CareerExplorer’s step-by-step guide on how to become a data scientist.

Step 1

Is becoming a data scientist right for me?

The first step to choosing a career is to make sure you are actually willing to commit to pursuing the career. You don’t want to waste your time doing something you don’t want to do. If you’re new here, you should read about:

What do data scientists do?
Career Satisfaction
Are data scientists happy with their careers?
What are data scientists like?

Still unsure if becoming a data scientist is the right career path? to find out if this career is right for you. Perhaps you are well-suited to become a data scientist or another similar career!

Described by our users as being “shockingly accurate”, you might discover careers you haven’t thought of before.

Step 2

Exploring the Data Scientist Career Path

If you are interested in becoming a data scientist, there are several steps you can take to determine if this career path is right for you. Here are a few suggestions:

  • Learn about the field: Start by researching what data science entails and the various roles and responsibilities of a data scientist. You can read blogs, attend webinars, and watch videos to get a better understanding of what the job entails.
  • Assess your skills: Evaluate your technical skills and experience to determine if you have a strong foundation in programming languages, statistical analysis, and machine learning techniques. You can also take online courses or participate in workshops to build and enhance these skills.
  • Explore job opportunities: Look for job openings and descriptions for data science positions to get an idea of the qualifications and responsibilities required. Consider reaching out to current data scientists to ask questions and gain insights into the day-to-day work.
  • Participate in projects: Participate in data-related projects to gain practical experience in data science. You can join online communities or attend hackathons to connect with others in the field and work on real-world problems.
  • Reflect on your interests: Consider if you have a passion for solving complex problems using data, and if you are comfortable with the technical and mathematical aspects of the job. Reflect on your strengths and interests to determine if a career in data science aligns with your goals and aspirations.

By taking these steps, you can gain a better understanding of what it takes to be a data scientist and whether it is the right career path for you.

Step 3

Steps to Becoming a Data Scientist

Becoming a data scientist typically requires a combination of education, experience, and skill development. Here are some steps you can take to become a data scientist:

  • Get an education: Most data scientists have at least a Bachelor's Degree in Data Science, Computer Science, Statistics, or Mathematics. Consider pursuing a master's degree or Ph.D. to enhance your knowledge and credibility in the field.
  • Gain experience: Gaining experience in data analysis, statistical modeling, and programming is essential to becoming a data scientist. This can be achieved through internships, working on data-related projects, or participating in data science competitions.
  • Develop technical skills: Data scientists must have a wide range of technical skills, including proficiency in programming languages like Python and R, knowledge of statistical modeling techniques, and the ability to work with big data tools and technologies. Consider taking online courses or attending workshops to build and enhance these skills.
  • Build a portfolio: Create a portfolio of projects that demonstrate your skills and experience in data science. This can include examples of data analysis, statistical modeling, and programming projects that showcase your ability to solve complex problems using data.
  • Network and collaborate: Participate in data science communities and connect with other professionals in the field. Collaborate on projects and seek out mentorship to enhance your skills and gain valuable experience.

While formal education can be helpful in becoming a data scientist, it is possible to enter the field without a traditional degree. In fact, many data scientists have backgrounds in fields such as engineering, physics, and social sciences. Here are some ways to become a data scientist without formal education:

  • Gain practical experience: Practical experience is highly valued in data science. Work on personal projects or contribute to open-source data science projects to build up your portfolio and demonstrate your skills.
  • Take online courses: Online courses and certifications can provide valuable training in data science techniques and tools. Some of these courses are even offered by prestigious universities, such as MIT and Harvard.
  • Attend workshops and meetups: Attend data science workshops and meetups to network with professionals in the field and learn from their experience.
  • Build a network: Build a network of contacts in the data science community. Participate in online forums and discussion boards, connect with people on LinkedIn, and attend conferences to meet other professionals in the field.
  • Demonstrate your skills: Build a website or blog where you can showcase your data science skills and share your work with potential employers. Participate in data science competitions and hackathons to demonstrate your skills and gain experience.
Step 4

