DHT Curriculum ♟︎

Course Description:
This course teaches basic programming skills to non-programmers and introduces them to the value of those skills. Students will learn about the various capabilities of the R programming language and participate in discussions about the purpose of programming including task automation and interactive web design. Students will be introduced to elementary data types, control flow and functions as well as functional and object oriented programming. Students will practice approaches to problem solving with computer programs and learn debugging strategies. By the end of the course, students are expected to create a program that helps them solve a problem or perform a task (either self-chose or assigned) in the context of data science.

Course Description:
This graduate course takes students with a basic background in statistics and equips them to tackle massive data sets in health. The focus will be on advanced statistical tests in machine learning and assemble such tests by accessing and validating publicly available code in the R programming language and creating their own code as needed. Students will also learn additional techniques pertaining web scraping, working with unstructured data, data cleaning and data governance building upon the course Data Science in Health I. The course will emphasise creative approaches to analysing data and how to be critical of misleading analysis. Each class will involve both lecture and weekly tutorial assignments. The major project for the course will involve a large health data set that teams will compete to analyse.

Course Description:
In this course, we discuss data analytics and visualizations using Tableau Desktop and Tableau Prep software. We survey methods in data cleaning and prepping through ETL (Extract, Transform, Loading). We examine best practises in developing dashboards and understand how to tell a story with health and biological data. Our overall goal is to provide a foundation for using Tableau in data-driven decision making in the life sciences. Visualization with Tableau will be supplemented with R coding techniques students have learned from previous courses.

Upon completion of this course, students will be able to frame various classes of healthcare problems as analytics problems using Tableau, to appropriately identify data sources and build visualizations from them. Students will get practice in planning and developing interactive dashboards that help answer analytical problems and provide data accessibility to a non-technical audience.

Course Description:
This foundational course introduces students to a broad range of the critical managerial concepts that are required to operate success­fully in today’s biotechnologically focused organisations. Topics covered include forms of business ownership, an introduction to financial statements, financial statement analysis, time value of money, marketing manage­ment, market segment­ation, product positioning, the marketing mix, pricing decisions, channel and marketing commun­ications management, as well as some aspects of organisational behaviour and strategic management. Theory and application are combined through the use of readings, case studies, presentations and a group project.

Course Description:
This course will introduce students to the development of a wide range of product categ­ories and topics pertaining to the commercial­isation of health­care products. The course will touch upon medical devices, wearable technology, clinical trial design, biopharma­ceuticals, digital health, big data in health, medical apps, biomarkers, medical marketing, treatment guidelines, screening tools, diagnostics and social listening. Under­standing clinical trial design and the regulatory pathway through the US FDA is a major focus of the course. There will be an emphasis on digital health regulation. The course will also introduce students to 3D printing and its applications in health­care. Students will be required to get familiar with the digital modelling tool Blender. Students will explore segment­ation of physicians based on their clinical practices, drawing upon some of their data science training. The major project for the course will focus on the use of social listening for a product to derive insights into different issues based on the expressed interests of an industry partner. Students will be required to develop a strong level of mastery with the social listening tool Brandwatch and combine that with their foundational knowledge in the course, along with their data science training, to derive insights.

Course Description:
This series of centre­piece courses is designed to grant our students a more in-depth appreciation and understanding of the biotech­nology and biopharma­ceutical industry in a corporate and/or industrial setting, and to apply their knowledge and skills in a real-world context. Students are required to complete two 4-month full-time Work Term placements that are arranged by the course coordinator to ensure that the role, responsibilities and activities are at a graduate level. Credit-granting responsibilities reside with the course instructor, based on assignments and feedback from students’ Work Term supervisors.

Required preparatory exercises and assignments must be completed in the Fall and Winter/Spring sessions, leading up to the start of the placement in Summer, in order for students to qualify for Work Term I. These preparatory requirements can involve résumé work­shops, one-on-one meetings with members of the MBiotech team, attendance at the annual Career Day, and more.

Students in Work Term II may continue with the same employer from Work Term I, or alternatively with a new employer or department. Students’ performance and their work experiences is evaluated in a manner similar to that for Work Term I.

Note: BTC1920Y, Work Term III is an optional elective course that extends the placement experience to a full 12 months’ duration. Students taking Work Term III share their experiences by means of a mandatory Networking Night component in late April or early May.

Students receive credit/no credit grades for all three courses.

Course Description:
This course has two parts that challenge students to think about the commercial­isation of digital health products. In the first half of the course students will build upon their training in digital health regulation by doing a reverse engineering assignment on a technology (e.g., health software). Students will also be introduced to detecting hype around a health tech­nology and to distinguish this activity from more meaningful indicators of validation. Human factor engineering will be introduced in the context of basic psych­ology and its application to usability analysis and product design. The first half of the course involves individual student presentations that forecast the future of a digital health product and provide a critical analysis of the technology. The second half of the course challenges the students to think about creating their own digital technology. Special emphasis will be placed upon developing a compelling use case, regulatory strategy and market analysis. The second half of the course shifts to independent study by student teams that is supervised by the instructor.

Course Description:
In this course, we will define tech­nological innov­ation as the process of lever­aging new ideas to create economic value and deliver this value to share­holders, employees, consumers, and our society at large. This process involves critical strategic choices that are common to most organisations, from small startups to large established companies: What is the best way to bring an idea to the market, and to arrange production and distribution? How should we redesign our internal organisation, as well as the system of partnerships and relationships with external players? Should we redefine our vertical and horizontal boundaries, for example by outsourcing some activities or entering new geographical markets? Throughout the course, we will refine our ability to approach and find the best answer to these (and many other) questions. Using an applied and discussion-based method, we will learn how to effectively convert a creative idea into a valuable innovation.