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Data Science DATA

DATA 201 211 305 311 501 601 602 603 604 605 606 607 608 611 612 613 614 621 622 623 624

Instruction offered by the members of the Faculty of Science, Haskayne School of Business and Cumming School of Medicine.

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; H(2-3)
Also known as: (formerly Science 201)

Data Science 211

Programming with Data

A hands-on introduction to basic coding skills, including core programming concepts and the fundamentals of reading, writing, and executing code â€“ 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; H(2-3)
Antirequisite(s): Credit for Data Science 211 and any one of Computer Science 215, 217, 231, 235, Computer Engineering 339 or Engineering 233 will not be allowed.

Senior Courses

Data Science 305

Computational Statistical Modelling

Random variables and their probability models. The Central Limit Theorem and parameter estimation. Statistical modelling of univariate and multivariate data with applications to discrete and continuous data. Data transformations. Introduction to simulation-based inference including randomization and permutation tests.
Course Hours: 3 units; H(3-2)
Prerequisite(s): Data Science 201; one of Data Science 211, Computer Science 217, 231 or 235; and one of Statistics 205, 217, 327, Biology 315, Economics 395, Political Science 399, Psychology 300, Sociology 311, Engineering 319 or Linguistics 560.
Antirequisite(s): Credit for Data Science 305 and any one of Statistics 323, Psychology 301 or Sociology 315 will not be allowed.

Data Science 311

Data Processing and Storage

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; H(2-3)
Prerequisite(s): Data Science 201; and one of Data Science 211, Computer Science 217, 231, 235 or Engineering 233.
Antirequisite(s): Credit for Data Science 311 and either Computer Science 319 or 331 will not be allowed.

Data Science 501

Data Science Capstone

A substantial research project in the field of Data Science. The project will consist of all of the elements in the data cycle: collection, cleaning, exploratory analysis, statistical and computational analysis and presentation.
Course Hours: 3 units; H(1.5-5)
Prerequisite(s): One of Data Science 311 or Computer Science 471; and one of Data Science 305, Statistics 323, Biology 315, Sociology 315, Economics 395, Linguistics 560, Psychology 301 or 312.

Graduate Courses

Data Science 601

Working with Data and Visualization

An introduction to fundamental data science concepts including basic data organization, data collection, and data cleaning. Includes a review of basic programming concepts in Python, as well as an introduction to the fundamentals of data visualization and critical thinking with data. Also provides an introduction to data ethics, security, and privacy.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Certificate in Fundamental Data Science and Analytics, or the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 602

Statistical Data Analysis

An introduction to the foundations of statistical inference including the application of probability models to data, as well as an introduction to simulation-based and classical statistical inference, and the creation of statistical models with R.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Certificate in Fundamental Data Science and Analytics, or the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 603

Statistical Modelling with Data

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; H(3-0)
Prerequisite(s): Data Science 602 and admission to the Post-baccalaureate Certificate in Fundamental Data Science and Analytics or the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 604

Big Data Management

An introduction to data storage and manipulation at both desktop and cloud scales. Introduces core database concepts and provides a practical introduction to both SQL and NoSQL systems. Also introduces parallel and distributed computing concepts including distributed storage and large scale parallel data processing using MapReduce. Design and implementation of new data visualizations to aid analysis, with emphasis on the practical and ethical implications of design and analysis decisions.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Data Science 601 and admission to the Post-baccalaureate Certificate in Fundamental Data Science and Analytics or the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 605

Actionable Visualization 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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 606

Statistical Methods 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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 607

Statistical and Machine Learning

Advancement of the linear statistical model including introduction to data transformation methods, classification, model assessment and selection. Exposure to both supervised learning and unsupervised learning.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 608

Developing Big Data Applications

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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 611

Predictive Analytics

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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 612

Decision Analytics

Introduces fundamental concepts and modelling approaches to solve problems that are faced by decision makers in todayâ€™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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 613

Introductory Data 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 usersâ€™ understanding of the data that is collected.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 614

Advanced Data 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; H(3-0)
Prerequisite(s): Data Science 613 and admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 621

Advanced Statistical Modelling

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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 622

Machine Learning for Precision Health

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.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 623

Big Data in Health

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; H(3-0)
Prerequisite(s): Admission to the Post-baccalaureate Diploma in Data Science and Analytics.

Data Science 624

Advanced Exploration and Visualization in Health

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