Master's Degree and Doctorate

Master's Degree
Getting a master's degree can be advantageous for those who want to become a data scientist. While a bachelor's degree in a related field such as computer science, mathematics, or statistics can provide a foundation for a career in data science, a master's degree can offer additional benefits. Here are some reasons why getting a master's degree can be advisable:

  • Advanced coursework: A master's degree program in data science will offer advanced coursework in topics such as statistical modeling, machine learning, and big data technologies. This can help build a strong foundation in data science techniques and tools.
  • Specialization: Master's degree programs often allow students to specialize in areas such as data engineering, data visualization, or data analytics. This can help prepare students for specific roles in data science and make them more competitive in the job market.
  • Networking opportunities: Graduate programs provide opportunities to network with other students, faculty, and professionals in the field. This can help students gain valuable contacts and mentorship opportunities.
  • Internship opportunities: Many graduate programs offer internship opportunities, which can provide practical experience in data science and help students build their portfolios.
  • Career advancement: A master's degree can make a candidate more competitive in the job market and open up opportunities for career advancement.

While a master's degree is not always necessary to become a data scientist, it can provide a significant advantage in terms of knowledge, skills, and job prospects. However, it's important to note that experience, skills, and a strong portfolio can also be valuable in securing a data science job.

A doctorate is not necessarily required to become a data scientist. While some companies may prefer candidates with a PhD, many data scientists have advanced degrees in related fields such as computer science, statistics, or mathematics, or have gained practical experience through data science bootcamps, online courses, or on-the-job training.

That being said, a doctorate can provide data scientists with a deeper understanding of statistical theory and advanced analytical techniques, as well as the ability to conduct original research and develop innovative solutions to complex data problems. It may also be necessary for certain specialized roles in academia or research.

Ultimately, the decision to pursue a doctorate in data science or a related field will depend on an individual's career goals, interests, and personal circumstances. It's important to weigh the time, financial, and opportunity costs of obtaining a doctorate against the potential benefits it may provide for your career.

Step 5

Specializations and Employment Opportunities

There are many different specializations within the data science field. Here are some common specializations:

  • Data engineering: Data engineering involves the design, development, and management of data systems and infrastructure. Data engineers are responsible for building and maintaining the systems that collect, store, and process data.
  • Data analytics: Data analytics involves the use of statistical methods and software tools to analyze data and extract insights. Data analysts typically work with structured data to identify patterns, trends, and relationships.
  • Machine learning: Machine learning involves the development of algorithms and statistical models that enable machines to learn from data and make predictions or decisions. Machine learning is used in a wide range of applications, including image recognition, natural language processing, and recommendation systems.
  • Big data: Big data involves the management and analysis of large and complex datasets that are too big for traditional data processing tools. Big data specialists are responsible for developing and managing the systems and tools needed to process and analyze these datasets.
  • Data visualization: Data visualization involves the creation of visual representations of data to help communicate insights and findings to non-technical stakeholders. Data visualization specialists are skilled in the use of software tools and design principles to create effective and impactful visualizations.
  • Business intelligence: Business intelligence involves the use of data to inform business decisions and strategy. Business intelligence specialists are responsible for identifying key performance indicators, creating dashboards and reports, and analyzing trends and patterns to inform business decisions.

These are just a few examples of the many specializations within data science. Data scientists may choose to specialize in one or more of these areas depending on their interests and career goals.

