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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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9 answers
Updated
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
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
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
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
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
Calixto Gutierrez
Data Analyst and Project Manager
3
Answers
Updated
Calixto’s Answer
Hello Cassandra!
Some of the things I wish I learned earlier are:
Story: it's all about telling a story. Both to better communicate what is it you need or discovered but also because stories are more compelling than raw data points. You want to identify the problem and use data to solve it, but data is mostly a tool and not the desired outcome.
The people: In order to understand the problem you want to solve you want to involve those that are most affected by it. They are usually the experts and will help you better understand what needs to be done and what is the actual impact of solving it. A great addition to anyone in data analytics is communication skills not only to be clear to your audience but also to persuade stakeholders to help you with information you will not get otherwise, or to clear roadblocks that make your work harder and the solution take longer.
Simplify both in data visualization and everything many times we want to share a lot of information and it ends up being hard to consume. Try to be really clear and show the least but most important information that will help the audience get what they want. Also consider simplifying in the sense of taking the least amount of steps to complete something. Try to learn how to automate with your usual tools, even excel lets you automate a lot and this will make everything easier.
Some of the things I wish I learned earlier are:
Story: it's all about telling a story. Both to better communicate what is it you need or discovered but also because stories are more compelling than raw data points. You want to identify the problem and use data to solve it, but data is mostly a tool and not the desired outcome.
The people: In order to understand the problem you want to solve you want to involve those that are most affected by it. They are usually the experts and will help you better understand what needs to be done and what is the actual impact of solving it. A great addition to anyone in data analytics is communication skills not only to be clear to your audience but also to persuade stakeholders to help you with information you will not get otherwise, or to clear roadblocks that make your work harder and the solution take longer.
Simplify both in data visualization and everything many times we want to share a lot of information and it ends up being hard to consume. Try to be really clear and show the least but most important information that will help the audience get what they want. Also consider simplifying in the sense of taking the least amount of steps to complete something. Try to learn how to automate with your usual tools, even excel lets you automate a lot and this will make everything easier.
Updated
Liqi’s Answer
Hi Cassandra,
Love that you’re already thinking ahead — starting with a Statistics major and interest in healthcare analytics is a great combo! Here are a few things I wish someone had told me early on:
1. Less is more. When building dashboards or reports, focus on clarity and actionability. It’s tempting to include everything, but if everything’s important, then nothing stands out. Highlight the key insights that drive decisions.
2. Understand the “why.” Don’t just crunch numbers — understand the business or policy question behind them. Ask why something matters and how your analysis helps people make better decisions. That’s how you create impact.
3. Stay curious and keep learning. Tools and technologies change fast in data analytics. Be open-minded, explore what’s trending (like SQL, Python, Tableau, dbt, or AI-driven analytics), and never stop experimenting.
4. Communicate your findings. The best analysts aren’t just good at data — they tell compelling stories with it. Practice explaining your insights simply to non-technical audiences.
You’re already on a great path — stay curious, keep learning, and focus on impact over output. You’ll go far!
Love that you’re already thinking ahead — starting with a Statistics major and interest in healthcare analytics is a great combo! Here are a few things I wish someone had told me early on:
1. Less is more. When building dashboards or reports, focus on clarity and actionability. It’s tempting to include everything, but if everything’s important, then nothing stands out. Highlight the key insights that drive decisions.
2. Understand the “why.” Don’t just crunch numbers — understand the business or policy question behind them. Ask why something matters and how your analysis helps people make better decisions. That’s how you create impact.
3. Stay curious and keep learning. Tools and technologies change fast in data analytics. Be open-minded, explore what’s trending (like SQL, Python, Tableau, dbt, or AI-driven analytics), and never stop experimenting.
4. Communicate your findings. The best analysts aren’t just good at data — they tell compelling stories with it. Practice explaining your insights simply to non-technical audiences.
You’re already on a great path — stay curious, keep learning, and focus on impact over output. You’ll go far!
Updated
Bryan’s Answer
What a fantastic plan! You are so ahead of the game, and that major/minor combo is perfect for working in healthcare data. That's a field where you can make a huge impact.
It’s a great question to ask before you start. Here’s some "wish I'd known" advice based on those ideas:
- Don't Sleep on Excel: Seriously. I know it sounds basic, especially when you're about to learn cool stuff like Python and R. But I guarantee you, mastering Excel is a superpower. Knowing pivot tables, VLOOKUPs, and just being fast at cleaning data in a spreadsheet is something you will use constantly, and it will make you look like a wizard.
- Always Ask "Why?" (and "So What?"): This is maybe the most important one. Your job isn't just to run code or make a graph. It's to solve a problem. Always, always ask questions like, "What problem are we really trying to solve here?" or "What decision will someone make based on this data?" In healthcare, this is a huge deal. The goal is never "to run a report"; it's "to figure out why patients are missing appointments."
- Your Soft Skills Are Your Real Hard Skills: You're going to get tons of technical (hard) skills in your classes. But the analysts who get promoted are the ones who have strong soft skills. This means being able to explain your findings to a nurse, a doctor, or an exec who doesn't know (or care) about stats. It's about teamwork and, most of all, telling a story with your data.
