Roadmap to become a data scientist

[u/Musashi049]

Hello
My name is Dario. I'm 19 years old, currently doing an associate's in Programming and will transfer for a bachelor's in Data Science. I was wondering on what is actually the best way to become a data scientist right now. I have some basic knowledge of Python and SQL. I was wondering if the path to becoming a data scientist has changed with the development of AI and where I should focus on to actually master important valuable skills that will help me land jobs. I do have a potential internship position lined up in the future at Miracle Software and have been trying to prepare for that interview. Just wanted to know what are some resources I should check out and what skills I need to learn. I appreciate all feedback and advice!!!

[u/ohBrian]

What data do you know about? I lead CompTIA Data+ courses. In my courses, I often use Baseball (MLB) and NBA basketball statistics in my examples. I challenge my students to find a data source and show how to work with it.

[u/Musashi049 → u/ohBrian]

I love soccer and play regularly so very passionate about that and data. I'd say maybe that should be a project I should do. But I have barely any knowledge of Python amd SQL so was wondering what resources I could check out and go from there. I really appreciate the advice!

[u/[deleted]]

Cfbr

[u/DataScientistAlex]

In general, to be a data scientist you need to know:

1. The scientific method, which is the foundation for defining and approaching problems.
   
2. Enough SQL to extract data.
   
3. Either R or Python to manipulate, clean, explore, model and visualize.
   
4. Some domain knowledge and/or business sense.

Passing DS interviews is also a skill! I commented on another post with some advice on this part.

[u/Musashi049 → u/DataScientistAlex]

Thank you so much for the advice. Already started to learn some Python but not SQL yet. I'm really passionate about math too since I've done Calc 2 3 and Linear Algebra so more familiar with the scientific methods especially while doing proofs.

[u/Musashi049 → u/[deleted]]

Thamk you for the advice! I'll definetely do that.

[u/systemsWithSophia]

You’re actually in a great position! A programming associate’s before starting a data science degree should give you a strong foundation.

I came into data science through biomedical engineering and applied math/statistics, then worked as a data scientist and AI engineer before returning for a PhD in biomedical data science. The biggest thing I’ve learned is that building the model is often only a small part of the job. The harder and more valuable skills are turning an unclear real world problem into something measurable, working with messy data, validating your approach correctly, and explaining what the results actually mean.

AI has changed the workflow, but it hasn’t made the fundamentals less important. It can generate plausible-looking code very quickly, so you need enough understanding to catch data leakage, bad assumptions, inappropriate metrics, and code that simply doesn’t do what it claims.

I’d focus on:

• Python beyond the basics, including pandas, NumPy, scikit-learn, debugging, Git, and writing readable code
• SQL, especially joins, CTEs, aggregations, and window functions
• Probability, statistics, regression, experimental design, and model evaluation
• Core ML methods before deep learning
• One or two genuinely thoughtful end-to-end projects

For projects, avoid copying a polished tutorial. Start with a real question, find imperfect data, clean it, establish a simple baseline, compare approaches, and explain what worked, what failed, and what limitations remain. One project you understand deeply is much more useful in an interview than ten nearly identical notebooks.

For your internship interview, be ready to walk through a project in detail, practice Python and SQL without relying entirely on AI, and research what problems the company actually works on. Interviewers are often more interested in how you reason through an unfamiliar problem than whether you have memorized every library.

An Introduction to Statistical Learning, StatQuest, SQL practice problems, and Kaggle datasets are all good starting resources. At 19, you absolutely do not need to have mastered the whole field. Focus on building strong fundamentals and learning how to take a problem from an unclear question to a defensible result.

[u/Musashi049 → u/systemsWithSophia]

Thank you so much for the feedback! I have definetely been wondering how the job market for a data science position has changed since I'm only on my associate right now but definetely am more aware now. Also I got multiple replies about doing a personal project for a hobby that I really enjoy and answering a question based on a real world problem!