For more information about these courses, contact the Department of Mathematics and Statistics .
Junior Courses
Data Science 201
Thinking with Data
An introduction to tools and techniques for managing, visualizing, and making sense of data. Includes an introduction to data cleaning, basic statistics, exploratory visualization, sensemaking, and data presentation. Course Hours:3 units; (2-3) Also known as:(formerly Science 201)
A hands-on introduction to basic coding skills, including core programming concepts and the fundamentals of reading, writing, and executing code – with a focus on data manipulation. Emphasizes important tools and practices for programming with data, including development environments, source control, and debugging. Course Hours:3 units; (2-3) Prerequisite(s):Data Science 201. Antirequisite(s):Credit for Data Science 211 and any one of Computer Science 215, 217, 231, 235, Computer Engineering 339, Engineering 233 or Digital Engineering 233 will not be allowed.
An introduction to fundamental data structures, including lists, stacks, trees, hash tables, and graphs, and their application for data processing, analysis, and storage. Covers the fundamental design and use of relational databases, with an emphasis on SQL. Course Hours:3 units; (2-3) Prerequisite(s):Data Science 201; and 3 units from Data Science 211, Computer Science 217, 231, 235, Digital Engineering 233 or Engineering 233. Antirequisite(s):Credit for Data Science 311 and either Computer Science 319 or 331 will not be allowed.
An introduction to fundamental data science concepts including basic data organization, data collection, and data cleaning. Provides a review of basic programming concepts in Python, as well as an introduction to problem-solving concepts including algorithmic complexity, recursion, vectorization, and regular expressions. Covers the fundamentals of data visualization and critical thinking. Course Hours:3 units; (3-0) Prerequisite(s):Admission to the Graduate Certificate in Fundamental Data Science and Analytics, or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.
An introduction to the foundations of statistical inference including probability models for data analysis, classical and simulation-based statistical inference, and implementation of statistical models with R. Course Hours:3 units; (3-0) Prerequisite(s):Admission to the Graduate Certificate in Fundamental Data Science and Analytics, or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.
An introduction to the creation of complex statistical models, including exposure to multivariate model selection, prediction, the statistical design of experiments and analysis of data in R. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 602 and admission to the Graduate Certificate in Fundamental Data Science and Analytics or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.
Explores the progression of data storage and processing, beginning with small, simple files and continuing to big, cloud-based databases. Data representation and simple file structures. An introduction to core database concepts with a variety of underlying data models, with a practical introduction to both SQL and NoSQL systems. Parallel and distributed storage and processing, including an introduction to cloud computing. Practical implications of storing and manipulating data at
scale, with emphasis on ethics, privacy, and security. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601 and admission to the Graduate Certificate in Fundamental Data Science and Analytics or the Graduate Diploma in Data Science and Analytics, or the Master of Data Science and Analytics.
Introduces deeper tools, skills, and techniques for collecting, manipulating, visualizing, analyzing, and presenting a number of different common types of data. With a data life-cycle perspective, looks into data elicitation and preparation as well as the actual usage of data in a decision-making context. Introduces techniques for visualizing and supporting the interactive analysis and decision making on large complex datasets. Focus on critical thinking and good analysis practices to avoid cognitive biases when designing, thinking, analyzing, and making decisions based on data. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.
Design of surveys and data collection, bias and efficiency of surveys. Sampling weights and variance estimation. Multi-way contingency tables and introduction to generalized linear models with emphasis on applications. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.
Latent variable models for clustering and dimension reduction. Parametric and nonparametric methods for regression and classification including naïve Bayes, decision trees, random forests, and boosting. Model assessment and selection. Deep learning. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.
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Provides advanced coverage of tools and techniques for big data management and for processing, mining, and building applications that leverage large datasets. Addresses database and distributed storage design for both SQL and NoSQL systems, and focuses on the application of distributed computing tools to perform data integration, apply machine learning, and build applications that leverage big data. Students will also examine the security and ethical implications of large-scale data collection and analysis. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Data Science.