- Focus on Your Logic, Not Just the Tools: The tools will always change. The hot programming language today might be old news in 10 years. But your problem-solving logic is forever. Your stats major is the perfect place to build this. Focus on how you break down a complex question, how you check if your data "makes sense," and how you logically get from a messy spreadsheet to a clear answer.
You're on such a great path. Stay curious, and don't be afraid to ask questions. You're going to do awesome!
It’s a great question to ask before you start. Here’s some "wish I'd known" advice based on those ideas:
- Don't Sleep on Excel: Seriously. I know it sounds basic, especially when you're about to learn cool stuff like Python and R. But I guarantee you, mastering Excel is a superpower. Knowing pivot tables, VLOOKUPs, and just being fast at cleaning data in a spreadsheet is something you will use constantly, and it will make you look like a wizard.
- Always Ask "Why?" (and "So What?"): This is maybe the most important one. Your job isn't just to run code or make a graph. It's to solve a problem. Always, always ask questions like, "What problem are we really trying to solve here?" or "What decision will someone make based on this data?" In healthcare, this is a huge deal. The goal is never "to run a report"; it's "to figure out why patients are missing appointments."
- Your Soft Skills Are Your Real Hard Skills: You're going to get tons of technical (hard) skills in your classes. But the analysts who get promoted are the ones who have strong soft skills. This means being able to explain your findings to a nurse, a doctor, or an exec who doesn't know (or care) about stats. It's about teamwork and, most of all, telling a story with your data.
- Focus on Your Logic, Not Just the Tools: The tools will always change. The hot programming language today might be old news in 10 years. But your problem-solving logic is forever. Your stats major is the perfect place to build this. Focus on how you break down a complex question, how you check if your data "makes sense," and how you logically get from a messy spreadsheet to a clear answer.
You're on such a great path. Stay curious, and don't be afraid to ask questions. You're going to do awesome!
Updated
Lin’s Answer
Hi Cassandra,
That’s a fantastic question to be asking before you even start your first semester! Planning this far ahead will put you in a great position for a successful career. Your planned major in Statistics with a minor in Computer Science or Public Policy is the perfect foundation for working in healthcare data analytics.
Here are a few things I wish someone had told me before I started my journey in data analytics.
Your initial plan is spot on. The combination of Statistics and Computer Science is the technical backbone of data science and machine learning. Statistics teaches you the "why"—the theory behind the models and how to avoid common fallacies. Computer Science gives you the "how"—the ability to handle large datasets and write efficient code. Adding Public Policy is a brilliant move, especially for your interest in healthcare. It provides context and teaches you about the regulations, ethics, and societal impact of your work, which is incredibly valuable.
The field of data and AI is moving incredibly fast. What you learn in your classes is the foundation, but your ability to learn on your own is what will keep you relevant. Start a GitHub account on day one and put even your small class projects there. It becomes a living resume that shows your progress. Participate in Kaggle competitions to apply your skills to real-world problems. This is far more compelling to employers than just a list of courses. You should also follow data leaders on social media, listen to data-focused podcasts, and subscribe to a few newsletters. This will help you understand the trends of the industry long before you apply for your first internship.
The single most underrated skill in data analytics is communication. Your technical skills get you the data, but your communication skills get that data to influence decisions. You can have the most brilliant analysis in the world, but if you can't explain it clearly to a non-technical audience, like a doctor or a hospital administrator, it's useless. Think of yourself as a translator, converting the complex language of data into the simple language of actionable insights. Taking a public speaking or writing class will pay off immensely.
By focusing on this blend of technical depth, practical application, and effective communication, you'll be incredibly well-prepared for a rewarding career in healthcare analytics. Good luck with your first semester!
That’s a fantastic question to be asking before you even start your first semester! Planning this far ahead will put you in a great position for a successful career. Your planned major in Statistics with a minor in Computer Science or Public Policy is the perfect foundation for working in healthcare data analytics.
Here are a few things I wish someone had told me before I started my journey in data analytics.
Your initial plan is spot on. The combination of Statistics and Computer Science is the technical backbone of data science and machine learning. Statistics teaches you the "why"—the theory behind the models and how to avoid common fallacies. Computer Science gives you the "how"—the ability to handle large datasets and write efficient code. Adding Public Policy is a brilliant move, especially for your interest in healthcare. It provides context and teaches you about the regulations, ethics, and societal impact of your work, which is incredibly valuable.
The field of data and AI is moving incredibly fast. What you learn in your classes is the foundation, but your ability to learn on your own is what will keep you relevant. Start a GitHub account on day one and put even your small class projects there. It becomes a living resume that shows your progress. Participate in Kaggle competitions to apply your skills to real-world problems. This is far more compelling to employers than just a list of courses. You should also follow data leaders on social media, listen to data-focused podcasts, and subscribe to a few newsletters. This will help you understand the trends of the industry long before you apply for your first internship.
The single most underrated skill in data analytics is communication. Your technical skills get you the data, but your communication skills get that data to influence decisions. You can have the most brilliant analysis in the world, but if you can't explain it clearly to a non-technical audience, like a doctor or a hospital administrator, it's useless. Think of yourself as a translator, converting the complex language of data into the simple language of actionable insights. Taking a public speaking or writing class will pay off immensely.
By focusing on this blend of technical depth, practical application, and effective communication, you'll be incredibly well-prepared for a rewarding career in healthcare analytics. Good luck with your first semester!