How to Break Into Data Science?

As data continues to become more important across virtually all spheres of life, it is no surprise that there is a soaring demand for data scientists. Currently, there’s a shortage of qualified labor in the industry. Figures from job hubs like LinkedIn show that a data science career will remain one of the most promising in the coming years, so how can you break into the industry?

To break into data science, you need to look at your current background to understand the approach that will work best for you. Once you’ve charted a course of action that matches your background and followed it, focus on getting your foot in the door quickly and pushing forward.

This article will cover everything you need to know about starting your career as a data scientist, as well as everything you need to know about landing your first role.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

Background Analysis

When you’re thinking about getting into data science, you need first to analyze your background to figure out the best steps you should take. Most people will fall into the groups discussed below:

The STEM Switcher

You’re in this group if you already have an advanced academic degree in science or a technical field and already have some years of experience working in or around the field. Many people in this group are now making the transition to data science as the popularity of this domain continues to soar.

People in this group usually have a strong research and mathematics background, which means they can easily understand the statistics and linear algebra that make up the core of machine learning models. They also have lots of experience digesting academic papers and find formulas interesting instead of intimidating. These are skills that can be quickly transferred, helping them to become good data scientists.

The Fresh Data Science Graduate

In the last few years, more universities have started to offer data science MSc programs. Therefore, we have more people looking to break into data science fresh from completing their studies. Some of the departments that may offer data science programs today include electrical engineering, industrial engineering, and statistics.

The degrees won’t cover everything, but they tend to offer a foundation that most short term bootcamps won’t provide. The best of these programs include publications and thesis, which helps potential employers to evaluate a prospective candidate in more detail. 

Candidates have to understand the core of the thesis, show a grasp of alternative approaches, and discuss why they made different micro-decisions throughout the thesis. People in this group get more attention from recruiters in the market for beginners.

The Hopeful Switcher

People in this group haven’t completed any data science training, and they don’t have extensive statistics or math background. While some of them are entirely new to the science field, others may have a moderate analytics foundation coming from sectors like healthcare and finance. Members of this group often have their work cut-out for various reasons.

Those without any statistics or math background may find it harder to grasp concepts, while those with analytics experience in domains like finance get fixated on learning only aspects of data science relevant to them. People in this group will most likely take longer to break the glass and complete their data science move.

With the three possible background options squared off, it’s time to look at the important steps you need to take to make the switch. You should know which group best describes your background by now. This will determine what steps you should take from our list below and which ones you should discard.

We will now go over some of the proven steps you should take when the focus is to break into data science.

Shake Up Your Math Skills

As we’ve briefly mentioned above, you need math in data science. The exact amount you’ll need will come down to the job role, but you still need to have a good grasp of linear algebra, probability, statistics, and calculus. 

You don’t need to be an expert in these math fields or get a degree, to become a junior-level data scientist. You just need to know enough to intuitively solve basic problems that will arise over the course of your job. Don’t waste time trying to learn how to solve complex math problems by hand.

If you already have an advanced math degree, you’ll only need around three months to brush up your math skills and get it up to the level you’ll need to get started in data science. After you land a job, you can decide if it’s necessary to delve deeper into math.

Get a Masters Degree

You can get into data science with your BSc. However, since the industry is research-centric, an MSc from a reputable university can make you more prepared and open doors for you. As we touched on briefly above, more departments are now offered MSc programs in data science (or a combination of programs with data science as one side of the divide).

If you can’t find a complete data science MSc program, consider getting one in computer science or statistics. Such a program can take up to two years to complete, but it’s a good way to invest your time. Some employers will prioritize MSc holders when recruiting. Look for good universities around you and see what is available—bearing in mind that what qualifies as a good university will vary from place to place.

Learn the Right Programming Language

You need coding skills in data science. If you land a job in the industry without the right coding skills, you’ll find it hard to deliver on the job, even when you know what you have to do. You may also end up being one of those that have to rely on copying codes off sources like Stack Overflow.

When learning programming for data science, you should ideally start with Python and SQL. Virtually all data science jobs will require you to know these languages as they’ll form the core of the technical tests you’ll have to go through during interviews. Some employers will also expect to see some python code samples you’ve built on GitHub.

While there’s nothing wrong with seeking solutions online from time to time, you should know enough programming to solve basic problems on your own. If you’re completely new to coding, it’s easy to get overwhelmed at the start of your learning process. You can avoid this by starting small and reading a book on Python, SQL, and R. Once you’ve grasped an overview of the roles of these languages in data science, you can get more serious with syntax.

Don’t waste time trying to memorize everything. Grab the basics, and then make a note of the best places to look when you get stuck. If you’re one of those that have already read books on the languages we talked about or even completed a course, you probably understand the syntax already but don’t know how to approach data problems. Learning data structures and algorithms should be your next step.

On the other hand, if you have satisfactory programming skills, the next step is to analyze Pandas, Numpy, and other such libraries. How much time you’ll spend at this stage of the migration to data science will vary a great deal. Complete beginners may need to spend up to six months learning, while those that are only looking for library knowledge will need less than a couple of months.

