How to Become a Data Scientist

From an organizational standpoint, data is invaluable because it provides the insight needed to anticipate market shifts, understand customer behavior, and make strategic decisions that drive growth and resilience in a competitive business environment. While many analysts work with data, not all have the ability to turn raw data into meaningful insights.

Leveraging data to gain a competitive advantage requires specialized skills and training. The role blends technical expertise, statistical analysis, and business acumen, making it one of the most sought-after careers in the modern job market.

Individuals wondering how to become a data scientist should explore what the job entails, what to expect regarding job growth and salary, and the steps to take to qualify for roles. Because data science is a competitive field, investing in education, such as a graduate-level data science program, is highly recommended.

What Is a Data Scientist?

Data scientists use statistical and analytical methods to turn raw data into valuable information that supports decision-making and helps solve complex problems within an organization.

Common job duties of a data scientist include the following:

• Collecting, cleaning, and organizing large datasets from multiple sources
• Using statistical techniques and machine learning algorithms to analyze data
• Developing predictive models and data-driven solutions to business problems
• Creating data visualizations and dashboards to communicate insights to stakeholders
• Collaborating with cross-functional teams to implement data-based recommendations
• Staying current with emerging tools, programming languages, and analytical techniques

Data is used to inform decisions and strategy development across various industries. In marketing, for example, data is used to understand consumer behavior and optimize campaigns. In finance, data is used to track market movements, forecast economic trends, and evaluate investment opportunities with greater precision.

Steps for Becoming a Data Scientist

Becoming a data scientist is a multistep process that involves a combination of education, practical experience, and specialized skill development. Some steps are required, while others are highly recommended for competitiveness in the job market.

1. Earn a Bachelor’s Degree

The first step in becoming a data scientist is earning a bachelor’s degree in a relevant field, such as computer science, statistics, mathematics, or data science. A bachelor’s degree provides the necessary foundational knowledge in programming, statistical analysis, and data management.

Although employers sometimes accept candidates with undergraduate degrees in unrelated fields, having a technical or quantitative background greatly increases job market competitiveness.

2. Earn an Advanced Degree (Optional)

Although not always required, pursuing an advanced degree can significantly enhance a candidate’s career prospects as a data scientist and open doors to higher-level opportunities.

For example, earning a Master of Science in Data Science or a related field provides in-depth training in areas such as machine learning, big data technologies, advanced statistical modeling, and domain-specific applications. Completing a master’s program can help candidates qualify for mid-level and senior roles, as many employers value the specialized expertise and problem-solving capabilities that come with graduate-level education.

For those interested in research-intensive or senior leadership positions, earning a doctorate in big data analytics, data mining, statistics, or a related discipline may be the next step. Doctoral programs focus on original research, advanced analytics, and the creation of new algorithms or methodologies. A doctorate can unlock opportunities in specialized research laboratories, think tanks, and high-profile corporate environments, where innovation and deep technical knowledge are essential.

3. Gain Experience and Build a Portfolio

Employers want to know that data scientist candidates possess more than theoretical knowledge; this is why hands-on experience is critical for qualifying for competitive roles. Entry-level positions, including data analyst, business intelligence analyst, and junior data scientist, provide opportunities to apply and develop skills in real-world settings. Internships, freelance projects, and volunteer opportunities can also help build valuable experience.

Candidates can showcase their experience in a portfolio, which is one of the most important assets for aspiring data scientists. It should include examples of cleaned and analyzed datasets, predictive models, and data visualizations. Highlighting real-world projects demonstrates to employers that the candidate possesses the ability to apply technical and problem-solving skills in practical scenarios.

4. Earn Certifications (Optional)

Professional certifications are an excellent way to validate technical skills and competencies. Popular options include the SAS certified data scientist credential, the Microsoft Azure data scientist associate credential, and certifications in programming languages, such as Python or R.

While these credentials are optional for most roles, they help candidates show employers that they possess specialized technical knowledge and are committed to professional development.

Data Scientist Salary and Job Outlook

Knowing how to become a data scientist is only one part of the decision-making process. For many job seekers, an equally significant question is determining whether this career path is worth it.

According to the U.S. Bureau of Labor Statistics (BLS), employment for data scientists is projected to grow 36% from 2023 to 2033, much faster than the average growth rate of 4% for all occupations combined. This rapid growth reflects the increasing reliance on data analytics across industries.

The BLS also reports that the median annual salary for data scientists was approximately $112,590 in 2024, with top earners making more than $190,000 depending on experience, location, and industry.

Leverage Your Education Into a Successful Data Science Career

Becoming a data scientist is a rewarding and sustainable career path that combines analytical skills, technical expertise, and creative problem-solving. By earning a relevant degree, obtaining certifications, and getting practical experience, candidates can effectively set themselves up for success in this high-demand field.

If you’re ready to make your move into data science, the first step is investing in an educational path that best supports your long-term goals, such as the Master of Science (MS) in Data Science program offered by Tseng College at California State University, Northridge. The program is 100% online, takes two years to complete, and features a curriculum that establishes the necessary foundational knowledge and skills for graduates to successfully pursue data scientist roles.

Explore the MS in Data Science program to see how it supports the next step in your data science career.