Employment Opportunities
There are many employment opportunities for data scientists across a wide range of industries. Here are some examples of employment opportunities:

  • Technology companies: Many technology companies, such as Google, Amazon, and Facebook, employ data scientists to analyze user data, develop machine learning algorithms, and improve product performance.
  • Healthcare: In the healthcare industry, data scientists may work to analyze patient data, develop predictive models for disease diagnosis and treatment, and improve patient outcomes.
  • Finance: In the finance industry, data scientists may work to analyze financial data, develop trading algorithms, and manage risk.
  • E-commerce: E-commerce companies such as Amazon, Alibaba, and eBay employ data scientists to analyze customer behavior, develop recommendation systems, and improve customer experience.
  • Marketing: In the marketing industry, data scientists may work to analyze customer data, develop predictive models for customer behavior, and optimize marketing campaigns.
  • Government: Government agencies such as the National Institutes of Health (NIH), the Centers for Disease Control and Prevention (CDC), and the National Aeronautics and Space Administration (NASA) employ data scientists to analyze data and inform policy decisions.

These are just a few examples of the many employment opportunities for data scientists. As more companies and industries recognize the value of data-driven decision making, the demand for data scientists is expected to continue to grow.

Step 6


There are many certifications available for data scientists that can demonstrate their expertise and enhance their job prospects. Here are some of the most popular certifications for data scientists:

  • Certified Analytics Professional (CAP): The CAP certification is offered by the Institute for Operations Research and the Management Sciences (INFORMS). It is designed to test the skills and knowledge of analytics professionals in areas such as data mining, predictive modeling, and statistical analysis.
  • Cloudera Certified Data Scientist: The Cloudera Certified Data Scientist certification is designed for professionals who work with big data technologies such as Hadoop and Spark. It tests skills in data preparation, machine learning, and model deployment.
  • IBM Certified Data Scientist: The IBM Certified Data Scientist certification is designed to test skills in data mining, machine learning, and data visualization using IBM technologies.
  • Microsoft Certified: Azure Data Scientist Associate: The Azure Data Scientist Associate certification from Microsoft is designed to test skills in machine learning, data engineering, and model deployment using Azure technologies.
  • SAS Certified Data Scientist: The SAS Certified Data Scientist certification is designed to test skills in data manipulation, predictive modeling, and statistical analysis using SAS technologies.
  • Google Cloud Professional Data Engineer: The Google Cloud Professional Data Engineer certification is designed to test skills in designing and implementing data processing systems using Google Cloud technologies.

These certifications can help data scientists demonstrate their expertise and credibility to employers, as well as increase their job prospects and earning potential. However, it's important to note that certifications are not a substitute for experience and practical skills, which are also highly valued by employers in the field of data science.

Step 7

Learning and Networking Resources

There are many resources available for data scientists to learn new skills, stay up to date with industry trends, and network with other professionals in the field. Here are some popular learning and networking resources for data scientists:

  • Online courses: Platforms such as Coursera, edX, and Udemy offer a wide range of online courses in data science, machine learning, and related topics. These courses are often taught by industry experts and can be a great way to learn new skills and gain practical experience.
  • Data science communities: Online communities such as Kaggle, Data Science Central, and Reddit's r/datascience are great places to connect with other data scientists, share knowledge and resources, and participate in data science competitions.
  • Conferences and events: Attending data science conferences and events can be a great way to stay up to date with industry trends, network with other professionals, and learn about new tools and technologies. Some popular data science conferences include the Data Science Summit, the IEEE International Conference on Data Mining, and the Strata Data Conference.
  • Meetup groups: Local data science Meetup groups are a great way to connect with other data scientists in your area, share knowledge and resources, and network with professionals in your field.
  • Online forums and discussion boards: Online forums and discussion boards such as Quora and Stack Exchange can be a great resource for getting answers to specific data science questions, learning about best practices, and connecting with other professionals in the field.
  • Professional organizations: Joining a professional organization such as the Association for Computing Machinery (ACM) or the Institute for Operations Research and the Management Sciences (INFORMS) can be a great way to network with other professionals in your field and stay up to date with industry trends.

These are just a few examples of the many learning and networking resources available to data scientists. By taking advantage of these resources, data scientists can stay current with industry trends, build their skills, and connect with other professionals in their field.