Breaking Into Data Science in 2025? Here are 7 Things You’re Probably Asking Yourself
Insights from coaching over 60 aspiring data scientists
In this article, I’ve compiled the most frequently asked questions I’ve gotten as a Data Science coach over the past year.
I wanted to share this to help bring you more career clarity as you prepare to break into data science in 2025.
Here are the questions I’ll answer (plus a bonus at the end):
Do you need a master’s degree to land a Data Science job?
Should I learn Python even if I already know R?
How many projects should I put on my portfolio?
How much machine learning do data scientists do?
Do I need to learn LLMs to be a Data Scientist in 2025?
Should I learn Tableau or PowerBI?
How much statistics should I know?
Let’s get started!
1. Do you need a master’s degree to land a Data Science job?
Absolutely not
But it certainly helps….
There are 2 big career benefits to having a graduate-level degree in Data Science, statistics, or another relevant technical field:
Fast-track entry: It increases your chances of landing your first entry-level Data Science role straight out of University.
More technical roles: Later in your career, it will help you qualify for more technical roles that require the research skills you get through a higher degree, such as an MS or PhD.
I’ve witnessed this firsthand. Companies I've worked for have hired graduates straight out of University into an entry-level Data Science role.
So even though you can break into Data Science with just a boot camp or a series of online courses and certificates, a traditional degree is still highly valued in this career.
💡 Remember, there isn’t one ideal route, and whether you go for a boot camp or a university degree depends on your background and your specific career goals.
I wrote an article on the topic of making strategic career moves to your first Data Science job👇
2. Should I learn Python even if I already know R?
Absolutely yes, and here's why:
💡 First of all, don’t mistake these two as "rival" languages, think of them as complementary tools instead, and believe me, as a Data Scientist you'll need many in your toolbox.
And although they both have a place in your toolkit, they serve slightly different purposes:
R excels in statistical analysis and complex visualizations.
Python is the language that ties everything together in Data Science. It is great for data manipulation, analysis, machine learning, automation, and more.
And with 7 years working in Tech, I can tell you most Data Scientists rely on Python when it comes to advanced analytics.
So here is what I recommend:
Know R already? Learn Python to unlock more opportunities and broaden your skillset for advanced analytics and machine learning.
Don’t know where to start? Start with Python, as it’s the most versatile language in data science and you can consider learning R later.
3. How many projects should I put on my portfolio?
If you are trying to break into the job market without relevant work experience, the honest answer is as many projects as you can build.
However, building a competitive Data Science portfolio takes time and hard work, so I recommend you have at least four strong projects that cover the core technical competencies of data science.
I wrote an entire article on this topic 👇
But let me give you an overview of technical competencies you should cover in your portfolio:
Data Visualization: Build a dashboard with a BI tool (e.g., Tableau, Power BI) that delivers clear, actionable insights and demonstrates your ability to communicate insights effectively.
Exploratory Data Analysis (EDA): Analyze data to uncover trends, share actionable insights, and document your process clearly in a well-structured notebook.
Data Engineering: Create a project involving web scraping, APIs, or large datasets to showcase your ability to automate data collection and preprocessing.
Advanced Analytics: Solve a complex problem using techniques like predictive modeling, A/B testing, or clustering, and connect insights to practical applications.
4. How much machine learning do data scientists do?
“You’ll spend all day building machine learning models”
This is the biggest misconception I’ve heard about Data Science.
In reality, many data scientists rarely touch ML and instead, spend their time:
Cleaning data and building data models
Building dashboards or running experiments
Communicating insights to non-technical teams
Using statistical techniques to analyze data and test hypotheses
💡In the end, your job as a data scientist is to drive business value, no matter which techniques you use to get there, but most business problems out there don’t require machine learning solutions.
Real quick, help me bring you more value 👇
5. Do I need to learn LLMs to be a Data Scientist in 2025?
The short answer is Yes—but let me explain.
The rise of GenAI is reshaping data science, and Large Language Models (LLMs) are at the center of it all. Companies are looking for data scientists who can leverage these tools to drive efficiency, extract insights, and build smarter solutions.
I wrote an article analyzing the trends for the coming year 👇
Here’s why LLMs (and GenAI skills) are critical:
Growing expectations for efficiency: LLMs automate tasks like querying data, summarizing reports, and even debugging code, allowing data scientists to deliver more value in less time.
Practical applications beyond chatbots: From building domain-specific tools to analyzing unstructured text, LLMs are solving real-world business challenges that require GenAI expertise.
High demand for NLP and GenAI skills: Businesses across industries are actively hiring data scientists who can leverage LLMs and other GenAI technologies. Adding this to your skillset positions you for better opportunities and future-proofs your career.
💡 You don’t need to be an LLM expert or start fine-tuning models tomorrow. However, getting familiar with tools like Hugging Face or OpenAI’s API will ensure you’re prepared for the increasing demand for GenAI skills.
Check out this article for resources on how to get started with LLMs 👇
6. Should I learn Tableau or PowerBI?
It depends on your goals, but here’s the short answer: learn the BI tool your target companies are using.
Let me explain…
Tailor to the job market:
If the companies you’re applying to use Power BI, prioritize that. If they use Tableau, focus there. Employers care more about you being adaptable than about the specific tool you know.If you’re unsure, go with Tableau:
Tableau has a free public version and is widely popular, making it a great choice for building a portfolio. It’s highly in demand and intuitive, which means less time learning and more time showcasing your skills.Don’t learn both right away:
Becoming proficient in one tool is better than being average at two. Most BI tools have similar core concepts, so picking up a new one later won’t be as hard.
💡 The key is to show that you can create impactful dashboards and tell stories with data, regardless of the platform. The specific tool is secondary to your ability to drive insights.
7. How much statistics should I know?
You need enough to build a solid foundation because statistics is the backbone of data science, and skipping it will catch up to you sooner or later.
Here are some guidelines:
Master the foundations: Focus on descriptive statistics, probability, hypothesis testing, and regression—core skills you’ll use often.
Learn what you’ll use: For ML or experimentation roles, dive into A/B testing and Bayesian stats; otherwise, stick to practical knowledge.
Don’t memorize, understand: Focus on interpreting results and explaining them effectively rather than memorizing formulas.
Practice over perfection: Apply statistics in real projects to make concepts stick and demonstrate value to employers.
💡 You don’t need a Master's in Statistics to succeed as a Data Scientist, just enough knowledge to confidently back up your insights.
Do you have a question? 🤔
I was planning to add a few bonus questions but I thought why not just let you ask your questions about breaking into data science in 2025.
Leave your questions in the comments!
Thank you for reading! I hope you found this article insightful.
See you next week!
- Andres
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