Foundations in Data Science Bootcamp

Our virtual four-day Foundations in Data Science Bootcamp will give you a solid and engaging introduction to big data analytics, tools, and statistics. Enhance your learning experience with interactive lectures and activities led by expert practicing data scientists. Small classes will provide you with the opportunity to interact closely with your instructors and other students.

Bootcamp Details

Delivery: Fully online (synchronous learning); 22 hours of instruction

Dates: Tuesday, June 8–Friday, June 11, 2021

Time: 9:30 a.m.–3:00 p.m. each day (includes two 15-minute breaks)

Note: No additional software is required.

Registration information will be available soon. To receive updates, contact Brigid Elmy and provide your email address.

Why Should I Participate in This Bootcamp?

  • You have no background knowledge in data science and are interested in gaining a better understanding of what it is and how it could be used to enhance organizational goals.
  • You do not have a background in data science, but you have basic computational mathematics and numeracy skills; you would like to understand big data science to determine if this is an area you would like to further pursue for upskilling and/or as a career.
  • You wish to become comfortable using big data science terminologies and engaging with data scientist colleagues and others with whom you are collaborating on data science projects.

What Will I Learn?

  • An overview of data science
  • Statistical tools for data analysis
  • Data visualization techniques
  • Methods for creating data-driven stories
  • Machine learning methods (supervised and unsupervised)
  • Model evaluation
  • Programming in R language
  • Data preparation, regression, clustering, and classification in R

Learning Objectives

  • Reframe a business challenge as an analytics challenge.
  • Become familiar with statistical concepts and machine learning.
  • Demonstrate the fundamental concepts and techniques of data analytics.
  • Define basic concepts related to data analytics.
  • Discuss the challenges related to big data analytics.
  • Identify current trends in big data analytics.


Dr. Ceni Babaoglu is a Senior Data Analytics Associate at The Chang School and a Data Science instructor at Ryerson University. Since receiving her PhD in Applied Mathematics from Istanbul Technical University, she has worked as a researcher and Mathematics instructor in Istanbul, Sweden, and Toronto. Four years ago, inspired after visiting a Data Science Laboratory – where she learned the practical applications of data analytics and how it involved (her specialty) mathematics – Ceni shifted her focus to data. Her current research is focused on numerical analysis, data mining, and machine learning programming. She is also an associate member of the Yeates School of Graduate Studies at Ryerson University and co-supervises the major research projects of Data Science and Analytics MSc students.

Connect with Ceni on LinkedIn, or visit her website to learn more about her areas of expertise, teaching, and research.

Dr. Tamer Abdou is an Assistant Director of Data Science at The Chang School at Ryerson University. Tamer's primary research area is software engineering, where he mines historical project data and applies artificial intelligence and statistical analysis techniques. His aspiration is to build a pragmatic solution that enables practitioners and researchers to improve and optimize software process quality through an affordable amount of resources. Some of Tamer's research has been conducted in collaboration with industrial sectors and/or adopted by companies such as Mozilla Corporation and IBM CAS Canada. Tamer holds his doctoral degree in Computer Science from Concordia University.

Visit Tamer on Google Scholar for an overview of his recent publications.

Need More Information?

For more information about the Foundations of Data Science Bootcamp, contact Brigid Elmy at