How Physics and Data Science are Actually Similar

I’ve realized that physics and data science aren’t so different after all. In fact, there are striking similarities that drew me to both fields.

For starters, both physics and data science are fundamentally about understanding patterns and structures in the data we observe, whether it’s from a laboratory experiment or a vast database. At their core, each discipline relies heavily on the use of mathematical models to make sense of complex systems and predict future behaviors.

What’s more, the skill set required in physics — analytical thinking, problem-solving, strong grasp of mathematical concepts, and others — is also essential in data science. These are the tools that help us explore the unknown, whether it’s the mysteries of the universe or hidden insights in big data.

Another parallel lies in the methodological approach both physicists and data scientists employ. We start with a hypothesis or a theory, use data to test our assumptions, and refine our models based on the outcomes. This iterative process is as much a part of physics as it is of machine learning.

Moreover, the transition from physics to data science felt natural because both fields share a common goal: to explain the world around us in a quantifiable way. While physics might deal more with theoretical concepts of space and time, data science applies similar concepts to more tangible, everyday problems, making the abstract more accessible and applicable.

Do you see other parallels between your field and data science that could be valuable? I’d love to hear your thoughts.

Why Physicists Excel in Data Science

As I’ve navigated my path from physics to data science, I’ve encountered many moments of synergy that highlight how a background in physics is not just relevant but a powerful advantage in the data science field.

Both fields rely heavily on the ability to formulate hypotheses, design experiments (or models), and draw conclusions from data.

Furthermore, physics often involves dealing with massive datasets generated by experiments or simulations, necessitating skills in data handling, analysis, and computational techniques.

So, if you are studying or studied physics, you are on a great path to transition to data science.

Moreover, the quantitative skills that are natural to physicists — such as calculus, linear algebra, and statistical analysis — are foundational in data science. Whether it’s creating algorithms for machine learning models or analyzing trends in big data, the mathematical proficiency gained through physics studies is indispensable.

But in my opinion, I see that the biggest advantage is not even the heavy math you learn, the statistical courses you take or the programming language that you started to learn early on in the course. Studying physics cultivates a problem-solving mindset that is quite unique and not commonly found in many other disciplines, including other scientific fields. This ability to approach and unravel complex problems is invaluable, particularly in data science, where analytical and innovative solutions are crucial.

Physicists are trained to tackle some of the most abstract and challenging problems, from quantum mechanics to relativity. This ability to navigate complex and ambiguous problem spaces is incredibly valuable in data science, where answers are not always clear-cut and the ability to think outside the box is often needed to find innovative solutions.

Last but not least, the curiosity that drives physicists — a desire to explore and understand unknown territories — aligns perfectly with the objectives of data science. Both fields thrive on discovery and the extraction of meaningful insights from data, whether it’s understanding the universe at a macro scale or predicting consumer behavior from sales data.

How to start the transition to Data Science

Define your Goals

Naturally, everything comes down to your personal goals. It’s essential to start by clearly defining what you aim to achieve. Ask yourself some critical questions to guide your journey.

Do you have a specific field within data science you’re drawn to? Are you looking to specialize strictly in data science, or are you open to exploring related roles such as machine learning engineer, data analyst, or data engineer?

I mention this because many people initially set out to study data science, but often find themselves transitioning into related fields such as data engineering, machine learning engineering, or data analysis. This is a normal part of the journey, as it’s common for people to explore and discover what they truly enjoy doing, which may lead them to switch to a similar area.

Research which skills are the most crucial for you to acquire first (more on that in the next sections).

Additionally, set clear timelines for yourself — when do you hope to secure your first internship or land that exciting first junior position?

Define your Strategy

With clear goals set, crafting a strategic plan becomes the next essential step.

“A goal without a plan is just a wish.”

— Antoine de Saint-Exupéry

What skills are you going to learn first? And how are you going to learn them?

After deciding what field you would like to transition to (data science, data analysis, data engineering, machine learning engineering), you can start researching about the skills that you need to learn to succeed.

For example, roles in data science often focus more on Python and machine learning, though this isn’t a strict rule and can vary. Conversely, data analysis positions usually focus more on SQL and R.

My personal tip? I used to browse job listings on LinkedIn and other platforms to stay informed about which skills were in high demand.

Curiously, I’ve observed significant changes even within the span of two years. For instance, there’s currently a growing demand for AI and Machine Learning Operations (MLOps) skills, which aligns with the ongoing surge in AI interest.

