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What do you wish someone told you before you started working in data analytics?
Hello! I am an upcoming Fall 2025 university student majoring in Statistics with a potential minor in Public Policy/Computer Science looking to work in data analytics, preferably in the healthcare field. I would love any advice, tips, or lessons you've learned along the way--or things I should start preparing for before my first semester in college!
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Ibrahim’s Answer
Hey! First off, huge congrats on your upcoming start at university — majoring in Statistics with interests in Public Policy and Computer Science already puts you in a great spot for a future in data analytics, especially in the healthcare field. That's a path with a lot of impact and growing demand.
If I could go back and tell myself something before diving into data analytics, here’s what I’d say:
1. Learn to tell stories with data.
It’s not just about running the numbers or making graphs — it’s about understanding what the data means and communicating it in a way that others can understand, especially people who aren’t technical. In healthcare, this is super important because your audience could be doctors, policymakers, or patients.
2. Don’t wait to get hands-on experience.
Even in your first year, you can start working on small projects. For example, find public datasets (like CDC or WHO data), and explore them using tools like Excel, Python, or R. Ask simple questions like “Which states have the highest flu vaccination rates?” and then dig into why. It builds your confidence and gives you stories to share in interviews later.
3. Learn SQL early.
I can’t stress this enough. So much of data analytics (especially in the real world) involves extracting and cleaning data from databases. SQL is your best friend, and it’s often more important than people realize.
4. Understand the business or policy context.
Especially since you’re interested in public policy and healthcare, learning how data connects to decisions is key. Read up on how healthcare systems work, how data is used in public health, or how policy decisions rely on analytics.
5. Your soft skills matter.
Being kind, curious, and willing to ask questions will take you far. Join clubs, volunteer for data-related projects, and don’t be afraid to reach out to people. That’s how you build a network and learn what real-world data work looks like.
6. Don’t stress about having it all figured out now.
College is about exploring. It’s okay to try things, change direction, or combine your interests in unexpected ways. What matters is that you stay open, keep learning, and put yourself out there.
You’re already ahead of the game by thinking this way before even starting college — that’s honestly impressive. If you ever want help finding resources, project ideas, or just someone to look over your resume when the time comes, feel free to reach out. You’ve got this!
If I could go back and tell myself something before diving into data analytics, here’s what I’d say:
1. Learn to tell stories with data.
It’s not just about running the numbers or making graphs — it’s about understanding what the data means and communicating it in a way that others can understand, especially people who aren’t technical. In healthcare, this is super important because your audience could be doctors, policymakers, or patients.
2. Don’t wait to get hands-on experience.
Even in your first year, you can start working on small projects. For example, find public datasets (like CDC or WHO data), and explore them using tools like Excel, Python, or R. Ask simple questions like “Which states have the highest flu vaccination rates?” and then dig into why. It builds your confidence and gives you stories to share in interviews later.
3. Learn SQL early.
I can’t stress this enough. So much of data analytics (especially in the real world) involves extracting and cleaning data from databases. SQL is your best friend, and it’s often more important than people realize.
4. Understand the business or policy context.
Especially since you’re interested in public policy and healthcare, learning how data connects to decisions is key. Read up on how healthcare systems work, how data is used in public health, or how policy decisions rely on analytics.
5. Your soft skills matter.
Being kind, curious, and willing to ask questions will take you far. Join clubs, volunteer for data-related projects, and don’t be afraid to reach out to people. That’s how you build a network and learn what real-world data work looks like.
6. Don’t stress about having it all figured out now.
College is about exploring. It’s okay to try things, change direction, or combine your interests in unexpected ways. What matters is that you stay open, keep learning, and put yourself out there.
You’re already ahead of the game by thinking this way before even starting college — that’s honestly impressive. If you ever want help finding resources, project ideas, or just someone to look over your resume when the time comes, feel free to reach out. You’ve got this!
Thank you, Ibrahim!
Cassandra
Leo McKenzie
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Leo’s Answer
As someone who has a data analytics background and has worked in the medical field, I would suggest that you leverage all free or inexpensive avenues to learn the basic information and focus on things such as:
* What problems am I solving with data analytics?
* What is data analytics?
