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How to Become a Data Scientist in 2024: 10-Step Guide

9 minute read | December 1, 2023
Monica J. White

Written by:
Monica J. White

Ready to launch your career?

According to the U.S. Bureau of Labor Statistics, “data scientist” positions are growing at a faster than average pace, with projected 35% growth by 2032.. Coupled with a median salary of $131,490, it’s never been a better time to get into the field of data science. 

But changing careers is not something that can be done overnight. From education and portfolio-building to networking and interviewing, there are several steps budding data scientists must take before they are ready for their first day on the job.

How to Become a Data Scientist

The following list outlines the steps required to become a data scientist in 2024. Each step includes a link to the corresponding section below, in which we outline each one in greater detail:

1. Start with a Prep Course

If you are completely new to the field, it’s best to start with an introductory prep course into data science. You’ll learn a lot about the tools you’ll use, the industry, and what your day-to-day working life might be like.

2. Brush Up on Data Science Fundamentals

The obvious place to start when considering how to become a data scientist is assessing what you know and what you don’t. Niches and specialties cannot be developed until the fundamentals of the trade have been perfected, so starting simple is never a waste of time!

Here’s where you should focus your energy:

  • Statistical Analysis. This includes analyzing data and presenting the findings through unbiased and accurate statistics.
  • Programming. Writing code and building software are key skills in many areas of data science.
  • Data Visualization. Presenting data in tables, graphs, charts, or dashboards ensures that it can be quickly and easily comprehended by both technical and non-technical individuals.
  • Machine Learning. Create software that “learns” from the data it ingests and uses it to spontaneously adapt its processes in response to unique situations.

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3. Meet the Educational Requirements

Don’t worry – you don’t need a college degree to become a data scientist, but there are some educational requirements you need to be aware of before you attempt to land your first data science job.

  • You Don’t Need a Degree in Data Science, But a Bachelor’s Degree Can Be Helpful. Data science degrees are still relatively new college programs that combine elements of mathematics, computer science, statistics, and business science. As such, a separate or advanced degree in any of these or other relevant fields is a perfectly acceptable educational background for a data scientist.
  • Take a Certified Course or a Bootcamp. If you are considering how to become a data scientist without a college degree, the best options are to look at secondary certifications. Taking a certified course or enrolling in a data science bootcamp is a great way to get an immersive, hands-on, and practical education in the fundamentals of the subject of data science and, more specifically, in the role of a data scientist.

4. Plan Your Career Path and Specialization

Once you have the fundamentals under control, you can begin to plan a path for yourself and develop a niche or specialization that can give you an edge when you’re in the job market.

Related Read: 14 Data Science Careers to Consider [Skills, Salary, Role]

5. Hone Essential Skills

Earning certifications in data science or developing your own data science projects to showcase the list of skills below is a great way to demonstrate your ability.

Technical Skills

IT professionals who work with computers every day can save themselves time and effort if they knowhow to troubleshoot problems with some of these general technical skills.

  • Programming Languages. A data scientist is expected to be fluent in Python, and generally familiar with other languages like R or SQL.
  • Data Mining. Data mining is the gathering of relevant and trustworthy data from different sources and formats and often includes third parties, as well.
  • Data Visualization. Getting a grip on data visualization tools like Tableau, Microsoft Excel, and Google Charts to display data in readable ways is a key skill for data scientists.
  • Data Analysis. Querying and analyzing structured data can help identify patterns and points of interest that translate into actionable insights and business opportunities.
  • Statistics. Statistics can provide unbiased insights based purely on numbers and data to help decision-makers separate fiction and feeling from fact when forming business plans. 
  • Machine Learning. Once you have studied the fundamental concepts behind machine learning, you may want to pursue it as a specialization, which means furthering your study and working on your own machine learning projects.
  • Deep Learning. Deep learning is a branch of machine learning that focuses on replicating certain learning methods that ingest and reinforce information via artificial neural networks that resemble human neurons.
  • Natural Language Processing. Another branch of machine learning, natural language processing (NLP) is widely used for virtual assistants like Apple’s Siri or Amazon’s Alexa.
  • Algorithms. Algorithms are sets of instructions that solve problems and perform computations. AI programs utilize these algorithms to automate business processes or perform various functions, depending on the unique instance they are employed for.
  • Data Engineering. Data engineering includes every data-related skill, from gathering data to wrangling and cleaning it — basically all the steps prior to analysis.

Soft Skills

Data scientists must communicate their findings to employees beyond the scope of their data science teams, which means explaining their insights to people who lack the same knowledge or skill sets. This requires highly developed soft skills – also called people skills – in communication and presentation.

  • Analytical Mindset. Data science isn’t just about finding the right answers from data sets, but also about forming insightful questions, which requires an analytical mindset.
  • Business Acumen. Many data scientists work with the goal of improving the company they work for, whether that means improving efficiency, raising profits, coming up with profitable ideas, or finding new ways to save money. In addition to being able to read the data, data scientists need to understand what a business needs.
  • Critical Thinking. Critical thinking helps root out anomalies and incorrect values in data sets, and is therefore particularly useful for the data cleaning and wrangling process.
  • Adaptability and Flexibility. Data is what it is, not what we want it to be, which means it may not always show us the information we want to see. When hiccups occur, adaptability and flexibility are key to overcoming problems and turning them into something useful.
  • Collaboration And Teamwork. Data scientists usually work within a team and so must be able to work well with others. They also need to collaborate with other departments to collect data and share their findings.
  • Problem-Solving. Being a self-starter is a valuable trait for data scientists. Working on solutions and improving your problem-solving skills will make you a great asset to any team. 
  • Communication. When a problem needs to be shared, a deadline extended, or a plan changed, swift and clear communication is key to maintaining a happy team and a healthy project.
  • Patience And Persistence. Sifting through data can be a time-consuming process, and patterns will not always jump out willingly. Patience is key to finding those all-important actionable insights. 

