Change the world through data
Data Science is a booming field that uses computing and statistical reasoning to generate valuable insights from data. It has emerged as a key competence for any organization, including both businesses and governments.
As a student in the Data Science program at York University, you will master the statistical methods, computation skills and data analysis techniques that enable data scientists to extract knowledge from data. In your studies, you will become familiar with the nature and needs of analyzing large and complex data through case studies in specific domains such as business, health, and digital media, as well as through a capstone experience that engages students in research with data in an industrial setting.
Honours Major BA Program
Honours Major BSc Program
A unique aspect of our program, compared to other Data Science programs in Ontario, is our streams. You will be required to select a stream or specialization in a domain of application. Along with the required courses in statistics, computing and mathematics, you will also take courses in your selected stream, which could be any of the fields below.
Take courses from Schulich School of Business in marketing, finance, accounting, and management.
Take courses from the School of Health Policy and Management in health informatics, healthcare quality and decision making.
Take courses from the School of the Arts, Media, Performance & Design in interactive digital media and video game development.
Take courses on operations research in the Department of Mathematics & Statistics.
Take courses on numerical and computational methods in the Department of Mathematics & Statistics.
The capstone experience further differentiates this program from others in Ontario by bringing together students’ statistical, computing, and stream knowledge to work on a real-life problem from industry or the public sector. The full-year capstone course, taken in the fourth year, will provide you with hands-on research experience before graduation. You will meet with professionals from companies and organizations to learn about their problems related to data science and work together on a solution, reporting back to your instructor regularly on your project progress.
This experience will give you valuable, real-world experience – an edge when seeking employment after graduation.
Prospective employers were consulted in this program’s design to ensure career success after graduation. Our Data Science program offers a middle ground between computer science and statistics, to ensure you are well prepared to obtain meaningful employment in data science-related positions in a wide range of industries.
You will take courses in computer programming (Python, R), communications, data visualization, data structures, machine learning, data analytics, ethics, and more. You will learn both the theoretical and applied perspectives of data science technologies and be knowledgeable in your stream subject. The capstone course will provide real-world experience that will open doors to internships and employment opportunities.
For a preview of information on the Data Science program, please see the Data Science channel of the Come Study Math & Stats at York Discord Server
Who will you work with in Data Science?
Dr. Jairo Diaz-Rodriguez
Dr. Diaz-Rodriguez’s research interests are in data science, machine learning, high-dimensional statistics, optimization and big data. Most of his research contains both theoretical development and practical applications, with strong interdisciplinary components, and cloud and parallel computing implementations. He will be teaching MATH1130 in the fall term.
Dr. Kelly Ramsay
Dr. Ramsay works mainly in robust statistics and nonparametric statistics. Under this umbrella, she has worked on functional data, change-point problems and data privacy. Some of her areas of application are medical imaging, such as f-MRI, finance, and speech recognition. She has also worked on data collection and web-scraping in the past. She will be teaching MATH1130 in the winter term.
Dr. Kevin McGregor
Dr. McGregor’s research has focused on biostatistics and various topics on statistical methods in genomics. In the past he has developed novel Bayesian methods to model community structure in the human microbiome by means of network and diversity estimation. He has also done work in methods for adjusting for cell-type heterogeneity in DNA methylation studies.
Dr. Steven Wang
Dr. Wang has research experience in data science with applications to health science including biostatistics and bioinformatics. His current research is focused on data science for health care such as analysis of ECG, EEG and PPG data from wearable devices. He also has thirty years of consulting experience in data science.
Dr. Yuejiao Cindy Fu
Dr. Fu is the Statistics Director in the Department of Mathematics and Statistics. Her main research interests are mixture models, empirical likelihood, density ratio models, and statistical analysis of high-dimensional data, spatial data, genetic data, gene expression data and DNA methylation data.
Dr. Xin Gao
Dr. Gao’s research areas include biostatistics, computational statistics, statistical theory, machine learning, high-dimensional data analysis, and data science. She have published over 60 publications in peer reviewed statistical journals. In the past, she has served as the president of the Southern Ontario Chapter of Statistical Society of Canada, associate editor of the Canadian Journal of Statistics, and a board member of the Statistical Society of Canada. She has collaborated with biomedical companies on various machine learning projects.
Dr. Yuehua Amy Wu
Dr. Wu is an elected member of the International Statistical Institute. She has co-authored 130 papers, published in Proceedings of the National Academy of Sciences, the Journal of Applied Statistics, and the Journal of Multivariate Analysis, among others. Professor Wu's research centers on statistical theory and methods in data science and statistical machine learning. Her current research interests include robust statistics, model selection, multiple change-point analysis, multivariate analysis, spatio-temporal modeling, high-dimensional statistics, financial econometrics, data mining, and computational algorithms.