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How to Become a Data Scientist With No Experience
Data Science

How to Become a Data Scientist With No Experience

10 minute read | December 21, 2023
Sakshi Gupta

Written by:
Sakshi Gupta

Ready to launch your career?

We all wish we had the foreknowledge to pick the perfect career and undertake the necessary preparation to get there, but real life isn’t always a linear path—and that’s part of what makes it fun. Moreover, with the fast pace of technological change, new industries and lines of work are emerging all the time. One career path to consider is a career in data analysis!

Since data science is a high-growth, in-demand career field with strong job prospects, it’s a good time to explore whether becoming a data scientist is the right next career for you.

The great news is, you don’t need prior experience to become a data scientist and land your first data science job. There are plenty of ways to acquire a data science skillset on your own. Becoming a data scientist is no longer a pipe dream. Here’s your cheat sheet to getting there.

Before exploring the needed skills to master to become a data scientist without work experience, let’s first define what exactly a data scientist does.

What is a data scientist?

Data scientists collect and clean large amounts of data, maintain easy-to-use dashboards and databases, interpret data to solve problems and run experiments, build algorithms, and present data to stakeholders in attractive visualizations. And yes, they analyze data, but there’s a lot more to it. You’ll need to understand the fundamentals of machine learning, data visualization, relational databases, deep learning, exploratory data analysis, big data, and master techncial skills in statistical software, natural language processing, and predictive modeling. You’ll also need to learn to communicate effectively so you can present those meaningful insights you’ve gleaned by analyzing large data sets to important stakeholders.

Today there are many reasons to pursue a career in data science: a high salary, relatively stable and growing job market even during a global recession, and exciting problems to solve across diverse industries. The data science field is rich, with numerous interesting entry-level positions to get you started.

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Is data science hard?

Whether or not data science is hard really depends on your background and whether you enjoy working with numbers and data. While data scientists do not need as much software engineering or machine learning as data engineers, you will need to learn how to code in order to build predictive models. Data science concepts take time to learn, and it takes time to refine your data science skills. But once you have the basic idea, you can perform analyses and complete personal projects to help you impress future employers in no time.

Data science has a steep learning curve, involving tough problems, a large amount of data, technical expertise, and domain knowledge, but luckily there are many free online resources to help you get started as an entry-level data scientist. Hopefully, you enjoy continuous learning because data scientists are constantly upskilling and learning new technologies.

do you need a degree to become a data scientist

Finding a Job In Data Science

In today’s competitive job market, securing a data science job requires a strategic approach, combining technical prowess with a solid foundation of skills. Data scientists are highly sought-after professionals who analyze data to extract valuable insights, solve complex problems, and contribute to decision-making processes. Whether you’re an entry-level candidate or an experienced data scientist, navigating the job market in the data science field involves showcasing a diverse set of skills and qualifications.

A typical data science job involves utilizing machine learning algorithms to analyze large datasets. Data scientists employ various programming languages, such as Python, R, and SQL, to perform analysis and draw meaningful conclusions. Proficiency in these languages is essential for effective communication and collaboration within the data science community.

Data visualization is another critical aspect of a data scientist’s role. Being able to present findings in a clear and compelling manner is crucial for conveying complex ideas to both technical and non-technical audiences. Using tools like Tableau, Power BI, or matplotlib, data scientists create visual representations that help stakeholders understand trends and patterns discovered during the analysis.

The data science field is broad, encompassing computer vision, artificial intelligence, and other advanced topics. Having a theoretical understanding of these concepts, coupled with hands-on experience, is extremely important for success in the industry. Employers often seek candidates with a strong background in both the theoretical and practical aspects of data science.

While a bachelor’s degree is a common prerequisite for entry into the field, the job market also values candidates with additional certifications and specialized training. Job opportunities for data science professionals are diverse, spanning various industries such as finance, healthcare, and technology.

The median salary for data scientists is competitive, reflecting the high demand for their skills. As technology evolves, so do the tools and techniques used in the field. Staying abreast of these changes and continuously acquiring new skills is essential to remain relevant in this dynamic field.

Technical skills alone are not enough; soft skills are equally crucial. Data scientists often work in interdisciplinary teams, and the ability to communicate complex findings to non-technical stakeholders is a valuable asset. Collaboration, creativity, and critical thinking are highly valued by hiring managers in the data science job market.

In conclusion, getting a data science job requires a combination of technical expertise, a strong foundation in the field, and the ability to communicate effectively. With the high demand for data science professionals and the ever-expanding job market, investing in acquiring and honing the necessary skills is a wise step towards a successful career in data science.

Do you need a Computer Science degree to become a data scientist?

No. It is possible to learn data science without an advanced degree, or even a bachelor’s degree. Although most job postings list a master’s degree or Ph.D. in engineering, computer science, mathematics, or statistics, the demand for data scientists far outpaces the supply, meaning companies are open to hiring non-traditional applicants. In fact, many top companies like Google, Apple, and IBM no longer require college degrees for applicants. There are many entry-level positions you can pursue if you have the right practical knowledge and experience according to the job descriptions.

If you are looking to break into data science without a degree, you can take online courses and certification programs or self-teach yourself through videos and modules that can help you land that data science job. Many data professionals have started their careers this way.

Below you’ll find six steps for breaking into data science without previous experience.

