Health Data and Visualization

This course is part of the UBC Micro-certificate in Health Data Analytics: Opportunities and Applications program. The program consists of three courses that can be taken individually or combined into the Micro-certificate.

Health Data and Visualization offers insights into the challenges of working with real-time clinical data. Key areas include data cleansing, the value of data warehouses, and the differences between experimentally generated and observational data. You will learn basic statistical design principles and the strengths and weaknesses of various study designs. Focus is given to understanding data limitations, the role of outliers, and the importance of visual formatting. You will gain the skills to effectively communicate your findings and learn about storytelling using data visualization to move your work from analytics to action. This course also covers the ethical considerations critical for working with health data, including data privacy and algorithmic bias.

By the end of the course, you will be able to:

• describe the messiness of real-time operational clinical data
• discuss the basic concepts of data cleansing and the benefits of data warehouses
• compare and recognize experimentally generated data and observational data, with reference to the strength of ensuing statistical conclusions
• explain foundational concepts of statistical design and analysis of experiments in a data science context
• identify the different types of data analysis questions and categorize a question into the correct type
• outline the strengths and limitations of various study designs for a particular health research question
• understand how to standardize visual formatting
• understand the underlying data and its limitations
• understand how outliers affect conclusions and limitations of presenting averages in skewed data sets
• understand the importance of communication and storytelling to convey the results to build on data visualization
• demonstrate how data science storytelling can lead to policy and plan changes
• explain the issues involved in data privacy and ethics, including aggregated data
• discuss algorithmic bias, examining its causes, consequences and strategies to mitigate bias in machine learning.

Course activities include readings, videos, tutorials, virtual small group discussions and online discussion boards.

How am I assessed?

Assessment takes place on successful completion of short assignments, student reflections and quizzes each week. These activities are marked using a proficiency scale, and your instructor provides you with informal feedback. The minimum passing grade for this course is 70%.

Expected effort

The duration of each course is four weeks, and the approximate time commitment expected for completing the course is 25 hours. Participants are expected to allocate approximately six hours per week to engage with the course materials, which encompass lecture videos, readings and activities.

Technology requirements

To take this course, and for the best experience, we recommend you have access to:

• an email account
• a computer, laptop or tablet
• the latest version of a web browser (or previous major version release)
• a reliable internet connection
• a video camera and microphone

Course format

This course is 100% online instructor supported with weekly office hours.

Each week there will be an opportunity to attend instructor or teaching assistant office hours if you need any help with course content. There will also be weekly small group discussions with your peers to provide you with a community of practice and enable you to network with others working in this field in BC.

The weekly live, virtual office hours are Thursdays, 6 to 7pm Pacific Times (subject to change).