date icon November 21, 2023
Time icon 9 MIN READ

How to get into Data Science: 5 Routes to Break into the Industry

CEO & Founder at CodeOp

Interested in a career in data science but don’t know how to get into it? Our guide outlines five different routes you can take to break into the industry and start your journey in this field.

turned on black and grey laptop computer

Photo by Lukas Blazek on Unsplash


‘How to get into data science’ is a question we’re being asked a lot now. Why? Because data science is a rapidly growing field with a high demand for professionals who can help businesses make decisions using their data. Despite shifting economic conditions and recent layoffs by the biggest names in tech like Google, Amazon, Microsoft and Meta, a career in data science still is very much a possibility. According to the US Bureau of Labour, ‘Employment of data scientists is projected to grow 36 percent from 2021 to 2031, much faster than the average for all occupations. Similarly, a LinkedIn 2022 study on the most in-demand jobs in the UK showed that data science is one of the fastest-growing and in-demand job titles in the UK.  In other words, there’s no better time to upskill in Data Science!

So how to get into data science? A good starting point is to look at the qualifications and skill requirements shared in the job descriptions of data scientist jobs. There are a few key skills you’ll need to develop if you want to get into data science. These include:

  • proficiency in programming languages such as Python and R, 
  • a strong foundation in statistics and mathematics, and 
  • the ability to work with large datasets and analyse complex information. 

Based on your current qualifications and skill levels in the above areas, there are several paths you can take to get started. And don’t worry – there’s definitely an option for everyone. From getting a degree in the industry to taking online courses and building a portfolio, our comprehensive guide outlines five different ways to get a job in data science and start your journey towards becoming a data scientist.


Here’s a quick breakdown of what we’ll cover: 


Pursue a formal education in data science

One of the most traditional routes to get into the data science industry is formal education in the field itself. This can include getting a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field. These degrees give you a strong foundation in statistics and mathematics which are crucial to build a successful career in data science. Many universities now offer specialised data science programmes such as the graduate programmes in Harvard University, Oxford University and University of Amsterdam. Some even offer online options if you need more flexibility. 

Getting into data science with a degree  can be a good way to develop a strong grounding in statistics, probability, and mathematical analysis. A 2023 study by 365datascience.com highlighted the best data science degrees in 2022. It used the background information of current data scientists on LinkedIn. This study showed that 91% of the data scientists worldwide have graduated from a university or other higher education institution. It also showed that 12% of the data scientists completed an undergraduate degree only. So don’t be put misled into thinking that you definitely need a Masters degree to get into data science. 

The most common degrees among employed data scientists include Computer Science, Statistics, Mathematics, and Engineering. But it’s also worth bearing in mind that there are also many successful data scientists who come from non-traditional backgrounds, such as social sciences or humanities. 

In fact, many of CodeOp’s graduates such as Alonna Williams, Oriana Medlicott and Christine Shearer started their journeys in Data Science with a non-STEM background.

Statistics involves the collection, analysis, interpretation, and presentation of data, while probability is all about understanding the likelihood of different events occurring. Mathematical analysis, on the other hand, deals with concepts like limits, differentiation, integration, and analytic functions. By mastering these core concepts, you’ll be well on your way to understanding the theoretical aspects of data science.


Participate in online courses and bootcamps

Sure, a STEM degree is the usual route to getting into data science. But now there are other completely feasible options to get into data science. Perhaps full-time education is not your thing or maybe you consider it to be too expensive (yes, university course fees can be significant!). Either way, you shouldn’t give up on your dream to make a career change and become a data scientist. So how to switch to a data science career? Participating in online courses and bootcamps are great options. These programs offer a more flexible and affordable option if you do not have the time, resources or desire for full-time education.

Online courses and bootcamps can give you hands-on experience with data science tools and techniques, as well as opportunities to meet industry professionals. Entering the field of data science requires a combination of theoretical knowledge and practical skills. Bootcamps are a particularly great way to learn the practical skills needed to become a data scientist alongside building the fundamental theoretical knowledge to apply those skills. These skills include programming languages like Python and R, as well as machine learning algorithms, data analysis and visualisation techniques. 