How to Become a Data Scientist With No Experience

  1. Complete a Course

  2. Polish up on your Math Skills

  3. Learn a programming language (or two!)

  4. Take on side projects or internships

  5. Start as a data analyst

  6. Work hard—and network harder

  7. Explain your career transition to potential employers

Related Read: How To Get Into Data Science (Without a Data Science Degree)

Step 1: Complete a Course

The best way to get practical experience is with a bootcamp. These online courses not only cover all of the fundamentals you need, but also involves meeting with a mentor and completing practical projects. Good bootcamps also come with a data science job guarantee. These courses can teach you how to create algorithms, manipulate data, master machine learning, gain insights from the data generated, and other data engineering skills. You’ll also complete personal and team projects that will help you during your job search.

Step 2: Polish up on your math skills

If you are coming from a quantitative background, data science should be an easy transition. Before analyzing data with high-tech tools, you need to get to the foundation of data analysis, which starts with plotting data points on graphs along the X and Y axes and finding correlations and trends between different variables.

To make sure you can write efficient code and draw accurate conclusions, here are some recommended math concepts to master:

  • Statistical methods and probability theory
  • Probability distributions
  • Multivariable calculus
  • Linear algebra
  • Hypothesis testing
  • Statistical modeling and fitting
  • Data summaries and descriptive statistics
  • Regression analysis
  • Bayesian thinking and modeling
  • Markov chains

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Step 3: Learn a programming language (or two!)

Compared to other career fields, data science is more about what you know and how well you can prove your relevant skills and less about the prestige of your alma mater. The skill-based interview process tends to level the playing field for people coming from different backgrounds.

Once you have a solid foundation with math, you can begin to pick up a few of the must-know programming languages for aspiring data scientists: SQL, R, Python, and SAS.

Read this article for an introduction to necessary data scientist skills and which languages to prioritize.

  • Python is a scripting language with libraries that enable you to wrangle, filter, and transform big data and unstructured data. Python has applications for web development, software development, deep learning, and machine learning. It is the most frequently used tool by data scientists.
  • R is an open-source programming language useful for complicated mathematical and statistical calculations. It also allows for data visualizations and has a large support community to help you get started.
  • SQL is a relationship management tool through which you can query for and join data across multiple tables and databases as a data scientist.
  • SAS is an expensive tool used by large corporations for statistical analysis, business intelligence, and predictive analytics, but it is not recommended for individuals because of the cost. If you learn the other languages, you can easily pick up SAS on the job.

You can practice basic programming in Springboard’s free data analysis course and then complement those skills with more advanced programs, like the data science bootcamp. This will help you navigate the job market with ease.

Step 4: Take on side projects or internships

To build your resume, companies will want to see professional practical experience. As you start building out your knowledge base, you can apply your skill-set in real-world settings and get real-time feedback.

You can use freelancing platforms like Upwork or Fiverr, as well as search for part-time work or internships through social media and job boards. Kaggle also offers competitions with monetary prizes. The data science job market is rich and diverse.

Before interviewing, make sure to practice solving coding problems on LeetCode and explore potential data science interview questions.

Show examples of past work samples on Github, LinkedIn or a personal website to build a good portfolio and a strong online presence.

It can be hard to gain experience without experience, but by leveraging online communities and starting small, you can prove that you have what it takes to turn data science knowledge into measurable business outcomes.

data science internships

Step 5: Start as a data analyst

Data scientists and data analysts are not one and the same, and they are both career fields exploding in popularity.

Data analysts manage data collection and identify dataset trends.

  • Data scientists not only interpret data but also apply skills in coding and mathematical modeling
  • Data analyst positions can be easier to break into as a first job and can be a great launchpad to a data science career

For those interested in starting in data analytics, Springboard’s mentor-driven data analytics bootcamp covers framing structured thinking, analyzing business problems, connecting data using SQL, visualizing data with Python, and communicating analyses. You can find work as a data engineer more easily with a bootcamp.

If you’re not sure which data course is the ideal fit, take this quiz to find out.

Step 6: Work hard—and network harder

Getting to know other data scientists is the best way to learn more about different career opportunities and maybe even meet your future team members. You can also discover what kind of company you’d like to work for (size, industry, culture), what projects appeal to you, and how to prepare for the job application process. Join networking events where industry leaders are present and mingle with other professionals.

When starting out without experience, it can be easier to break into smaller companies, but larger companies in the tech industry with entry-level programs may have more infrastructure built-in for training and mentorship.

Another great option is to pivot into data science from another position at your company. If you’re in good standing, you can usually start networking internally and explore the opportunity to interview with a data science team, provided you meet the technical requirements.

As you set up virtual coffee chats and phone calls, you may find the conversations pique your interest in certain job postings. This opens the door to asking people in your network for personalized referrals. According to Careerbuilder, 82% of employers rated referrals as having the highest ROI, and many companies incentivize employers to be on the lookout for emerging talent by offering cash bonuses.

Step 7: Explain your career transition to potential employers

Data science is a highly interdisciplinary field, and most likely not all prior knowledge will be lost. Data scientists need to be able to connect their models to direct business impact. Although you should definitely focus on your data science experience in your resume and cover letter, you should mention past roles where you used Microsoft Excel or developed business, communication, collaboration, and other transferable skills. Think about your ideal job title and structure your letter accordingly.

When applying for data science jobs without experience, include a short summary section on your resume explaining your shift, using keywords, and listing courses you’ve taken, technical languages and complex algorithms you’ve learned, and any project work you’ve completed, to frame your expanding data science skillset in the best possible light.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.