Learn Databases

As a data scientist, most of the data you’ll analyze will come from a database. This is the main difference between a standard work environment and courses and books where you’ll always get a clean formatted CSV file. To work with databases, you need a good knowledge of SQL and some domain knowledge.

For analysis in programming languages such as R or Python, you don’t need to devote much time to learning SQL functions such as T-SQL or PL SQL, or other advanced functions. Your SQL work will revolve around combining tables to run your analysis. If you have the right programming language, you shouldn’t spend more than a month at this stage.

Get Into a Bootcamp

If you already have some expertise in the programming languages we mentioned and you have a relevant MSc, you may not think of bootcamps as a route into data science. However, these programs vary widely in terms of what they can offer you. While some of them only offer intensive training that can help you become a data scientist for 3-6 months, others will provide the training and also offer some kind of income sharing agreement.

In the latter scenarios, the bootcamp organizers can help you land your first job as a data scientist in return for a fraction of your salary above a designated threshold for a fixed number of years or until you pay off the cost of tuition. Some bootcamps may get you to sign an agreement to stay employed for two years with them or with a partner company. There’ll typically be an exit clause that covers the cost of tuition.

If you already have adequate knowledge of theoretical knowledge of machine learning, some coding skills, and a good handle on statistics/math, you can still use these bootcamps as a way to further tune your knowledge and give yourself a shot at landing a position—assuming you sign up for one that offers placement and revenue sharing.

For anyone just getting started, the bootcamps may also provide a condensed form of everything you need to learn in one place. You’ll have a clearer path to work with and a curriculum to follow instead of figuring things out on your own. The downside to it is that the timing may be too short to fully grasp principles. Individuals in our third background group above will likely find 3-6 months’ typical duration to be inadequate.

Register for an Online Course

If bootcamps are too intensive for you, you should consider enrolling in an online course. There are so many quality courses on data science these days, where top professionals from around the world will teach you. You should get started with the introductory courses that cover data science basics and then progress from there. You can go deeper into areas that align with your career goals.

Most of these courses can be completed in 100 hours or less with some effort, but the good thing is you don’t have to force anything. You can learn at your desired pace. A good tip is to be wary of courses that promise to teach you everything you need to know about Machine Learning to become a data scientist. Such courses simply don’t exist. 

They all qualify as introductory general knowledge at best. You won’t pass many data science interviews off the back of completing that course most of the time. Like bootcamps, the best data science online courses will have a curriculum you can follow easily. 

You can also choose to learn programming languages through these courses if you have a coding knowledge gap to fill. However, you should know that these courses don’t always come free of charge, and it’s rare to find any of them offering placements as you’d get with bootcamps.

Start Working on Side Projects

When you have enough skills to land an entry-level data science position, it’s time to start showing hiring managers what you can do. Grab some disjointed data and do the work required to add it to your portfolio. This is the kind of work you’ll be doing at the entry-level, so you’d want to show prospective employers that you’re perfectly comfortable with handling datasets.

Network Extensively

Getting into data science may still prove difficult even after you amassed all the necessary skills for a junior role. This can leave you frustrated, especially when you keep reading about how there’s a shortage of data scientists. 

However, you need to keep in mind that recruiters are always looking for the best, even at the entry-level. If a Ph.D. holder applies for the same position as you with only an MSc, you’ll probably get passed over for the doctor.

This is why you should spend a lot of time networking. Go to meet-ups and conferences as much as you can. Talk to people in the industry on social media. Your first data science job will most likely come from one of the people you reached out to directly, who is willing to give a newcomer a chance to grow.

Look for a Data Analyst Job in Your Domain

Instead of spending months waiting for that perfect data science job, you should consider joining a company in your domain with a data analyst position open. You’ll gain some useful knowledge, learn some stakeholder management skills, and learn business reporting, which will help you a great deal when you finally land a data scientist role.

Take Up an Internship

An internship—especially an unpaid one—may be unattractive, but it’s an excellent route to break into data science. Instead of searching for only full-time roles, you can use one of these as a stepping stone to either flesh out your resume and gain more real-world data science experience or climb the ladder within the company.

Similarly, don’t turn down offers from analytics companies. A business analyst role, for example, is a good opportunity to get close to a data science job. Take it, get close to the data science team in the company, and wait for a chance to make the switch.

Other Top Tips to Land Your First Break Into a Data Science Role

Meet the Job Requirements

When you go over a data science job listing, make sure you have all the skills listed or at least come close. If they ask for specific skills and qualifications, you should ensure you have them, clearly highlighting each one in your resume and cover letter. 

We’ve touched on some of these above, but below are some examples of the qualifications and skills some recruiting managers will require for a data science position. Bear in mind that different companies will have different requirements.

An MSc or a Ph.D

Companies may require that you have at least one of these in fields such as statistics, applied mathematics, computer science, engineering, etc.

Quality Programming Skills

We have mentioned Python above as one of the most popular programming languages for data science, so you should expect to find proficiency in it as a part of the job requirements. 