Some popular online course providers include Coursera, Udemy, and edX. They offer popular courses taught by university professors with the option to get a credential if you pass the course requirements. Universities have also started to offer their own online courses which are shorter than full formal degrees and less expensive. For example, the MiTx Micromasters programme in Statistics and Data Science teaches both theoretical and practical aspects of data science. The bootcamp format is growing ever more popular now.

Our very own data science bootcamp has led the way with this new format of learning which offers more intensive and immersive learning experiences, and you can get some hands-on practice with our free data science masterclass.

Build a strong portfolio of data science projects

Once you get into data science with a degree or a bootcamp, you’ve taken your first step towards foundational knowledge.The next step is to get a strong project portfolio ready, so you can get a job in data science. This can demonstrate your skills and expertise to potential employers or clients. 

The good news is this requires only your time and interest to get started: 

1. Immerse yourself in all things data science 

You can do this by reading articles and books written by experienced data scientists. This will help you understand the language and thought processes used in the industry, as well as the current trends and challenges. Some good books that you can pick up are An Introduction to Statistical Learning: With Applications in R and Doing Data Science. If you prefer to read articles, then Medium has a host of authors sharing their insights on various topics of Data Science such as Cassie Kozyrkov and Susan Li

2. Choose a project

Identify a problem or question that interests you and gather relevant data. Getting good raw data to work on is difficult and is usually a stumbling block for many at the beginning of building a portfolio. But there have been many others who were in your shoes before and this is where the benefit of the data science community comes in. Kaggle provides a great way to be part of the data science community. It has a rich source of datasets which have been put together by others on this platform. These are usually in a good shape to start working on as they are cleaned already to some extent.

3. Work on the data

Then, start using your data science skills to analyse and interpret the data, and present your findings in a clear and compelling way. 

There are a lot of data science projects out there that you can take inspiration from. These can help you to understand:

  • What kind of topics are good to work on, 
  • How to work through a question, 
  • How to present your findings to an audience or 
  • Ways to write the right code to solve a problem 

Just remember to give due credit wherever applicable. Once you are happy with your project, you can share them on platforms like GitHub or Kaggle, or even create your own website or write a blog on Medium to showcase your work.  You can then add your profiles on these platforms in your LinkedIn profile for potential recruiters to see all the hard work you’ve put into your portfolio!


Attend industry events and network with professionals

OK, so firstly let’s tackle the elephant in the room: networking. It doesn’t have to be daunting or intimidating. We promise! Here are a few things that you can do to make it easier-

  • Identify why you want to network. What do you want to get out of it? This will help you work your way through the networking process more confidently.
  • Do your prep before the event.Think about the way you want to present yourself to others such as your introduction. If you feel like you belong, you will be so much more comfortable interacting with the other participants.
  • Find someone from your friend circle who might be interested in the same topic and bring them along to the event. It’s always nice to have someone you know to avoid feeling intimidated.

Attending industry events and networking with professionals is definitely a great way to learn about trending data science topics. It’s also a good way to understand about different job profiles that companies are looking to hire for within the Data Science industry. 

The field of data science is continuously evolving. You should be able to easily understand the  general requirements of a data scientist from an online job posting. You can also use these job descriptions to understand company specific expectations of these roles. You’ll  see many different job titles during your research – such as Data Engineer, Machine Learning Engineer and Decision Scientist among others. In these instances, it’s useful to find out first hand about where the role sits within the organisation and what the day-to-day tasks are, from people in the data science or the hiring team. 

Even if you’re not actively looking for a job, networking is a good way to learn about what it takes to land a job and be successful as a data scientist. You can identify the gaps in your current skill set and focus your time and energy to fill them.