6. Familiarize Yourself With the Essential Data Science Tools

Here is a list of popular tools used in the field of data science so that you can familiarize yourself with them.

  • Apache Spark. An analytics engine used for large-scale data engineering, processing, and machine learning.
  • Tableau. A data visualization tool that connects to databases and allows users to create powerful and varied visual representations of their data.
  • SAS. A statistical software suite designed to help users manage, analyze, predict, and visualize data.
  • MATLAB. A programming language used to develop algorithms and create models.
  • Python. Python is a programming language designed for readability and used frequently by 75% of data scientists
  • R. A statistical programming language used by 47% of data scientists
  • BigML. A popular machine learning platform.

7. Gain Practical Experience by Working on Projects and Building Your Portfolio

Developing a large, impressive, and detailed portfolio is a great way to showcase your ability and passion, especially if you lack formal education or experience. Creativity and individualism are highly valued, so you can choose from any and all data science projects that may interest you.

8. Build Your Network 

Building your network is a good way to get your foot in the door when you’re first starting out. It will help you develop your skills, make friends with similar interests, work on your passion projects, and finally, find a data science job. 

  • LinkedIn. Linkedin is a useful tool if you use it correctly. Always connect with colleagues and acquaintances you make within the industry, even those with whom you simply attended an event. 
  • Online Communities. Online professional and amateur communities on platforms like Discord and Reddit are great places to exchange ideas. 
  • Conferences and Meet-Ups. Becoming an active member of your local data science community doesn’t have to come after you’ve landed the job and made a name for yourself. Start attending conferences and meet-ups as soon and as often as you are able. 

9. Pursue a Data Science Internship

If you are lucky enough that you can work for free or for limited pay, look into getting a data science internship to complement your study with real-life experience. 

10. Prepare for and Ace the Data Science Interview

When preparing for a data science interview, research is key. Research the company, the people, the role, typical data science interview questions, and questions you should ask the interviewer.

Popular Roles Within Data Science

Here is a breakdown of some of the most popular roles and titles within the field of data science

become data scientist

Data Scientist Salary: How Much Can You Make?

Here are some average salaries for data scientists at different stages in their careers.

Entry-Level Data Scientist

For a data scientist’s first role, the average salary sits at around $110,179. However, even in your first data science or analysis role, salary negotiation is possible.

How To Become a Data Scientist, entry level data scientist salary
Source: Glassdoor

Related Read: 7 Entry-Level Data Science Jobs

Mid-Level Data Scientist

Mid-level data scientists have around 2-4 years of experience and can expect to earn about $120,256.

How To Become a Data Scientist, mid level data scientist salary
Source: Glassdoor

Senior-Level Data Scientist

Senior-level professionals who are around 5-7 years into their data science careers can expect to earn around $150,910 annually. However, Glassdoor reports a top threshold of $307,000.

How To Become a Data Scientist, senior level data scientist salary
Source: Glassdoor

FAQs on How to Become a Data Scientist

Here are some FAQs about the path to becoming a data scientist.

Is It Hard To Become a Data Scientist?

It is not specifically easier or harder to become a data scientist compared to other jobs in the tech industry. If you are motivated and hard-working, you will be able to gain the skills needed to become a data scientist.

Can a Beginner Learn Data Science?

Yes, there are even data science bootcamps and online courses available that are catered specifically for beginners with no prior experience in the field

Can You Become a Data Scientist Without a Degree?

Yes, there are other ways to showcase your data science skills and credentials, such as completing accredited online courses, earning official certifications, and developing an extensive portfolio.

Is Data Science a Stressful Job?

Data science is not reported to be a stressful job. In fact, Glassdoor reports that from a pool of 26,000 data scientists, the work/life balance for the job is rated at 4 out of 5 stars. 

How long does it take to become a data scientist?

Becoming a data scientist can take anywhere from a few months to several years.

If you have a strong background in mathematics, statistics, and computer science, and are familiar with programming languages such as Python or R, you may be able to acquire the necessary skills in a few months through intensive self-study or a data science bootcamp. However, if you are starting from scratch, it may take several years to gain the necessary education, skills, and experience.

How to Become a Data Scientist – The Next Steps

If you’re still wondering how to become a data scientist after reading all the steps above, we understand – the process is long and often confusing. A great place to start if you aren’t sure is with a reputable data science bootcamp. The courses offered by Springboard are comprehensive and provide graduates with the essential resources and skills to grow their careers, as well as the industry knowledge to identify the best path for them. 

About Monica J. White

Monica is a journalist with a lifelong interest in technology, from PC hardware to software and programming. She first started writing over ten years ago and has made a career out of it. Now, her focus is centered around technology and explaining complex concepts to a broader audience.