In addition to Python, some recruiters may also ask for experience with all or some of C++, Java, R, and Hadoop. It’s also not uncommon to find jobs requesting knowledge of scripting languages such as Perl or PHP.

Advance Math Abilities

We talked about brushing up your math skills earlier, and it only takes your first few attempts at an application to understand why. The bulk of the statistical data analysis you’ll do in this field will involve applied optimization, sampling methods, stochastic models, multivariate analysis, linear models, Bayesian, and more. 

Therefore, most recruiters will expect you to show proficiency in linear algebra, calculus, and of course, statistics.

Data Management Skills

Your job as a data scientist, especially at the entry-level, will involve a lot of collecting and cleaning datasets, manipulation, and visualization. You should show proficiency in using SQL to manage the relationship between multiple data sets and using tools like Power BI and Tableau to present data in consumable forms.

Machine Learning Experience

This may not be a requirement for many junior-level roles, but you will see it in some intermediate-level job descriptions. As you work with big data, you’ll at some point need to use supervised and unsupervised algorithms to do your job. Job roles where experience in machine learning is a requirement will highlight it very clearly.

General Soft and Business Skills

As a data scientist, the bulk of your job will revolve around improving the user experience, growing revenues, and improving efficiency. Therefore, you need to show an understanding of the business process and also show excellent communication, teamwork, and critical thinking skills. You’ll also need to have strong presentation skills.

Develop a Strong Online Presence

Many recruiters have turned to online tools that will help them gather and weed through prospects. They also rely on some of these tools to evaluate candidates. To improve your chances of getting your first data science job online, you need to make it easier for recruiters to find you and evaluate your skillset even before the first interview. In this area, here are a few things you can do:

Keep Your Website and Online Portfolios Up to Date

Ensure they contain all your relevant qualifications, ticking off as many of the keywords recruiters in the industry are likely to be searching for. Leave a good first impression with your resume, listing out your experience, the technical skills you deployed, and your impact on the job. You should also make sure a link to your Github portfolio is visible.

Update and Brush Up Your LinkedIn Profile

Many recruiters rely on LinkedIn to search and find candidates that may fit the job description. You should ensure your profile is fully optimized. Use a professional image, and ensure your profile summary is SEO optimized. 

This way, your profile will come up in searches. You may be wondering why you need a professional image for your LinkedIn. A recent study highlighted the fact that recruiters spend 20% of their time looking at profile photos. You need to make a good first impression.

Become Active on Social Media

Ideally, you should spend more time on platforms where your knowledge and skills will shine through and be seen by many people in the industry. So, think more of Reddit and Twitter than Facebook and Instagram.

Prepare Adequately for Your Interview

Even when you have all the right skills on paper, you still need to convince recruiters that you are a good fit for the job in an interview. Go over some common interview questions. There are a ton of them on Google right now.

For complex problems, don’t hesitate to think out loud. This will help you better arrange your thoughts and also give the interviewer a peek into your thought process. Don’t forget to practice coding challenges that are likely to come up during the interview.

If the interview is a virtual one, you have to prepare adequately for that. Cover the basics like double-checking your internet, microphone, and camera, and also setting up in a quiet and properly-lit room. Adjust your calendar to reflect any difference in time zones if necessary.

Schedule a practice session with someone in data science or tech, in general, to ensure you are fully prepared. Your personality should shine through over the course of the interview session. You need to demonstrate competence while also convincing the recruiters that you’re a perfect fit for the role. Spend as much time as you need on developing the confidence you need to ace the interview.

Negotiate Properly

After an interview (or multiple interview rounds), the next step in your data science journey is to negotiate and close out your first offer. You have the leverage at this point, but you still need to avoid dropping the ball. Here are some things to keep in mind:

  • Pay attention to the entire package. You probably already know to look beyond the basic salary when evaluating offers, but it’s even more important in a post-pandemic world. What is the company culture like? Do they allow flexible hours? Can you work remotely? Are there additional benefits, like stock options or equity? Are you okay with the terms and conditions?
  • Use competing offers as leverage. If you have multiple offers, don’t hesitate to negotiate with the highest offer you’ve received as the benchmark. The bigger tech companies will try to match your highest offer. Startups will try to come very close.
  • Don’t feel bad about negotiating. Most employers don’t expect you to accept the first offer they present to you. So, you shouldn’t feel bad about negotiating a higher offer. If this is your first data science job, you’ll probably be more anxious to start and end up short-changing yourself. Here are more negotiating tips to help you. Go over them to ensure you’re properly armed for the back and forth.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


Breaking into data science is difficult, but it’s not as difficult as becoming a doctor or a commercial pilot. By understanding your background and taking the best route possible, you can make the journey a lot shorter.

For example, if you already have a higher degree in math or statistics and know some Python and SQL, a 3-6 months bootcamp may be all you need to get your data science career started. On the other hand, if you have no science background or coding skills, you may need a couple of years—depending on how fast you can grab concepts.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.


Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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