Updated on 8 Sept 2022

How to Become a Data Scientist with No Experience in 2022?

Aleksandra Yosifova Published on 7 Sept 2022 8 min read

So, you want to become a data scientist, but you have no prior experience? Employers, on the other hand, are only interested in candidates with working experience.

Most of us have been there, so don’t worry!

The good news is that there are ways to overcome this seemingly insurmountable challenge. Over the years, 365’s team has trained hundreds of thousands of people. We’ve heard many stories and we know what has worked for our students and what hasn’t.

In this article, we will share our secret formula on how to become a data scientist with no experience in 2022.

One of the aspects many individuals underestimate is having a clear roadmap in mind. So first, we’ll talk about the learning journey you need to embark on to become a data scientist. Then, we’ll focus on the things you can do to put the odds in your favor and land your first data science job.

Read on for a detailed breakdown of the process. You can also watch our video below for a more concise overview of the topic.

How to Become a Data Scientist with No Experience: Table of Contents

  1. What Qualifications Do You Need to Be a Data Scientist?
    1. The Non-Formal Education Path
  2. How to Get into Data Science with No Experience?
    1. Create a Compelling Resume
    2. Build a Killer Portfolio
    3. Create Industry-Specific Projects
    4. Networking
    5. Approach Employers
    6. Look For Entry-Level Data Scientist Jobs
    7. Consider Working Remotely
    8. Build Your Personal Brand
  3. Next Steps

What Qualifications Do You Need to Be a Data Scientist?

Let’s start with the learning journey. What steps do you need to take?       

Our research shows that 91% of data scientists have completed some form of higher education. The list of the most common degrees among employed professionals in the field goes as follows:

  • Data Science (21%)
  • Computer Science (18.3%)
  • Statistics or Maths (16.3%)
  • Economics and Social Sciences (12.3%)
  • Others

So, the most obvious answer to the question, “How to get into data science?” is “Get a degree.” If you’re at a point in your life when that is possible, we can recommend the best data science Bachelor’s degrees.

But if that’s not the case, you shouldn’t be discouraged by the numbers. The demand for data scientists is enormous. In fact, the US Bureau of Labor Statistics predicts that the search for big data and mathematical science specialists will increase by 27.9% from 2016 to 2026.

To examine the level of education among specialists, we conducted a study among 1,000 data scientists. Our results revealed that almost all of them have completed higher education.

However, the number of employed individuals with a bachelor's degree increased from 12% in 2020 to almost 20% in 2021! This shows that employers are starting to value skills more than the number of years spent in education.

That said, you do need both theoretical understanding and practical capabilities to succeed in this field. So, before you begin thinking about how to start a career in data science with no experience, make sure you have the required knowledge.

The Non-Formal Education Path

You don’t necessarily have to invest years and a small fortune in a university degree. Obtaining a certificate from an online course might suffice. If the curriculum is comprehensive, you can learn enough to prepare an impressive portfolio.

That said, the plethora of options available online can be overwhelming. Don’t worry, we can help with that too. We recommend you start by gaining a broader understanding of the data science field and how it adds value to businesses.

Here’s how to do this:

  • Study various data science terms and their application.
  • Find out why data plays such an important role in company management and profitability.
  • Determine how a company can be positioned in order to thrive in a world of data.

Other things you can learn when getting started in data science include:

  • What types of data analysis techniques are there?
  • How and why do we apply machine and deep learning algorithms?
  • and so on...

You might even want to take a few business courses like the one we offer on Data-Driven Growth. This will help you understand how a company creates value and what its current strategic positioning is within a given industry.

Once you’re past this initial stage, it’s time to learn the fundamentals.

Start with fields related to data science that will help you build a solid foundation, like Statistics, Mathematics, and Probability. This will allow you to understand technical topics later on. That way, you won’t simply apply existing frameworks blindly, but will also recognize their limitations and how they were constructed.

Of course, to become a data scientist, you will need to learn how to code. SQL and Python are the two most popular coding languages a data scientist needs. SQL allows you to work with structured databases, while Python gives you the ability to manipulate data and perform in-depth analysis.

Certainly, if you are a beginner in programming, you should learn the basics first. Then, apply what you’ve learned by working on simple projects. Check out our tutorial on the “4 essential Python projects for beginners” to find inspiration for entry-level projects.

At this point, you will be ready to tackle advanced topics, such as supervised and unsupervised machine learning algorithms, deep learning, and so on. Finally, to showcase those skills, you’ll need data visualization. So, don’t forget to work on this aspect too.

We offer a complete Data Science Career Track, which combines all these topics. It’s a great way to find all relevant subjects in one place and obtain a certificate at the end.

How to Get into Data Science with No Experience?

Right. You’ve gained the necessary advanced knowledge, but you still haven’t done any work to show for it.

So, how to become a data scientist with no experience?

The key to landing a job is being proactive and demonstrating enthusiasm for the position you’ve applied for. You can do this in a number of ways. Most importantly, you need to adopt a “can do” mentality!

Here are our suggestions on how to start a career in data science with no experience. But remember: the best ideas are the ones that come from you!

Create a Compelling Resume

To begin with, you need a resume to show your skills and the projects you’ve worked on. Go the extra mile and dedicate enough time to picking the right template.

And don’t just fill in your personal information. Personalize it and make sure it looks clean and professional! If you need more guidance, you can check out our comprehensive guide on building a resume.

Build a Killer Portfolio

How to get a job in data science if you don’t have the work experience to back the capabilities you claim to have in your resume? Well, your chance to stand out is to impress employers with your portfolio. It should show all the projects you’ve done, and it should look professional.