Thanks to social media and online events, it’s easier than ever to connect with professionals and other students in the industry. Instead of solely focusing on networking for job applications, it’s a good idea to shift your mindset to becoming a valuable part of the data science community. By immersing yourself in the industry and making connections, job opportunities will naturally follow. Look for conferences, meetups, and other events in your area or online where you can connect with other data scientists and learn about the latest trends and technologies in the field. You can attend events by organisations like the International Association for Data Science, Turing Institute and Women who code to connect with other professionals and access resources and training opportunities.  


Consider internships or entry-level positions to gain experience

One of the best ways to break into the data science industry is to gain practical experience through internships or entry-level positions.These can be part-time, full-time or even voluntary positions (if you have the financial means). If you think you’d pass the minimum requirements for internships or entry-level positions, don’t hesitate to apply to open positions. 

We strongly encourage you to avoid self-rejecting even if you don’t tick all the boxes. Infact, a LinkedIn study on how gender impacts the candidate journey of finding a job found that in order to apply for a job, women feel they need to meet 100% of the criteria while men usually apply if the only meet about 60%.So remember that it’s completely fine if you only know how to code in Python and have never worked on R despite the job description asking for the candidate to know how to code in multiple programming languages. Let the hiring team decide if you are completely unsuitable for the job!

Many companies offer internships to students or recent graduates with the minimum educational qualifications, providing them with hands-on experience and exposure to real-world data science projects. Entry-level positions may also be available, such as data analyst or junior data scientist roles, for someone with little to no past experience as a data scientist. These positions allow you to gain experience and build your skills while working alongside experienced professionals in the field. You can use the connections made through your networking to get their guidance on how to make yourself stand out or even get their referral as part of the application process.

Look for job postings on LinkedIn, company websites, or through networking connections to find these opportunities. If you don’t see any open positions but are passionate to work for any particular company, reach out to the hiring team expressing your interest and asking for any opportunities available. Even if they don’t have any available now, it is a good way to stay connected and next time there is an open position, they might reach out to you! Apart from paid jobs, there are voluntary positions that you can apply to collaborate on data science projects (for example Omdena) or on online competitions (for example Drivendata) designed to use data science for social impact.

In the end all these routes have one thing in common – a passion to learn. With recent developments in artificial intelligence and growing use of data in businesses, data science is only going to grow in importance. The open, collaborative and supportive nature of the data science community ensures that if you’re keen to make a  career change into data science has ample available resources to help in their journey. 

As Martin Luther King Jr said, “You don’t have to see the whole staircase, just take the first step.”



What qualifications do you need to be a data scientist?

Answer: While there is no one set path to becoming a data scientist, most professionals in this field have at least a bachelor’s degree in a related field such as computer science, statistics, or mathematics. Additionally, many data scientists have advanced degrees such as a Masters or PhD. However, with recent changes, employers are more keen to look at skills rather than just formal qualifications. If you can showcase that you have analytical, critical thinking and programming skills in Python or R, and have a good foundation in statistics then you’re on the right path to getting into data science.

How can I get into data science with no experience?

Answer: There are several ways to break into data science without any prior experience. Some options include taking online courses or bootcamps, participating in data science competitions, networking with professionals in the field, and working on personal projects to build a portfolio. It’s also important to develop skills in programming languages like Python and R, as well as in statistical analysis and machine learning.

Is it hard to get into data science?

Answer: While it may require some effort and dedication, getting into data science is not necessarily hard. With the right mindset, education, and experience, anyone can become a successful data scientist. It’s important to start by learning the necessary skills and tools, building a portfolio, and networking with others in the industry.

Does data science require coding?

Answer: Yes, coding is an essential skill for data scientists. They need to be proficient in programming languages such as Python, R, and SQL to manipulate and analyse large datasets. However, the level of coding proficiency required may vary depending on the specific job and industry.

Author: Katrina Walker
CEO & Founder at CodeOp
Originally from the San Francisco Bay Area, I relocated to South Europe in 2016 to explore the growing tech scene from a data science perspective. After working as a data scientist in both the public...
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