How to Become a Data Scientist: Requirements, Salary & Career Outlook

Professional Female Data Scientist Using Software To Analyze Information from Internet

The role of a data scientist is multifaceted: Part mathematician, part computer programmer, and part storyteller, they play a critical role in the world of data-driven decision-making.

As technology advances and we find more ways to collect and utilize data, there is a growing need for more data scientists to pull critical insights from the massive volumes of information generated by virtually every industry. Here, we take a step-by-step look at how to become a data scientist, including preferred experience, educational requirements, and ideal skills.

What Does a Data Scientist Do?

Utilizing a diverse skill set, data scientists bridge the gap between the massive amounts of raw data generated by organizations every day and the actionable insights derived from that data. Their day-to-day responsibilities may include all or some of the following: 

Data Analyst vs. Data Scientist

While their roles can seem similar, data scientists and data analysts each have distinct responsibilities and functions within their organizations. 

Data analysts focus on interpreting existing data to provide descriptive insights, reporting on past trends, and making data accessible for decision-makers. They often use tools like Excel and SQL to generate reports. Their work often supports that of a data scientist. 

In comparison, data scientists have a broader skill set, combining data analysis with advanced statistics and machine learning. They dig deeper into data, developing predictive models, finding patterns, and creating algorithms to drive future strategies. Data scientists handle complex, unstructured data using programming languages such as Python and R

Data scientists are typically considered more senior than analysts, though they often begin their careers as statisticians or analysts. Think of it this way: Data analysts describe the past, while data scientists predict and shape the future using data.

Required Experience for Data Scientists

In addition to the skills required to become a data scientist, many employers prefer candidates for this role to have specific types of professional experience. No one begins their career as a data scientist, but the right experience indicates that someone will succeed in the role. 

Employers may look for the following in data scientist candidates: 

It’s important to remember that job descriptions often encompass everything and anything someone might be expected to do in a specific role — meaning that, if you don’t think you have all the appropriate experience for an open data scientist role, don’t be discouraged from applying. Hiring managers typically include every eventuality in their job listings, but possessing the majority of the preferred experience can be sufficient to land an interview.

Data Scientist Skills & Qualities

The function of a data scientist combines the work of several other technical and non-technical roles, including mathematician, computer programmer, analyst, and business strategist. Their broad skill set includes both hard, technical skills and more interpersonal skills that help them communicate their data-driven conclusions to business leaders.  

Preferred technical skills include: 

Preferred non-technical skills include: 

Benefits of Working in Data Science

Beyond providing a stimulating and rewarding career for those fascinated by data, data science has the potential to make a meaningful impact on society as a whole. Because nearly every industry leverages data scientists, pursuing this career means you could play a role in improving healthcare, AI software development and deployment, expanding access to social services, interpreting political election results, and more. 

The following are some of the top benefits of working in data science. 

High demand: Data science is a rapidly growing field with a chronic shortage of skilled professionals. This high demand translates into excellent job opportunities and competitive salaries.

Diverse career paths: Data scientists can work in a wide range of industries, from healthcare and finance to tech and entertainment. 

Impactful problem-solving: Data scientists have the opportunity to tackle complex, real-world problems, which can lead to tangible, positive impacts on businesses and society as a whole.

Continuous learning opportunities: Data science is an ever-evolving field, which requires that professionals constantly learn new techniques, tools, and technologies. This keeps the work intellectually stimulating and enables career growth.

Competitive salaries: Data scientists are among the highest-paid professionals in the job market due to their highly valued, specialized skill set. 

Remote work opportunities: Many data scientists can perform their daily tasks remotely. This offers them the flexibility to work from different locations or even internationally.

Creativity and innovation: Though it may not seem like a creative profession at face value, data scientists often have the freedom to explore creative solutions to complex problems and innovate using unconventional (but still data-driven) approaches.

Job security: Given the increasing importance of data in making business decisions, data scientists are in demand across industries, providing job security in an ever-changing job market.

Data Scientist Salary Expectations

The current average salary for data scientists in the U.S. is around $120,000. Depending on level of experience, employer, industry, and geographic location, data scientists can expect to earn between $82,000 and $188,000.

The growth potential for data science roles is particularly noteworthy. While the average job growth across all fields is between 5–8%, data science roles are expected to grow by 36% in the next decade.

