How To Become A Healthcare Data Analyst (7 Easy Steps I Took!)

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When I graduated from college, I had no idea what I wanted to do with my life. All I knew was that I loved science and wanted to help people.

After some soul-searching, I decided to become a healthcare data analyst.

Healthcare data analysts are in high demand, and for good reason! They play a critical role in ensuring that our healthcare system runs smoothly.

But how do you become a healthcare data analyst? It’s not as difficult as you might think! In this article, I will share with you the 7 easy steps I took to make the transition from biology to healthcare data analytics.

So whether you’re just starting out or you’re looking to switch careers, read on for some helpful tips!

How To Become A Healthcare Data Analyst?

1. Get Educated and Informed on Data Analytics

The first step to becoming a healthcare data analyst is getting educated and informed on what data analytics is and how it works. You can do this by reading articles, watching videos, or taking courses.

I personally took multiple courses online and 2 in-person courses during this learning phase! You don’t need to learn everything in data analytics all at one go, because chances are, you’ve already had a statistics course in college or in high school where you learned some stuff about data already.

I personally took 1 course on Udemy, 1 course under Gen Infinity Academy, and one under Smartcademy’s Data Analytics class.

Out of all 3, the latter 2 were conducted in person in Singapore and were similar to boot camps. The Udemy course was done online.

Each course lasted about 4 weeks, but I would say that the amount of time you spend on each course really depends on how much effort you’re willing to put in and how quickly you want to learn.

I took my time with the Udemy course because I wanted to understand everything thoroughly before moving on. For the other two courses, I already had some prior knowledge so I was able to move through the material at a faster pace.

At the end of the day, it really all comes down to how much time and effort you’re willing to put in!

One mistake I made while pursuing my path as a data analyst was that I wanted to somehow become a “certified health data analyst”.

In hindsight, there really is no such thing that can certify it, because the health data analysis field is still new and small.

Don’t limit your knowledge acquisition to just courses!

Here are some other platforms where I consumed data-related content:

  • Data science YouTube channels
  • Data science blogs
  • Data science clubs in school

I’m a huge fan of watching YouTube, so I thought it would make sense to check it out. So I started watching data science channels like Data Professor, Data Science Dojo, Tina Huang, and Ken Jee.

I also read data science blogs like Kaggle’s blog and Towards Data Science.

And last but not least, if you’re still in school, take advantage of any data science clubs or societies that your school might have!

NOTE: At this point, I’d recommend getting a decent laptop that’s good for data analysis, as it can be really slow to load larger datasets on older ones. (like my old gaming laptop)

This was how I spent my first few months after deciding that I wanted to become a healthcare data analyst. By the end of it, I had a much better understanding of what data analytics was and how it worked. And I was ready to take the next step.

Also, make sure you read up on health information management to find out if that’s something you want to pursue too.

At this point in time, you want to take it easy and not push yourself too hard. Learning programming and having exposure to data analytics concepts isn’t easy.

Make sure you’re learning all the basic concepts right!

2. Practice Developing Your Own Data Analytics Projects

The second step is to start making your own data analytics projects.

There are many ways to do this, but I would recommend starting with small side projects.

For example, you can try to analyze your own data (if you have any) or find publicly available datasets online and see if you can glean any insights from them.

There are many websites that offer free datasets, such as Kaggle, UCI Machine Learning Repository, and Data.gov.

While you may not fully understand how electronic health records work, try to find dummy patient data or anonymized health data that you can analyze.

Healthcare data analysts all over work with medical records and business data alike. This will highly depend on whether your department works with clinical data or KPIs for the healthcare organization.

Try to find those that are somewhat related to health information management. This may help you out. Also, if you’re interested in becoming a healthcare business analyst, focus more on the business data of a health organization.

Another way to get started is to participate in online competitions. Kaggle is a great platform for this, and they have a wide variety of competitions that you can choose from.

Not only will participating in these competitions give you some great experience, but it will also force you to step out of your comfort zone and try new things.

Healthcare data analysts need to show that they are competent and familiar with health data or even electronic health records.

So that’s what I did.

I personally participated in the ASEAN Data Science Explorer competition where I was a finalist. I tackled one of the Sustainable Development Goals: Universal Health Coverage where I presented a data analysis of the problem and pitched solutions to it.