But before you have a panic attack while checking the immense skill lists that most job opening roles post, let me offer some reassurance:

• First, you don’t need to master every skill, tool, framework, platform, or model listed.
• And even if you are skilled in all these areas, you don’t need to be an expert in all of them. For less senior roles, having enough knowledge to effectively complete tasks is often sufficient. Often, companies value adaptability, a willingness to learn, and reliability more than expertise in every tool or programming language. Soft skills and the ability to grow within a role can be just as important as technical skills.

If you come from a physics background, chances are you’re already well-equipped with solid math and statistical skills, and maybe some programming skills as well.

Reflecting on my own experience, the physics course I undertook was quite rigorous. I grappled with some of the university’s most challenging math courses and delved deep into every course available on probability and statistics. Although it was somewhat painful at the time (studying all that hardcore math), looking back, I am profoundly thankful for that intense mathematical and statistical training.

But, if those areas were not covered extensively in your physics course, you may want to revisit them.

Once you’ve solidified your base knowledge, a practical next step is to explore job postings for roles you’re interested in and take note of the required skills.

That’s why it is important to have a strategy.

Be critical about what skills to prioritize based on the logical progression of learning. For instance, you wouldn’t dive into learning Machine learning Operations (MLOps) without first understanding the basics of machine learning, right? This step-by-step approach ensures you build a strong foundation before tackling more advanced topics.

If you are in need of a roadmap, I recommend this cool website. You can also drop me a message regarding this 😉.

For example, this roadmap is about AI and Data Science in 2024.

How can you start Learning?

In my case, I started learning during my master’s program. If you just finished your bachelor’s you might consider pursuing a master’s or postgraduate diploma in data science. For those who already hold a master’s degree, a postgraduate program could also be a viable option.

Besides taking courses in universities, many (most?) people in the data science field are largely self-taught, acquiring their skills through online courses, participating in online challenges, projects, or bootcamps. And honestly, self-taughting is something you will need to to for rest of your life if you want to be in data science field!

Data scientists are continually learning new skills, tools, frameworks, and models — it’s an integral part of the profession.

That’s why adaptability is so crucial in this field, a skill that studying physics may have already helped you develop 😉.

Let’s say you want to start learning online. How can you achieve this? It is pretty straightforward. Nowadays, there are numerous platforms offering courses in data science and machine learning. DataCamp, Coursera, Udemy, edX and Khan Academy are among the most well-known. Youtube also offers a lot of content to learn data science and machine learning.

Personally, I’ve utilized both Udemy and Coursera, but DataCamp is particularly effective for acquiring more practical, hands-on skills.

In addition to taking courses, participating in online challenges is a great way to build your skills and enhance your portfolio. One platform I’ve personally used is FruitPunchAI, a startup that hosts AI challenges. One other similar platform is Omdena.

These challenges not only allow you to make a real impact in the world but also help you showcase your abilities. DataCamp also hosts competitions, along with other well-known platforms like Kaggle.

There are numerous options available, so take some time to explore and find the ones that suit your interests and career goals best.

How can you Showcase your Experience?

Speaking of portfolio, after you have acquired some solid skills and after participating in some challenges, it’s time to show them to the world.

Self-promotion is crucial for your career growth. It’s about strategically positioning yourself in the job market.

Building a strong portfolio is essential as it not only demonstrates your technical capabilities but also highlights your problem-solving skills.

An impactful way to start your professional self to the world is by building a robust LinkedIn profile. LinkedIn is not just a networking platform; it’s a global stage for professional storytelling. Here, you can highlight your educational background, project experiences, and the unique skills you bring to the table.

In addition to LinkedIn, GitHub is an invaluable platform for data scientists and tech professionals. It allows you to host and review code, manage projects, and build software alongside millions of other developers.

I started uploading my projects on GitHub as soon as I started working on them.

For a data scientist, GitHub can be used to showcase your coding skills, collaborate on projects, and manage the documentation of your work. By actively maintaining a GitHub repository with your projects, you demonstrate your practical expertise and ongoing commitment to learning and development in your field.

You can also opt to present your work on a website, instead of GitHub (usually happens when you have more experience).

Finding a job

Let’s say you have started learning skills, maybe you have completed one or two relevant projects and you have polished your LinkedIn profile. Now it’s time to get a job!

Make a Great CV and Cover Letter

Crafting a great CV and cover letter customized to each position you’re applying for is crucial. This requires doing a bit of research to understand what exactly makes a standout CV and cover letter for each specific role.

Take the time to look through job descriptions and company profiles to get insights into the skills and experiences employers value most. Ensure your CV showcases your relevant experiences, and your cover letter clearly explains why you’re the ideal candidate for the position.