This projected growth can be attributed directly to the steep rise of big data and the increasing importance of harnessing that data to make informed decisions. Artificial intelligence is also enjoying massive popularity and inspiring increased interest and curiosity across industries. There is no doubt that organizations large and small will be looking for skilled data scientists with AI experience to harness new technologies for data analysis and predictive modeling.

How to Become a Data Scientist

There is no one way to become a data scientist; since the role calls for such a broad skill set, data scientists can come from any industry or professional background. That said, there are specific skills and experiences future data scientists should have on their resumes before applying for jobs.   

  1. Earn a relevant degree.  

While not always required, many employers do like to see some relevant academic experience or credentials. Most data scientists hold at least a bachelor’s degree in a related field such as computer science, mathematics, statistics, engineering, or a domain-specific discipline like economics or biology. You can, however, become a data scientist without a degree, provided you gain the necessary skills some other way.

If you would like to further enhance your academic background and qualifications, consider pursuing a master’s degree in data science, analytics, or a related field. By doing so you can deepen your understanding of the many facets of studying data by tackling real-world problems. Many data scientists even have business degrees, since business acumen and strategy can be a large part of the role. 

  1. Sharpen your data science skills.

Whether or not you choose to earn an advanced degree, you should pursue opportunities to build your technical and non-technical skill set. You’ll need to be familiar with common programming languages, statistics, machine learning, data management and visualization, and knowledge of how data functions in a particular industry. If your degree didn’t cover these topics, or if you choose not to earn a degree, many of these skills are available through online certificate courses or boot camps. Speaking of which…

  1. Earn a professional certificate.

Employers like to see that data scientist candidates are committed to pursuing continuing education and professional development. A data science certificate provides you with a credential that conveys your value to future employers and positions you as a candidate for career advancement. 

  1. Gain practical experience.

While learning theory and completing simulations is helpful for getting comfortable with data science, there is no substitute for real-world application. Internships and entry-level analyst positions are great for those just starting out to gain on-the-job experience; many data scientists start their careers as analysts. You can also participate in competitions and connect with others who are honing their skills. If you prefer to work on a personal data science project, you can still add it to your portfolio to showcase your practical skills. 

  1. Build your network.

A professional network is a highly valuable asset when it comes to your job search. Attend data science conferences, workshops, and meetups to connect with professionals in the field (be sure to add them on LinkedIn or another professional platform). Join online communities and forums to learn from others and stay updated with industry trends

  1. Apply for data science jobs.

Once you feel you’ve acquired the necessary education and experience, start applying for relevant positions. Data scientist is almost never an entry-level role, so look for positions with titles such as data analyst, business intelligence analyst, statistician, or data engineer.

As with any position, make sure to tailor your resume and cover letter to highlight your skills, projects, and relevant experience that would be of interest to the employer. Prepare for interviews by reviewing common data science interview questions and practicing problem-solving. Be persistent and apply to a range of positions to increase your chances of securing a job.

How to Advance in Data Science

As you start to build your data science career, keep an eye on industries that have a growing need for skilled analysts and scientists. Financial firms, government agencies, and pharmaceutical companies are regularly seeking data scientists, though opportunities are growing in many other industries. 

To advance to higher-level positions in the field, keep pursuing opportunities to grow and refine your skills. This is applicable to both technical and non-technical skills; in fact, taking courses or workshops in areas such as leadership and management makes you even more valuable to current and future employers. This indicates that you are as concerned with the interpersonal part of your job as the technical.  

You may also want to find a mentor, someone in a more advanced role who has likely been working in the field longer. They may be able to give you advice from their own lived experience and even recommend exploring certain lucrative or rewarding industries.  

Along with finding your own mentor, consider becoming a mentor to someone else, especially as you advance in your organization or career. Teaching is one of the best ways to learn, so passing your recommendations and perspective on to someone who is just starting out can help you build your own confidence or realize your own specialized interests within the data science field. 
Above all, there is no such thing as being too knowledgeable in the field of data science. New data analysis technologies and collection methods are introduced regularly, and the volume of data we generate is only going to grow exponentially. Pursuing an advanced certificate in data science and engineering — such as the online Practical Data Engineering Certificate from the University of San Diego — is an excellent way to demonstrate your commitment to staying agile and up-to-date on the latest tools and best practices.