Overview of the basic concepts and techniques in predictive analytics as well as their applications for solving real-life business problems in marketing, finance, and other areas. Techniques covered in this course include: decision trees, classification rules, association rules, clustering, support vector machines, instance-based learning. Examples and cases are discussed to gain hands-on experience. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.
Introduces fundamental concepts and modelling approaches to solve problems that are faced by decision makers in today’s fast-paced and data-rich business environment. Different decision alternatives are analyzed and evaluated with the use of computer models. Topics include the most commonly used applied optimization, simulation and decision analysis techniques. Extensive use will be made of appropriate computer software for problem solving, principally with spreadsheets. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.
Introduction to new tools for data analytics that can be used to discover, collect, organize, and clean the data to make it ready for analysis. Emphasis is placed on software tools used to interact with data sources and provision of user skills to create business applications that encompass a variety of business data sources; such as customers, suppliers, markets, competitors, and regulators. Software packages used to clean and organize the data for analysis will be introduced, as well as software to enable users’ understanding of the data that is collected. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.
Examination of tools and methods used in data analysis, including basic and advanced analytic tools, as well as machine learning techniques. One or more data analysis packages/programs are used to analyze different types of business data. Statistical and other analytic methods, such as data mining, machine learning and various techniques, and their application to business data analytics are explored. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604, 611 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.
An introduction to the fundamental statistical methods used in health data science including interpretation and communicating the results of these methods. Explores modelling using an epidemiological paradigm such as the assessment for modification and confounding. Introduces fundamental health research methods including study design and the evidence hierarchy. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
An introduction to the application of machine learning methods to problems in health data. The concepts of precision medicine and precision public health are introduced and the role of data science in these endeavors is explored. Using real examples from health data, various contemporary machine learning techniques are taught. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
         Antirequisite(s):Credit for Data Science 622 and Community Health Sciences 615 will not be allowed.
Many of the major health data assets that exist in Alberta and Canada will be explored through hands-on experience with several datasets. Issues relating to access, confidentiality, privacy and data stewardship will be examined. Methodological challenges related to data linkage will be discussed. Students will work with large health databases including health administrative data, electronic medical record data and various other databases. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics.
Explores the synthesis and summary of large volumes of information into interpretable and compelling results. Software packages useful for visualization of data are examined, including software for geographic information systems, augmented reality, and infographics. Data Science software commonly used in health industry is examined. Fundamental design principles are introduced to guide the approach to data presentation, communication, and interpretation. Course Hours:3 units; (3-0) Prerequisite(s):Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Health Data Science and Biostatistics. Antirequisite(s):Credit for Data Science 624 and Community Health Sciences 636 will not be allowed.
Provides a framework for students to initiate, perform and successfully complete a real-world project in Data Science and Analytics. This includes problem identification and formulation; discussion of legal, social, and ethical issues in data-driven projects; holistic processing and application of data; communication and data storytelling skills as well as leadership skills. In addition, students will be exposed to emerging topics in Data Science and Analytics. Course Hours:6 units; (6-0) Prerequisite(s):Admission to the Master of Data Science and Analytics and 24 units in 600-level Data Science courses.
Professional Internship in Data Science and Analytics
Students will integrate professional competencies and advanced analytical tools and apply them to a specific domain. Course Hours:6 units; (200 hours) Prerequisite(s):Data Science 691. Antirequisite(s):Credit for Data Science 693 and 695 will not be allowed. NOT INCLUDED IN GPA
Exposure to advanced data analytics methods and research methods applied to subdomains of data science, including business analytics and big data problems in healthcare. Course Hours:6 units; (6-0) Prerequisite(s):Data Science 691. Antirequisite(s):Credit for Data Science 695 and 693 will not be allowed. NOT INCLUDED IN GPA
COURSES OF INSTRUCTION