And lastly, don’t forget to document everything!

Write up a report or blog post explaining what you did and what you learned from the project. This will be extremely helpful when you’re applying for jobs or internships later on.

You should explore using R IDEs like Rstudio (if you’re using R) to push your R code online on Rpubs. This will help you gain online visibility of your work!

Here’s mine that I used to update a long time ago.

3. Land Healthcare-Related Internships (With Analytics Job Scope)

The third step is to land healthcare-related internships with an analytics job scope.

This is important because it will allow you to gain some real-world experience in the healthcare industry while also getting a taste of what it’s like to work as a healthcare data analyst.

Please feel free to stalk and even connect with me on LinkedIn! I list all my past experiences there on my profile πŸ™‚

There are many ways to go about this, but I would recommend reaching out to your network or searching for internships online.

There are many websites that list internship opportunities, such as Indeed, LinkedIn, and Glassdoor.

My brother, also a healthcare data analyst, was actually able to find my current internship through LinkedIn!

He created a post on not being able to find an internship during the COVID-19 pandemic and was offered a position as a data analytics intern at a health tech startup.

So don’t be afraid to put yourself out there and ask for help.

And once you’ve landed an internship, make sure you give it your all and do your best! This is your chance to show off your skills and prove that you’re capable of being a healthcare data analyst.

You may wish to find an internship that will train you in health information management. This will help you to understand the sensitivity of working with health data.

Healthcare data analysis may not just be in the business context too. In my internship at the Agency for Integrated Care. I analyzed patient satisfaction surveys conducted in nursing homes all across Singapore.

In these surveys, I assessed patient safety data on an ordinal scale (Strongly disagree to strongly agree) for all the nursing homes.

As a healthcare analyst, look out for any way you can transform data and present health data analysis full of insights to key decision-makers of your health organizations.

4. Build a Strong Data Analytics Portfolio

The fourth step is to build a strong data analytics portfolio.

This is important because it will show potential employers that you have the skills and experience needed to be a health data analyst.

Your portfolio can include anything from reports and blog posts to actual projects that you’ve worked on.

If you don’t have any projects to showcase yet, don’t worry! You can always create your own projects or participate in online competitions (as I mentioned earlier).

And if you’re not sure how to get started, there are many templates and resources available online! Do a quick search on Google and you should be able to find something that will work for you.

Once you have a few projects under your belt, make sure to showcase them prominently on your website or online portfolio.

And don’t forget to include a link to your online digital portfolio and LinkedIn in your resume!

5. Learn Skills in the Data Analytics Tech Stack One by One

The fifth step is to learn skills in the data analytics tech stack one by one.

This is important because it will give you a strong foundation in the various technologies that are used by healthcare data analysts.

There are many different tools and technologies out there, but some of the most commonly used ones include:

  • SQL
  • Python
  • R
  • Tableau
  • Excel

If you’re not sure where to start, I would recommend picking one tool and learning it inside out. Once you’re comfortable with that, you can move on to the next one.

Make sure you also experiment with different IDEs for both R and Python so you will know which you prefer and which is best for your use case.

Read this article for more info on data science IDEs:

And if you need some help getting started, there are plenty of resources available online for it!

The trick now isn’t to master them all at once!

It’s about really knowing how to use one and then discovering how learning another tool can help solve different business needs at work.

You must understand that businesses need data analytics as something like a rearview mirror in a car, to display historical data (cars behind) and make decisions moving forward.

You need to know when and which tool can do what task better. For example, Tableau and Excel are excellent tools for rapid analysis.

Excel analyses can be a quick pivot table when results are needed within the hour. Tableau dashboards are great for when you need results within the day.

However, for long-term and more stable analyses, you’ll need to code in R or Python, which allows for higher customizability in data wrangling.

But given their complexity, they will take a longer time to develop.

Note that at this point, it may be wise to invest in a laptop that is best for biology majors or data science!

6. Stay Up-to-Date with the Latest Trends and Technologies

The sixth step is to stay up-to-date with the latest trends and technologies.

This is important because healthcare data analytics is constantly evolving, and you need to be able to keep up with the latest changes.

One of the best ways to do this is to follow industry news sources and YouTube channels that are well-informed.

These are just a few of the many sources out there, so make sure to explore and find ones that are relevant to you and your interests!