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FAQs

What’s the difference between a data scientist and data analyst?

Data analysts describe the past, while data scientists predict and shape the future using data. Data analysts often support data scientists, focusing on interpreting existing data to provide descriptive insights, reporting on past trends, and making data accessible for decision-makers. Data scientists have a broader skill set, combining data analysis with advanced statistics and machine learning to drive future strategies. Data scientists are typically considered more senior than analysts, though they often begin their careers as statisticians or analysts.

Where do data scientists work?

Data scientists can work in a wide range of industries, from healthcare and finance to tech and entertainment. Financial firms, government agencies, and pharmaceutical companies are regularly seeking data scientists, though opportunities are growing in many other industries.

Do you need a degree to work in data science?

While not always required, many employers do like to see some relevant academic experience or credentials. Most data scientists hold at least a bachelor’s degree in a related field such as computer science, mathematics, statistics, engineering, or a domain-specific discipline like economics or biology. Whether or not you choose to earn an advanced degree, you should pursue opportunities to build your technical and non-technical skill set, such as certificate programs or boot camps.

Curriculum covered in this article

Data Analytics and Visualization Certificate – Bundle and save 10% when you enroll in all four courses (must add all 4 courses in one transaction for discount):

  • DAV-X800 Essentials of Analytics in Excel
  • DAV-X801A Data Visualization & Storytelling with Tableau
  • DAV-X801B Data Analytics with Python and R
  • DAV-X802 Data Analytics and Visualization Capstone

To apply credit, select the checkbox and select  "BUS – DAV Bundle and Save 10%" for a discount on each course.

**Only one discount can be applied per course. Discount does not apply to certificate fee.**

Business
Engineering Programs
2 Units
Online Fixed-Date

Data Analytics and Visualization Certificate – Bundle and save 10% when you enroll in all four courses (must add all 4 courses in one transaction for discount):

  • DAV-X800 Essentials of Analytics in Excel
  • DAV-X801A Data Visualization & Storytelling with Tableau
  • DAV-X801B Data Analytics with Python and R
  • DAV-X802 Data Analytics and Visualization Capstone

To apply credit, select the checkbox and select  "BUS – DAV Bundle and Save 10%" for a discount on each course.

**Only one discount can be applied per course. Discount does not apply to certificate fee.**

Business
Engineering Programs
2 Units
Online Fixed-Date

Data Analytics and Visualization Certificate – Bundle and save 10% when you enroll in all four courses (must add all 4 courses in one transaction for discount):

  • DAV-X800 Essentials of Analytics in Excel
  • DAV-X801A Data Visualization & Storytelling with Tableau
  • DAV-X801B Data Analytics with Python and R
  • DAV-X802 Data Analytics and Visualization Capstone

To apply credit, select the checkbox and select  "BUS – DAV Bundle and Save 10%" for a discount on each course.

**Only one discount can be applied per course. Discount does not apply to certificate fee.**

Business
Engineering Programs
2 Units
Online Fixed-Date
Business
Engineering Programs
3 Units
Online Fixed-Date

Practical Data Engineering Certificate – Bundle and save 10% when you enroll in all four courses (must add all 4 courses in one transaction for discount):

  • PDE-X800-Data Engineering Fundamentals
  • PDE-X801-Practical Data Extraction
  • PDE-X802-Practical Data Integration
  • PDE-X803-Practical Data Engineering Capstone

To apply credit, select the checkbox and select  "BUS – PDE Bundle and Save 10%" for a discount on each course.
**Only one discount can be applied per course. Discount does not apply to certificate fee. **

Business
Engineering Programs
2 Units
Online Fixed-Date

Practical Data Engineering Certificate – Bundle and save 10% when you enroll in all four courses (must add all 4 courses in one transaction for discount):

  • PDE-X800-Data Engineering Fundamentals
  • PDE-X801-Practical Data Extraction
  • PDE-X802-Practical Data Integration
  • PDE-X803-Practical Data Engineering Capstone

To apply credit, select the checkbox and select  "BUS – PDE Bundle and Save 10%" for a discount on each course.
**Only one discount can be applied per course. Discount does not apply to certificate fee. **

Business
Engineering Programs
2 Units
Online Fixed-Date