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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)

• INSY 336 Data Handling and Coding for Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2018

  Instructors: Ganju, Kartik (Fall)

• MGSC 401 Statistical Foundations of Data Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2018

  Instructors: Serpa, Juan Camilo (Fall)

  • Prerequisite(s): MGCR 271

Complementary Courses (9 credits)

3 credits from the following:

• INSY 446 Data Mining for Business Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2019

  Instructors: Khern-am-nuai, Warut (Winter)

  • Prerequisite(s): INSY 336

• MGSC 404 Foundations of Decision Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2018

  Instructors: Qi, Wei (Fall)

6 credits from the following:

• INSY 442 Business Intelligence and Data Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2018, Winter 2019

  Instructors: Choi, Inmyung (Fall) Bassellier, Genevieve (Winter)

  • Prerequisites: MGCR 271, MGCR 331

  • Restriction(s): Not open to students who have taken INSY 434 when the topic was ‘Business Intelligence’. Only open to U2 and U3 students.

• INSY 446 Data Mining for Business Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2019

  Instructors: Khern-am-nuai, Warut (Winter)

  • Prerequisite(s): INSY 336

• MGSC 404 Foundations of Decision Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2018

  Instructors: Qi, Wei (Fall)

• MRKT 440 Marketing Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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: This course is not scheduled for the 2018-2019 academic year.

  Instructors: There are no professors associated with this course for the 2018-2019 academic year.

  • Prerequisite: MGCR 352

  • Restriction(s): Open only to U2 and U3 students. Not open to students who have taken MRKT 434 when topic was "Marketing Analytics".

• ORGB 330 People Analytics (3 credits)

  Offered by: Management (Desautels Faculty of Management)

  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 2019

  Instructors: Hollister, Matissa (Winter)

  • Prerequisite(s): MGCR 271 or an equivalent introductory statistics class, and MGCR 222.