Post-Baccalaureate Diploma in Health Analytics

CampusX Kelowna (Jan)
Delivery Full-Time
Credential Diploma
Notes

Take the data science diagnostic test here.

This program is student loan eligible.

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Aerial view of E Building at Okanagan College Kelowna campus


Program Highlights

Identify data-driven solutions for health-care systems in positions that are highly sought after in industry. Gain an appreciation of how health systems work locally and abroad, and how applying mathematical, statistical and machine learning techniques make a difference in health organizations.

Tuition and Fees

2022-23: $9,580.63 (entire two years)

  • Fees include all mandatory fees: Activity fee, Health & Dental, Student Association fee, Education Technology fee, Development fee.
  • All amounts are approximate and are subject to change.
  • International students will pay International tuition fees

Program Details

This two-year post-baccalaureate diploma (60 credit/20 course) is aimed at students with a bachelor degree in any nursing, science, engineering, psychology, business or management program who wish to pursue a career in Health Analytics. Students will receive thorough training in statistics and data science. Term one of this program sets the mathematical and statistical foundation for higher level learning in the health and data science areas. In subsequent terms, students build on, and apply, these foundational skills to a diverse set of areas. While many of the applications have a health focus, the mathematical, statistical, and data science concepts learned are universally applicable to a wide range of disciplines.

At the end of this program students will:

  1. Understand healthcare systems in a variety of countries including how their history, geography, government and economy and privacy laws impact the healthcare system.
  2. Evaluate, define and explain data-analytic problems that offer the greatest opportunities for organizational benefits.
  3. Understand the relevant laws, regulations and standards involved with health data.
  4. Manage and manipulate data and create data visualizations using a variety of mathematical and statistical software.
  5. Apply mathematical, statistical and machine learning techniques to support organizational decisions as well as to identify new data driven opportunities.
  6. Participate in the planning and execution of a data science project culminating in recommendations based on the results of the analysis.
  • Health-Care Analyst / Senior Health-Care Analyst
  • Health Informatics Data Analyst
  • Systems Administrator
  • Program Manager Analytics
  • Data Solution Architect
  • Health Services Manager
  • Infection Prevention Manager

Students entering this program are expected to own their own laptop computer.  The computer should have Windows 10 installed  and with the following specs: 

  • A minimum of 8 GB of RAM.  

NOTE: If you can obtain 16 GB of RAM, this is a lot better. If 8GB is all you can obtain, then you will need to ensure you keep your stored files, and downloaded apps and programs to a minimum, and rely on external storage and cloud-based solutions. 

  •  For computing speed, you will require at least around 2+ GHz or better. 

NOTE: You can get away with 1.8-1.9 GHz, but speeds much slower might prove a little more time-consuming for you to complete assignment work, but can still function adequately most of the time.

  • An up-to-date, latest generation graphics card, that is optimized for speedy and optimal graphics rendering.  

NOTE: Many gaming laptops already have this. It is not necessary for you to use a gaming laptop, but we will be performing substantially graphics-heavy analyses for time to time, and the limitations of your graphics card can become an issue.  

You should ask your local computer outlet for assistance with the above, in the event that this is not clear to you, or if you have not purchased a laptop for advanced scientific studies before.  Also, although we recommend a PC with Windows 10, if you are running a different operating system (such as a MacOS, or an earlier version of Windows) you can still successfully complete the program.

Please be advised we perform our teaching activities exclusively in Windows 10 in this program, and this may mean that you could face challenges obtaining timely and/or effective assistance with issues that arise due to differences in our operating systems.  

Monitors: Many students have found it highly beneficial to invest in a second monitor for their laptop. This allows multiple screens and applications to be more easily viewed side-by-side when doing serious analysis work at home. In addition, having the ability to zoom effectively can greatly enhance clarity and visibility. We routinely make use of extended displays to greatly enhance our analysis and programming work, and we recommend that you at the very least investigate and decide whether this would be of benefit to you.

NOTE: Equipment requirements as of the 2021/22 academic year.

Students in the the Post-Baccalaureate Diploma in Health Analytics come from backgrounds in Computer Science, Health, Math, Statistics and other fields. No matter the background, however, applicants should have interest and skills in Math in order to be successful in the program.

 

Admission Requirements

Successful completion of a recognized Bachelor Degree in any science, nursing, engineering, psychology, or management program. A post-secondary basic calculus course, or equivalent, is highly recommended. A keen interest in mathematics and data science is required for this program.

A student who has completed a recognized undergraduate degree in a program different than those listed above may be admitted to the program provided they pass the Okanagan College Basic Algebra Proficiency Test with a minimum score of 20/25 AND the Calculus Readiness Test with a minimum score of 16/25.

 

Program Outline

Semester 1
DSCI 300 Data Wrangling and Visualization
DSCI 310 Mathematics Computation
DSCI 321 Health Care Analytics
STAT 230 Elementary Applied Statistics
MATH 314 Calculus and Linear Algebra with Business Applications
Semester 2
DSCI 400 Machine Learning I
DSCI 322 Comparative Health Systems
BUAD 283 Management Information Systems
MATH 251 Introduction to Discrete Structures
STAT 240 Applied Statistics II
Semester 3
DSCI 401 Machine Learning II
DSCI 324 Health Care Information Systems
DSCI 420 Mathematics for Machine Learning
STAT 310 Regression Analysis
Elective: Any 3 credit academic course
Semester 4
DSCI 323 Epidemiology and Health Analytics
DSCI 315 Dashboards and Analytic Reporting
STAT 311 Modern Statistical Methods
DSCI 490 Data Science Project
Elective: Any 3 credit academic course

Successful completion of the prescribed and elective courses as listed in the program outline with a minimum graduating grade average of 60%.

 

 

Additional Information

View the official Calendar details and policies
Learn more about the Department
View the Tuition and Fees page

 

 

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