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?
Required Experience for Data Scientists
Data Scientist Key Skills
Benefits of Working in Data Science
Data Scientist Salary Expectations
How to Become a Data Scientist
How to Advance in Data Science
Frequently Asked Questions

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 management: Data scientists are responsible for sorting through, analyzing, and managing large volumes of data, both quantitative and qualitative. They gather, clean, and organize data from various sources.
Hypothesis testing: Data scientists make inferences and test different approaches to sorting and analyzing information collected in nearly every industry. Their hypotheses can provide visibility into customer and market trends, financial risks, cybersecurity threats, stock trades, equipment maintenance needs, public health trends, and more.
Pattern identification: Patterns and trends are inherent in large datasets, indicating valuable insights that might not be apparent on the surface. It is a data scientist’s job to spot, understand, and propose concrete actions based on these patterns.
Statistical analysis: Because statistics underpin many data analysis methods, data scientists need to have a solid grasp of statistical tests and probability distributions.

Delivering business insights: Of course, all of this data needs to be put to use. Data scientists’ conclusions empower organizations to make more effective decisions, including improving marketing campaigns, enhancing customer service, optimizing supply chain management, and developing overall better business strategies.

Cross-functional collaboration: Data scientists collaborate with both IT professionals, who handle data infrastructure and technology, and executives who need data insights to guide their strategic decisions.
Defining best practices: Establishing standards and best practices is essential when it comes to collecting and analyzing data, so it is part of a data scientist’s job to continually maintain and update these rules as needed to ensure data integrity and quality.

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:

Proficiency in all phases of the data science lifecycle, from data collection and discovery to cleansing, modeling, validation, and delivering actionable insights

Understanding of common data warehouse and data lake structures

Ability to perform as-needed analysis and present findings clearly
Experience with data visualization tools such as Tableau and Power BI
Familiarity with statistical problem-solving techniques
Proficiency in popular machine learning frameworks and techniques
Expertise in both qualitative and quantitative analysis methods
Track record of innovation, especially in identifying new ways to apply machine learning and data mining tools to improve business processes
Experience with public cloud platforms and services for data processing and storage
Familiarity with various data sources, including databases, big data platforms, public or private APIs, and standard data formats such as JSON, YAML, and XML

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:

Programming languages: Studying and deriving insights from data requires that data scientists be familiar with programming languages including Python, R, SAS, SQL, or whichever language their organization uses.

Data manipulation: Mastery of libraries such as Pandas, NumPy, and data visualization tools like Matplotlib or ggplot2 for data cleaning and exploration.
Predictive modeling: By creating predictive algorithms and models based on historical data, data scientists can theorize about and forecast future outcomes.

Machine learning: Understanding and implementing various machine learning algorithms and frameworks such as scikit-learn or TensorFlow for predictive modeling.
Artificial intelligence: In some cases, data scientists may also develop AI technologies for internal use or for customers.

Database management: Knowledge of SQL for querying and managing databases.

Big Data technologies: Familiarity with distributed computing platforms such as Hadoop and Spark for handling large datasets.

Statistical analysis: Strong statistical skills to design experiments, perform hypothesis testing, and interpret results.

Data visualization: Proficiency in data visualization tools like Tableau, Seaborn, or D3.js to communicate insights effectively.

Preferred non-technical skills include:

Communication: The ability to convey complex findings to both technical and non-technical stakeholders is vital for making data-driven decisions. In essence, data scientists need to be able to relay the story the data is telling.
Problem solving: While most of their decisions and proposed solutions are driven by data, data scientists need to have some measure of creative problem-solving ability to develop solutions that tackle complex, unstructured issues and address the “big picture.”

Domain knowledge: Understanding the industry or domain in which they work is essential for contextualizing data and insights.

Critical thinking: Analytical thinking to approach problems creatively and identify innovative solutions.

Teamwork: Collaboration with cross-functional teams, including IT, executives, and domain experts, is crucial for successful projects.

Strong sense of ethics: An understanding of ethical data practices and privacy regulations to ensure responsible data handling.

Continuous learning: The field of data science evolves rapidly; data scientists must stay updated with the latest tools and techniques.

Project management: Effective project management skills are required to plan, execute, and deliver data projects on time and within budget.
Business acumen: The ability to align data insights with business goals and strategies.

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.

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.

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…

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.

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.

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

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.