Tableau for Health Data Analysis and Visualization

1:00pm to 3:00pm PST each day

This webinar-based training series takes place over four sessions on consecutive Mondays:
Session 1 - Monday October 4 | Session 2 - Monday October 11
Session 3 - Monday October 18 | Session 4 – Monday October 25


Overview

Register NowTableau software can help anyone see and understand their data.  It allows users to connect to almost any database, drag and drop data to create visualizations, and share with a click of the mouse. For those who are familiar with SPSS and R for data visualizations, Tableau offers comparable and added features. These include geospatial mapping of hotspots, executive style dashboards, visual analytics, and trend lines that are valuable for researchers, analysts and professionals working in health organizations.

Learning Objectives:

By the end of this course, participants will be able to:

  • Connect and import datasets into Tableau and conduct data quality checks and data cleaning.
  • Work with Data Sources to edit and save files in Tableau.
  • Become familiar with Tableau terms/features and use the interface to create data visualizations.
  • Prepare data visualizations (e.g., geospatial maps, heat maps, bar graphs), to create executive style dashboards and stories.
  • Work with formulas for data analytics, conduct basic calculations, and visual analytics for predictive modelling

Participants will also gain an appreciation for working with multiple health datasets including COVID-19 data, CIHI data, and the Canadian National Multiple Deprivation Index. Our analysis in the live teaching session will focus on city and provincial level data.

Course Outline

Session 1 - Introduction to Tableau and visualisations | Presenter: Vidhi Thakar

  • Tableau workspace and how to import data sets
  • Basics on how to conduct a data quality check in Tableau
  • Learning how to create data visualizations in Tableau (e.g., heat maps, bar graphs, bubble graphs)
  • Working with 3 datasets - Covid-19 Open Data Ontario and BC COVID-19 Open datasets and Canada Multiple Deprivation Index datasets
    • Plotting deprivation levels per province and by major cities (Toronto, Vancouver, St. John’s, Edmonton, Montreal) and comparing these deprivation hot spots with areas experiencing high incidences of Covid-19 to see if there is a correlation between the two.
    • Advantages of Tableau Public vs. Tableau desktop (e.g., data privacy, data protection).

Session 2 - Communicating with data - preparing Tableau stories and dashboards | Presenter: Vidhi Thakar

  • Communicating and preparing advanced data visualizations in Tableau with executive style, Tableau stories, including dashboards that synthesize bubble graphs, bar charts and heat maps.
  • Students will be able to follow the instructor’s activities and prepare Tableau stories that include a dashboard to communicate the key findings. 
  • Applied Case examples using Covid-19 Open Data Ontario, BC datasets and Canada Multiple Deprivation Index datasets
  • Applications of data to evidence-informed decision-making and public policy – case examples

Session 3 - Data cleaning, calculation and visualization in Tableau | Presenter: Melodie Song

  • Data cleaning and processing
    • Importing csv., json., pdf., files
    • Joining and splitting tables from different datasets
    • Filtering unwanted data without deleting data
  • Calculations using Tableau:
    • Understanding calculation types
    • Using 5 most common calculations
    • Other timesaving tips and tricks
  • Students will be able to download and clean datasets, conduct calculations for data visualization using the following datasets
    1. Ontario Long Term Care Homes and COVID 19 related data
    2. Service Ontario Report on Long Term Care Homes
    3. Ontario Marginalization Index, Public Health Ontario

Session 4 - Forecasting and graphing health data | Presenter: Melodie Song

  • Forecasting: generating trendlines, and maps with the Ontario Long-term Care dataset introduced in Session 3
  • Bythe end of this class, students will be able to upload an interactive story onto Tableau Public to compare:
    • a) a month-to-month trend of Long-Term Care Home (LTCH) outbreaks in private/public institutions
    • b) deaths by jurisdiction deprivation indices.

 


Who is this course designed for?

This course is designed for:

  • A person with little-to-no Tableau experience who wants to learn how Tableau can be useful to them.
  • A proficient Excel, SPSS or R user who wants to expand their skills to include Tableau analytics and visualization.

Course format

The interactive Gotowebinar software will provide remote access for participants to view the instructors’ screen, listen to the lecture in real time, and ask questions. Participants will also be able to follow along with the instructor’s activities if they have Tableau Public pre-installed on their computers. (See Tableau support details below for related details.

Open office hours will be provided on a drop-in basis. The first drop in session will be prior to the start of the course on Sept. 24, 2021 between 12-1pm. This session will be devoted to those who require support downloading Tableau software. Future drop in sessions will be scheduled as needed.

Course Materials

Participants will receive course slides (PowerPoint) and required readings prior to the start of the webinar. For practice between webinar sessions and for follow up study, participants will receive training datasets and sample formulas as well as exercises that outline the steps for data visualization in Tableau.

The following list of datasets will be made available via a download link as part of the participant course package upon registration. All participants should download the datasets onto their computers in advance of the course.

  1. Covid-19 Open Data Ontario
  2. BC COVID-19 Open datasets
  3. Canada Multiple Deprivation Index datasets
  4. Covid-19 Open Data Ontario, BC datasets and Canada Multiple Deprivation Index datasets
  5. Ontario Long Term Care Homes
  6. Service Ontario Report on Long Term Care Homes
  7. Ontario Marginalization Index, Public Health Ontario

Course Software

Tableau software can be downloaded from the following website: https://public.tableau.com/en-us/s/download

Course instructors will orient students to the Tableau software during the start of the first session. For those who need assistance downloading the Tableau software, a one hour drop-in session will be offered on Sept 24th from 12:00noon to 1:00pm.

For additional Tableau Help, please consult the following website:https://www.tableau.com/support/help

Course fees

Regular rate: $260
Student rate: $160


Presenters

Vidhi ThakkarVidhi Thakkar has completed her PhD in Health Policy from the Institute of Health Policy Management and Evaluation. Vidhi also received a Master of Science in Experimental Surgery from McGill University and her Bachelor of Health Sciences (Honours) at McMaster University. She is currently a Research Associate with the Canadian Longitudinal Study on Aging and has worked with the BC Ministry of Health. She has worked for more than 5 years in Canada’s healthcare system with organizations including provincial Ministry of Health and Long-term Care, BC Public Services, various hospitals, universities, and Cancer Care Ontario.

Her research interests include health informatics, evidence-informed policy making, patient-oriented research, health data, and health care accountability. Vidhi has been actively involved with the Canadian Association of Health Services and Policy Research and a board member for the Justice Emmett Hall Foundation. She has also won the Gordon Cressy Leadership Award at the University of Toronto. Vidhi teaches Health Informatics Data Analysis and Epidemiology at McMaster University. She enjoys connecting with scholars, researchers, policymakers, and health systems leaders on topics of Data Analysis, Health Informatics, and evidence-informed policy-making.

Melodie SongMelodie Song is a Canadian Institute of Health Research (CIHR) Health Systems Impact Fellow at Public Health Ontario (PHO). Her core mission as a fellow at PHO is to explore stakeholder perspectives (e.g., policymakers, decision-makers, and research scientists) on the intended use of Artificial Intelligence for equitable access to public health services, with a focus on immunization and the prevention and detection of communicable diseases.

In 2019, she was a postdoctoral research fellow at the Social Media Lab at Ryerson University specializing in social media network analysis on public opinion regarding health misinformation. She obtained a Health Policy PhD from McMaster University (2018), MSc in Public Health Policy and Management from the National Taiwan University (2011), and a BSc in Nursing (summa cum laude) from the same university (2009). She worked for the Ministry of Science and Technology of Taiwan.

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