In addition, make sure to put your work on GitHub. That way, hiring managers can easily open the link from your resume and see what you’ve done.

Create Industry-Specific Projects

Having a portfolio with projects you completed as part of your studies simply won’t cut it. You need something exceptional to stand out among the hundreds of applicants. We have a detailed guide on creating a portfolio that stands out.

Follow it step by step, and you’ll have an advantage over other people getting started with data science. In it, we cover everything from project ideas to how and where to build a portfolio and even the pitfalls of using Kaggle.

Here’s one extra tip.

Choose projects related to the industry you want to work in. If you’re passionate about a particular cause or job opportunity, you can use datasets or even collect your own data for that specific field.

This kind of effort shows that you’re willing to go above and beyond, and everyone wants to have such a person on their team.

Networking

With social media and online events, networking is easier than ever. This should be an inseparable part of your application process. Shift your mindset from “how to get a data science job” to “how to become a part of the data science world.” One will lead to the other.

Communication with like-minded people is valuable for several reasons.

First, it will help you get into the mind of accomplished professionals. You will familiarize yourself with the way data scientists speak and think, the issues and trending topics in the field, and so on. This will give you confidence during interviews, and the data science interview preparation process won’t seem that daunting.

Second, it can be a great source of support. You can find useful advice on how to become a data scientist – be it help on specific projects or the best places to look for a job.

Lastly, it can be your gateway into the data science world. Many companies have referral systems and use them to find employees. The easiest way to get noticed is through a recommendation from a current employee.

So, reach out to people on forums, go to conferences and networking events, and offer your help and advice when you can. You never know which connection will be the key to finding jobs without experience.

Approach Employers

Don’t rely only on job postings and cold applications. Instead, reach out to hiring managers from the companies you’d like to join and offer to do some free analysis for them. Or even better, perform analysis on their company, industry, product, or competitors and send it to them.

First, this is great practice. But more importantly, it puts your name out there. Even if that doesn’t lead to a job offer, you will expand your network.

Look For Entry-Level Data Scientist Jobs

There are plenty of strategies to find entry-level positions.

For example, consider an unpaid data scientist internship. Approach companies with this offer – even those that have rejected you for a paid position.

Internships and unpaid work are an investment for the future! Once you get your foot in the door, more opportunities will open up. A couple of months of sacrifice may lead you to your dream career.

And if you’re switching from another field and are already employed, you can look for part-time data science jobs to begin with. That way, you can gain experience without sacrificing your financial security.

Consider Working Remotely

Nowadays, many data scientist jobs are remote, so you don’t have to limit your search to employers near you. This is a great strategy for two reasons.

First, salaries tend to vary between locations. Data scientist jobs in NYC, for example, are much better paid than those in, say, Mumbai. Why not earn your salary in the US and spend it in India?

Second, by working remotely, you can broaden your search. That means you can find more entry-level data science jobs, increasing your chances to get hired with no experience.

Build Your Personal Brand

Brand awareness isn’t just for businesses anymore. You need to make yourself recognizable and easily discoverable. This strategy takes some time, but it will help you establish yourself as an expert.

How to do that?

For example, you can start a blog to showcase your skills. You need to show a true passion for the field and a willingness to learn and develop. Good hiring managers will appreciate that.

Also, don’t forget to spread the word on social media as well. Content creation is a great way to get meaningful connections on LinkedIn!

How to Become a Data Analyst with No Experience?
The path to becoming a data analyst is very similar to the one in data science. Ideally, you need higher education in Data Analysis or a related field, like Statistics, Computer Science, or Information Technology. Alternatively, you can take an online certification course. If you’re not sure where to start, take a look at the 365 Data Analyst Career Track. It starts with the foundational courses on statistics and programming languages included in the Data Scientist Career Track. Then, it continues with lessons on data preprocessing and cleaning, specific to the data analyst occupation. After obtaining a degree or certification, you need to get proactive. For the best results, follow the steps described above – creating an outstanding resume and portfolio, networking, looking for entry-level positions, and so on.
What Is the Salary for an Entry-Level Data Engineer?
According to Glassdoor, the median salary for an entry-level data engineering position in the US was USD65,490 per year at the end of 2021. They also estimated that this number goes up to USD106,806 per year with additional pay, such as bonuses, profit sharing, and so on. That number depends on a number of factors, though. The highest salaries are in the Financial Services and Information Technology sectors, reaching a total annual pay of USD130,184 and USD101,364, respectively. If you work in the Travel Administration industry, for example, you can expect to receive around USD64,012 per year in total. Needless to say, the salaries also depend on the location, company, years of experience, and other factors.

How to Become a Data Scientist with No Experience: Next Steps

Okay, we gave you some advice on how to become a data scientist with no experience. The only question remaining is – are you willing to put in the effort to succeed?

If the answer is “yes,” the 365 Data Science program might be the perfect tool to learn the skills needed on the job. It offers self-paced courses led by renowned industry experts. Starting from the very basics all the way to advanced specialization, you will learn with a myriad of practical exercises and real-world business cases. If you want to see how the training works, sign up for free and start with a selection of free lessons.

 

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Aleksandra Yosifova

Blog author at 365 Data Science

Aleksandra is a Copywriter and Editor at 365 Data Science. She holds a bachelor’s degree in Psychology and is currently pursuing a Master’s in Cognitive Science. Thanks to her background in both research and writing, she learned how to deliver complex ideas in simple terms. She believes that knowledge empowers people and science should be accessible to all. Her passion for science communication brought her to 365 Data Science.

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