* Learn the fundamentals of Python language (all free online)
* Make math a big focus in your learning toolset (basic, geometry and algebra)
* Once you have a good handle on the above math, go onto Youtube and watch videos about how to learn some basic statistics.
This will get you a good understanding of the basics needed in order to start doing data analytics. It's math + statistics + a programming language and put together help yield insight on what's going on with the use of data. It also allows you to filter out the noise (data cleaning) and may give you the opportunity to build a model to predict what happens next or what you should do next.
* What problems am I solving with data analytics?
* What is data analytics?
* Learn the fundamentals of Python language (all free online)
* Make math a big focus in your learning toolset (basic, geometry and algebra)
* Once you have a good handle on the above math, go onto Youtube and watch videos about how to learn some basic statistics.
This will get you a good understanding of the basics needed in order to start doing data analytics. It's math + statistics + a programming language and put together help yield insight on what's going on with the use of data. It also allows you to filter out the noise (data cleaning) and may give you the opportunity to build a model to predict what happens next or what you should do next.
Updated
Adetomiwa’s Answer
As a Statistics major with interests in Public Policy, Computer Science, and healthcare data analytics, it’s essential to build a strong foundation early. Focus on mastering core statistical concepts and learning Python, especially libraries like pandas, NumPy, and scikit-learn. Supplement your coursework with online platforms like Coursera, and begin working on personal data projects—especially ones related to healthcare—to apply your skills and build a portfolio. Exposure to tools like SQL, Excel, Tableau, and GitHub will also give you an edge in real-world settings.
In addition to technical skills, get involved on campus through data science or health-related clubs, and take advantage of office hours to build relationships with professors who can offer guidance and research opportunities. Begin exploring internships early, even if just to observe the field, and make use of platforms like LinkedIn to network with professionals and alumni. Strong communication skills—particularly your ability to explain data to non-technical audiences—will be critical in healthcare analytics, so prioritize writing and presentation opportunities throughout your college journey.
In addition to technical skills, get involved on campus through data science or health-related clubs, and take advantage of office hours to build relationships with professors who can offer guidance and research opportunities. Begin exploring internships early, even if just to observe the field, and make use of platforms like LinkedIn to network with professionals and alumni. Strong communication skills—particularly your ability to explain data to non-technical audiences—will be critical in healthcare analytics, so prioritize writing and presentation opportunities throughout your college journey.
Thank you for the advice, Adetomiwa.
Cassandra
Updated
Andrew’s Answer
The language of statistics/data analytics is mathematics. Hence, it is imperative that you acquire a strong background in mathematics.
To prepare for advanced courses in statistics, you need to complete Calculus I, II, and III, Ordinary Differential Equations, Linear Algebra, Partial Differential Equations at the least. Additional courses such as Real/Complex Analysis, Numerical Analysis, Operational Research, and Game Theory will be beneficial.
To prepare for advanced courses in statistics, you need to complete Calculus I, II, and III, Ordinary Differential Equations, Linear Algebra, Partial Differential Equations at the least. Additional courses such as Real/Complex Analysis, Numerical Analysis, Operational Research, and Game Theory will be beneficial.
Thanks for the advice.
Cassandra
Updated
Sneha’s Answer
Hi Cassandra! That’s an awesome direction, you’re setting yourself up for a field that’s both impactful and growing fast! One thing I wish someone told me early on is: data analytics is just as much about communication as it is about numbers. You can build the perfect model, but if you can’t explain what it means to someone without a stats background, it won’t make the impact it could.
Start learning tools like Excel, SQL, and Python, even at a basic level, it’ll give you a big head start. Try to work on small projects (even personal ones) that connect data to real-world questions, especially in public health or policy. And lastly, stay curious and open to learning from mistakes. Every messy dataset teaches you something valuable. You’re going to do great! Good luck!
Start learning tools like Excel, SQL, and Python, even at a basic level, it’ll give you a big head start. Try to work on small projects (even personal ones) that connect data to real-world questions, especially in public health or policy. And lastly, stay curious and open to learning from mistakes. Every messy dataset teaches you something valuable. You’re going to do great! Good luck!
Thank you for the advice, Sneha.
Cassandra