Another great way to stay up-to-date is by attending conferences and meetups. This is a great opportunity to network with other healthcare data analysts and learn about the latest trends in the industry.

Health information management can change with the times, and I suggest getting trained on health data analytics methods other health care data analysts use.

If you can’t attend any conferences or meetups in person, there are many online options available as well!

Finally, another great way to stay up-to-date is by reading books and articles written by healthcare data analysts already in the field.

I also noticed that nowadays, the electronic health records system is moving towards a better data infrastructure for easy querying by healthcare data analysts, and better machine learning accessibility by data scientists.

Medical records now provide a treasure trove of information for health data analysis and thereby may provide a benefit for health organizations.

7. Practice Coding Skills for Health Care Data Analyst Interviews

The seventh and final step is to practice coding skills for data analyst interviews.

This is important because it will help you prepare for the technical interview, which is a key part of the hiring process for many health data analyst positions.

There are many resources available online that can help you prepare for the technical interview, so make sure to do some research and find ones that are relevant to you.

And if you need some help getting started, I would recommend checking out the resources on Data Science Prep and Codeacademy!

On the healthcare side of things, I encourage you to also read up on health information management.

This is so you won’t get stumped when your hiring manager questions your knowledge of health information management.

You may also encounter some questions in your interview pertaining to handling data for clinical trials, convincing healthcare providers to adopt a data-driven strategy to the implementation of patient care, and the sensitivity of healthcare data management.

It’s important that you know that healthcare data analysts are different from other analysts in other industries.

Data analysis is needed everywhere in healthcare: clinical research, health informatics, bioinformatics, health insurance companies, patient satisfaction surveys, pharmaceutical data, population health management, and health information technology.

Don’t limit yourself to just healthcare business analytics!

What do healthcare data analysts do?

Healthcare data analysts are responsible for collecting, interpreting, and applying data from healthcare organizations. They use data analysis methods to identify trends and make predictions about the impact of various policies or treatments on patient outcomes.

This helps healthcare organizations improve their operations and provide better quality care for patients.

Health care data analysts may also manage databases, write reports, develop innovative solutions based on data analysis results, and help implement new initiatives that will improve patient care.

What is health data science?

Health data science is the application of data science techniques to healthcare-related tasks.

It involves the use of large amounts of data to identify patterns and gain insights into various aspects of healthcare, such as patient health, population health, hospital operations, epidemiology, disease progression, clinical outcomes, and more.

Health data science combines a variety of methods from statistics and computer science with knowledge from the medical and public health fields to extract meaningful information which can be used for decision making.

What are electronic health records?

Electronic health records (EHRs) are digital versions of a patient’s medical history. They include information such as diagnoses, treatments, medications, laboratory results, and imaging studies.

EHRs are securely stored on computers and can be accessed by authorized healthcare providers to provide better care for patients.

EHRs also provide valuable data which can be used for research and population-level disease management.

Do you need to be a certified health data analyst?

You do not need to be a certified health data analyst, but having a specialized knowledge base is essential. A degree in data science or related fields such as public health, informatics, or epidemiology is recommended but not necessary. Taking courses in programming languages such as Python and R can help prepare you to work with large health datasets and health information management.

A strong foundation in statistics and analytical techniques is also important for being successful as a healthcare data analyst.

Final Thoughts

That’s it! These are the seven steps that I took to become a healthcare data analyst without being a certified health data analyst.

I hope this article was helpful and that you found it informative for you on your path to becoming a healthcare data analyst.

Healthcare data analytics is a growing field with many opportunities. If you’re interested in pursuing a career in this field, I hope this article has helped give you some guidance on how to get started.

Of course, becoming a health data analyst is not an overnight process, and it will take time, effort, and dedication to achieve your goals.

But if you’re willing to put in the work, I believe that anyone can become a health data analyst.

I hope this article helps you have better ideas on how to become healthcare data analyst-worthy.

So what are you waiting for? Get started with your journey to becoming a “certified health data analyst” today!

Thanks for reading! πŸ™‚

Justin Chia

Justin is the founder and author of Justjooz. He is a Nanyang Technological University (NTU) alumni and a former data analyst.

Now, Justin runs the Justjooz blog full-time, hoping to share his deep knowledge of business, tech, web3, and analytics with others.

To unwind, Justin enjoys gaming and reading.

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