The Certificate in Data Analytics will educate managers, data analysts and others about the methods and techniques of data analytics, the problems inherent in big data analysis, and the potential of using software that learns or improves its analytical approach with minimal or no assistance.
The availability of data has exceeded the capacity of most managers and executives to analyze and interpret effectively. Since data collection continues to accelerate, the potential for its use is becoming even more evident and threatens the ability of managers to do their jobs efficiently as more competitors learn about analytics. The need for management to master data analytics is apparent.
This program utilizes Python in all three modules with participants coding alongside the instructor. Prior coding experience is not necessary to participate in this program and you do not need a statistical or technical background.
Online Modules and Learning Outcomes
Module 1 • Exploratory Data Analysis & Data Visualization
Investigate the variables, examine common applications involving the distributions of categorical variables and histograms of the numeric variables, and explore the relationships among sets of variables.
Module 2 • Data Cleaning & Manipulation
How do we clean raw data, filter out fields that are obsolete or redundant, deal with missing data and outliers, and perform transformation on certain variables to make projections more accurate?
Module 3 • Supervised & Unsupervised Machine Learning
Discuss step-by-step, hands-on applications using Python with a free software machine learning library (Scikit-learn) for real-world business problems using widely available data mining techniques in the area of supervised and unsupervised learning applied to real-world data sets.
This program is for managers, data analysts, and others who want to keep abreast of the latest methods for enhancing return on investment by creating business value with analytics, and gain a better understanding of how to leverage analytics in decision-making.
This program is also suitable for individuals who may not be in an analytics role but would like to learn more about this in-demand topic.
Format & Dates
- Fully online
- 1 half-day / week
- 6 week program
- 9:00 am - 12:00 pm online. (Toronto time, EST)
Spring 2024 Dates
March 15 & 22, 2024
April 5 & 12, 2024
April 19 & 26, 2024
Contact Us for an additional early bird discount code (valid until January 15, 2024)!
Individual registrations - $1,700 + HST / participant. Group registrations of 2 or more - $1,550 + HST / participant.
The fee includes all three modules and course materials. A certificate will be issued upon successful completion of the program and post- program assignment/project.
Meet the Instructor
Gerhard Trippen, PhD, MSc, is an Associate Professor, Teaching Stream in Quantitative Methods and Operations in the Department of Management at the University of Toronto Mississauga (UTM), with a cross-appointment to the Operations Management and Statistics area at the Rotman School of Management.
Most recent courses taught include Big Data Analytics for the Master of Management Analytics (MMA) program at the Rotman School of Management as well as Big Data and Marketing Analytics in the undergraduate programs at Rotman and UTM.
Past Participant Spotlight
As my first foray into programming, I could not have been more thrilled with the program. Dr Gerhard Trippen, with his wealth of experience and enthusiasm, was exceptional at demystifying the world of Python. The best part? The ability to apply concepts to my world of finance and immediately spend more time focusing on drawing insights from data.
Westlake Royal Building Products
“Coming from a background which did not include any study of statistical analytics or their uses, this course was incredibly mind-broadening. I've done document analysis and worked in finance and the analytical concepts in this course were so different yet were so applicable with a large enough data set. Both the professor and all the supporting staff were helpful and numerous additional resources were provided as needed. Thank you Professor Trippen”
Carmen Brown Prades
Facilities Management and Planning
University of Toronto Mississauga