Personalizing your application not only shows your interest in the role but also demonstrates that you have taken the effort to align with the employer’s needs. Don’t forget to include links to your LinkedIn and GitHub profile in the CV 😉.

Internships

To start my journey in data science, I performed two different internships, in two different countries abroad. Besides enriching my resume, going abroad also showed potential employers my ability to adapt to new environments and my willingness to step out of my comfort zone. Furthermore, being abroad is a great way to showcase your English language skills (if it is not your native language).

Personally, I looked for internships on LinkedIn, but there are many other platforms for job hunting.

I would also advise to apply directly on the company’s website if the position is listed there, as this can sometimes increase the visibility of your application to the hiring team.

After applying to several positions, I received responses from one or two, went through the interview process, and ultimately secured a position.

Typically, internships don’t involve deep technical interviews; however, they can be part of the process, especially if you’re applying to highly competitive companies, such as major firms like Google or Spotify).

In the interviews, they asked me about what I had learned during university and also asked me to present one or two projects I had worked on, among other questions.

This really shows why it’s so important to have a solid portfolio. Being able to effectively present and explain your projects is crucial, as they often ask why you chose a particular model over another, and how you approached specific challenges within your projects.

I also presented them with projects I had performed during the master’s degree, more specifically in the machine learning course. I also mentioned my master’s thesis, which involved coding and hardcore problem-solving. Plus, I mentioned the online challenges I participated in. Everything counts! On the interviews of the second internship, I showcased the projects I had done during the first internship. This is how you can build a foundation and grow step-by-step in your data science career!

Note: You can also showcase physics’ projects you have performed during your course. Especially if they involve a lot of programming, math, statistics, and especially, problem-solving and critical thinking skills, which I am sure they required!

In my own experience, I feel that having engaged in activities outside the academic world also help me land the internships. For instance, I had given private lessons to high-school students for 3 years and I made sure to highlight that experience as well. Additionally, I volunteered with various organizations, which is always a valuable mention in your CV and cover letter.

You might start with little to show, but as soon as you can showcase a project or a challenge, you can leverage it to build more content for your resume. From there, your experiences and achievements begin to compound, giving you increasingly more to offer potential employers.

Instead of opting for an internship, some might prefer to jump directly into junior or mid-level positions, especially if they already possess work experience in other fields. That’s a perfectly valid path as well!

Are you aware which work environment suits you best? Are you more productive in startups, corporate settings, or perhaps academic environments? Understanding this can also guide you in targeting the right organizations during your job search. It’s also perfectly okay to be open to a variety of options, as each can offer unique advantages and opportunities for growth.

Final Takes and Tips 💡

• There’s a high regard in the job market for those with a physics background. The strong skills in math and statistics, plus the exceptional problem-solving abilities, make physicists highly valued across various industries. Physics graduates are often seen as adaptable and analytically adept, marking them as top-tier candidates in any field!
• If you’re studying physics, you’re inherently a problem solver, naturally adaptable, curious, and resilient. Even if you don’t see yourself as having these qualities, trust me, you do.
• When crafting your cover letter and CV, be sure to highlight your qualities as a problem solver, adaptable, curious, and resilient. Emphasize that your training as a physics student or graduate has honed these characteristics. Be proud of your physics background — explicitly state that it is a significant advantage in your career pursuits.
• Remember, entirely normal and beneficial for the field of data science to be populated by professionals from a variety of backgrounds.
• Focus on quality over quantity: when building your portfolio, prioritize engaging in relevant projects and online challenges that also align with your career goals.

It’s a journey. Transition won’t happen overnight or in one month. Make sure to enjoy the journey as well. Stay curious and enjoy learning.

Remember, even those who have been in the data science field for years continue to learn constantly. Eventually, you’ll reach a point where you feel confident in your skills and knowledge, and you’ll be in a position to share your expertise with others.

I’m sure there are many more tips not covered in this article. If you have any specific question, drop me a message, I would be willing to help.

Also, if you need assistance in building your online presence, deciding which projects to dedicate time to, or if you’re looking for a roadmap for your transition, don’t hesitate to send me a message on LinkedIn.

Is there any specific topic in this post you would like me to explore deeper? If so, let me know in the comments!

Did you transition from Physics or other areas to Data Science? What were your main challenges? Share it in the comments!

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My name is Sara Nóbrega and I am a Data Scientist with a background in Physics and Astrophysics. I’m an enthusiast on AI, MLOps, Smart Cities, Sustainability, Cosmology and Human Rights.