How to Become a Data Scientist

The demand for data scientists is projected to grow by 36% from now to 2033, according to data from The Bureau of Labor Statistics. Within this period, about 20,800 job openings will be available each year.

While data scientists require skills in project management, programming, statistics, machine learning, and visualization, their work primarily involves the use of analytical tools and techniques to extract insights from data.

If you’re interested in breaking into the well sought-after field of data science, we’ve created a guide with the most important details and resources.

Current demand for data scientists

According to reports from Zippia, 97.2% of companies are investing heavily in data and artificial intelligence projects, and among the companies sampled, 24% of them now use big data analytics. This rapid growth in data and AI projects follows the rapid technological advancement affecting every sector.

With many companies relying on intelligent data to some degree to make business decisions, the need for skilled professionals who can analyze that data and glean business insights increases.

SEE: How to Measure Data Quality (TechRepublic)

Given the sheer volume of the data businesses need to analyze, using traditional data management tools like spreadsheets and traditional databases will not suffice. Hence the need for data scientists who can use big data analytics tools to help businesses analyze data in real-time and make data-driven decisions from them.

What skills do you need to be a data scientist?

Here are the 11 marketable skills a data scientist might need, according to an Indeed report:

1. Cloud computing.
2. Statistics and probability.
3. Advanced mathematics.
4. Machine learning.
5. Data visualization skills.
6. Query languages.
7. Database management.
8. Python coding.
9. Microsoft Excel.
10. R programming.
11. Data wrangling.

“If you’re looking to enter the field of data science and build a solid foundation of experience that will stand out in the eyes of future employers, there are three core skills you need: Python, R, and SQL,” said Pablo Ruiz Junco, Glassdoor economic research fellow. “With these skills, you’ll be eligible to apply to over 70% of all online job postings for data scientist roles. Plus, expanding your skills beyond these foundational languages can lead you to a higher salary and allow you to cast a wider net when applying.”

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What is the average salary of a data scientist?

Average salary figures differ slightly for U.S. data scientists depending on which job site you look at. For example, Indeed says the average base pay is $127,000 per year, while Glassdoor says the average base pay is $151,000.

Data scientists in Palo Alto, California, receive the highest pay, at $168,000, followed by Herndon, Virginia, at $166,000, according to Indeed.

As you’d expect, the skills that individual data scientists bring to the table can have a large impact on pay. Jobseekers should consider what role they are most interested in and make a cost-benefit analysis of which skills are worth spending time learning.

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What are typical interview questions for a career in data science?

A junior data scientist can expect questions like the following in a job interview, according to Forrester analyst Kjell Carlsson:

• Walk me through the project you are most proud of where you used data, data science, machine learning, or advanced analytics. What was your role on the project, and what did you do in each step?
• Tell me about a project where you used (insert programming language or skill here).
• Tell me about a time you had to work with someone who is not data-savvy on a data science project.
• Tell me about a time when you had to become an expert on a new technique quickly.

The interviewee might be given a mini-case study based on a project the team has undertaken, with questions such as: What data would you need? What are the hypotheses you would like to test? What technique(s) would you use to evaluate them?

For more senior positions, these questions may come up, according to Daniel Miller, vice president of recruiting at Empowered Staffing:

• Have you built a data warehouse from scratch? If so, tell me about the process you created in order to successfully implement the data warehouse. If they have not been part of it from scratch, interviewers may ask if the interviewee has been part of a department that dealt with a company merger or acquisition of data and how they handled it.
• What types of customized dashboards have you built, and what information or analytics were being presented through your dashboard?
• Tell me about the most complicated data project you have worked on, and what you were able to do in order to achieve success.

SEE: Quick Glossary: Business Intelligence and Analytics (TechRepublic Premium)

Data scientist education and certification requirements

To become a data scientist, the education and certification requirements can vary based on your background, experience, and the specific roles you’re aiming for. However, here’s a general overview of what is commonly sought after.

Education

• Bachelor’s degree: A strong foundation in mathematics and programming is essential, so many data scientists hold a bachelor’s degree in a related field such as computer science, mathematics, statistics, engineering, physics, or economics.
• Master’s or doctoral degree: These are optional but increase a candidate’s chances of landing more advanced or research-oriented positions, as they indicate a deeper knowledge of data science concepts and techniques.

Certifications

Some key certifications include the following:

• Certified Analytics Professional (CAP): This certification does not focus on a specific commercial platform but prepares candidates to learn how to get big data insights. It is valid for three years, and candidates are required to pass the Associate Certified Analytics Professional exam before they can receive a CAP certification.
• IBM Data Science Professional Certificate: This certification is offered on platforms like Coursera, edX, and IBM Training and covers data science tools, methodologies, and programming languages.
• Microsoft Certified: Azure Data Scientist Associate: Candidates with the Azure Data Scientist Associate certification are expected to possess specialized knowledge in the application of data science and machine learning on the Azure platform.

SEE: Quick Glossary: Big Data (TechRepublic Premium)

What are some top tools data scientists should know?

The market for data analysis tools has exploded over the past several years, with numerous platforms available for organizations of all sizes and suited for all industries. Here are some data science tools any prospective data scientist should check out.

Tableau

Tableau is a powerful data visualization tool that connects to various data sources and simplifies creating interactive dashboards. Learning Tableau enhances data scientists’ ability to present findings, drive data-driven decisions and make their work accessible to a broader audience.

SEE: Check out our cheat sheet on Tableau.

TensorFlow

TensorFlow is an open-source tool for machine learning, especially for advanced techniques like deep learning. Proficiency in Tensorflow enables data scientists to leverage deep learning for complex tasks like image generation, natural language processing and reinforcement learning.

SEE: Learn more with our cheat sheet on TensorFlow.

Jupyter Notebooks

Jupyter Notebooks is another important tool in any data scientist’s arsenal. It offers an interactive web-based environment for combining live code, visualizations and sharing analyses in an understandable format. Data scientists benefit from Jupyter Notebooks for communication and collaboration.

SEE: Discover how Jupyter Notebooks compares to Google Colab.

Python

Python’s versatility, extensive library, and ease of use make it essential for any data scientist. With Python, you can run data manipulation, analysis, visualization, and machine learning workloads.

SEE: Explore our Python cheat sheet.

This article was originally published in September 2023. It was updated by Antony Peyton in May 2025.