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Program Requirements
Students completing this concentration will have training in a diverse set of methods in analytics and tools to conduct analyses as applied in a variety of managerial disciplines. Today, business professionals, managers, and entrepreneurs need to be able to leverage the power of data that is collected. The Business Analytics concentration provides students with essential skills and knowledge needed to navigate in the world of data. This Concentration offers courses with a strong practical and applied orientation from a variety of managerial disciplines.
Required Courses (6 credits)
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INSY 336 Data Handling and Coding for Analytics (3 credits)
Overview
Information Systems : Preparation and analysis of data for business analytics. Topics include: data acquisition, data manipulation and computer programming for statistical analysis.
Terms: Fall 2019, Winter 2020, Summer 2020
Instructors: Ganju, Kartik (Fall) So, Hyunji (Winter) So, Hyunji (Summer)
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MGSC 401 Statistical Foundations of Data Analytics (3 credits)
Overview
Management Science : This course will provide statistical foundations for data analytics. In this course, we will learn an introduction to advanced statistical techniques and methodologies including sampling, regression, time-series and multi-variate statistics. We will support our approach by looking at applied examples and real cases and datasets across several business areas, including marketing, human resources, finance, and operations. Students will apply their skills to interpret a real-world data set and make appropriate business recommendations.
Terms: Fall 2019, Summer 2020
Instructors: Serpa, Juan Camilo (Fall) Serpa, Juan Camilo (Summer)
Prerequisite(s): MGCR 271
Complementary Courses (9 credits)
3 credits from the following:
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INSY 446 Data Mining for Business Analytics (3 credits)
Overview
Information Systems : Practical methods and techniques for data mining and predictive analytics to solve business problems. Use of statistical tools for hands-on learning. Topics covered include supervised learning, unsupervised learning, and text mining.
Terms: Winter 2020
Instructors: Khern-am-nuai, Warut (Winter)
Prerequisite(s): INSY 336
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MGSC 404 Foundations of Decision Analytics (3 credits)
Overview
Management Science : This course teaches quantitative methods used in business decision making. Topics include: optimization models, decision trees, simulation, and computer simulation. Business applications of these techniques are emphasized. Students in this course will acquire expertise in computer based methods for decision making, through computer analysis of real-life problems.
Terms: Fall 2019
Instructors: Qi, Wei (Fall)
6 credits from the following:
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INSY 442 Business Intelligence and Data Analytics (3 credits)
Overview
Information Systems : Introduction to the methods and tools for analyzing business data to improve business decision-making, focusing on extracting business intelligence by analyzing data and online content for various business applications.
Terms: Fall 2019, Winter 2020, Summer 2020
Instructors: Teimourzadeh, Aria (Fall) Hwang, Insung (Winter) Teimourzadeh, Aria (Summer)
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INSY 446 Data Mining for Business Analytics (3 credits)
Overview
Information Systems : Practical methods and techniques for data mining and predictive analytics to solve business problems. Use of statistical tools for hands-on learning. Topics covered include supervised learning, unsupervised learning, and text mining.
Terms: Winter 2020
Instructors: Khern-am-nuai, Warut (Winter)
Prerequisite(s): INSY 336
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INSY 448 Text and Social Media Analytics (3 credits)
Overview
Information Systems : The unlimited opportunities that exist today to leverage the power of user generated content analytics, focusing on questions ranging from strategic to operational matters pertaining to a firm鈥檚 social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance.
Terms: Winter 2020
Instructors: Yoo, Changseung (Winter)
Prerequisite: MGCR 331
Restriction(s): Open only to U2 and U3 students.
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MGSC 404 Foundations of Decision Analytics (3 credits)
Overview
Management Science : This course teaches quantitative methods used in business decision making. Topics include: optimization models, decision trees, simulation, and computer simulation. Business applications of these techniques are emphasized. Students in this course will acquire expertise in computer based methods for decision making, through computer analysis of real-life problems.
Terms: Fall 2019
Instructors: Qi, Wei (Fall)
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MRKT 440 Marketing Analytics (3 credits)
Overview
Marketing : Analytic techniques available to marketing managers including practice with actual data sets to use the techniques. Topics covered will include customer and product analytic models, digital marketing, and marketing resource allocation.
Terms: Fall 2019
Instructors: Ma, Yu (Fall)
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ORGB 330 People Analytics (3 credits)
Overview
Organizational Behaviour : This is the era of big data. Companies and organizations are collecting an enormous amount of information and we are only just beginning to grasp the ways in which this information might be used. This course covers the emerging field of people analytics, which involves applying data collection and analysis techniques to improve the management of people within organizations. We will cover current people analytics techniques, common pitfalls, and possible shortcomings of people analytics, as well as the ethical questions involved in undertaking such analyses.
Terms: Winter 2020
Instructors: St Denis, Xavier (Winter)
