Telephone: 514-398-5454
E-mail: pd.conted [at] mcgill.ca (subject: PDC%20in%20Data%20Science%20and%20Machine%20Learning)