Data-driven decision-making is increasing the demand for Data scientists across all employment sectors. Businesses must use their data strategically, and Data scientists are key to unlocking competitive advantage and growing market share.
Though it’s possible to become a Data scientist right out of college, according to a 2021 survey, as many as 87 percent of data science and analytics professionals have at least a master's degree. In fact, approximately 39% of data science openings require applicants to have at least a Master of Data Science degree. If you’re considering how to become a Data scientist to advance your career, a master’s or PhD degree is not just a good investment—it’s essential to your success. Here’s what you need to do to make sure the discipline is right for you and pave your path into this influential and lucrative profession.
Data scientists usually fall into two camps – those who know math really well but don’t code efficiently, and those who are great coders but don’t have a solid foundation in statistics," said David Sullivan, Rice Alumni and Data Scientist at New Knowledge. "I can speak to both sides, so I am right in the middle – exactly where I wanted to be.
What is a Data Scientist?
Data science often gets confused with computer science. While the disciplines overlap, they're distinct fields with different applications. Data scientists are responsible for taking the raw “big data” made possible by advanced computing, cleaning it, structuring it, analyzing it, and transforming it into intuitive visual insights and recommendations. A Data scientist’s data-driven observations and recommendations help business owners and executives make effective decisions that impact growth, influence offerings, drive operational efficiencies, and improve overall performance. Data scientists are creative thinkers and collaborators who understand the valuable intersections between data, business, government, medicine and society.
Data scientists must be proficient in math and statistics, with strong communication and collaboration skills. They also need basic-to-intermediate programming skills (particularly Python and R) that allow them to develop machine learning based algorithms and query databases, among other responsibilities.
Data Scientist vs Computer Scientist
While both occupations operate on the cutting-edge of digital innovation, Data scientists and Computer scientists apply their skills differently. Computer scientists are versatile “generalists” with the depth and breadth of computational knowledge to ideate, develop and build a range of digital tech innovations, from large-scale machine learning algorithms (for example, a personalized recommendations engine) to the cutting-edge of cybersecurity solutions within military defense operations.
Data scientists love the answers enabled by computer scientists’ advanced, secure systems. They mine the systems built and connected by teams of engineers to capture and store big data, clean it, analyze it, and visualize insights and recommendations that companies and organizations then use to make effective decisions. They partner across functions--from strategy to product, marketing to operations--to iteratively evolve and improve.
Data Scientist vs Data Analyst vs Data Engineers
Within their occupation, Data professionals can also specialize in a particular area of focus or interest.
Data analysts are present across nearly every function - HR, operations, development, marketing and sales, and more. Traditionally, Data analysts focus on different types of databases, applications and privacy regulation, using SQL skills to query those databases, and then Tableau or Power BI to visualize and communicate sensitive data and insights to mid-level management.
Data engineers specialize in the secure infrastructure (the “railroads” or “highways”) that transports and stores data and are responsible for development to improve capture, storage, and security of diverse sources of complex data.
The ubiquity of big data across all industries and disciplines now requires Data scientists, who are not just computer scientists, statisticians or mathematicians, but who are well versed in all of these fields. Data scientists are increasingly synonymous with big data, machine-learning based algorithms and Python or R programming, mining and synthesizing big data to solve some of business’s and society’s biggest problems.
Becoming a Data Scientist: Education Requirements, Qualifications & More
Becoming a Data scientist requires an advanced understanding of machine learning algorithms, statistics, mathematics, programming, and data visualization. Though it’s possible to start working in the field with minimal training or experience, a Master of Data Science or advanced degree is often preferred, if not required, as professionals strive for promotion into mid-level or executive Data science roles.
What Education is Needed to Become a Data Scientist?
As with any technical field, data science requires a strong educational background to ensure that graduates are prepared for professional demands. A bachelor’s degree in computer science, data science, engineering, or machine learning will set you up for success, but it’s not the only way to get the background needed to fulfill a data science education requirement.
Bootcamps, specifically those that focus on building strong coding and programming skills, will help those with degrees outside of computer and data science prepare to enter the field. The Rice University Data Analytics & Visualization Boot Camp can help students upskill or reskill for data analysis, analytics and visualization roles.
However, Data Science is a discipline that rewards mastery. More than 39% of employers looking to fill data science roles prefer applicants with at least a master’s degree. That graduate-level experience ensures you have an understanding of the computational and statistical foundations of data science—the backbone upon which you’ll build your career.
Students who are certain they want to pursue the data science path might consider an online Master of Data Science, which will allow the flexibility to continue working while earning a master's part-time. Programs like Rice's online Master's in Data Science allow you to specialize in either Machine Learning or Business Analytics.
What Qualifications Are Required to Become a Data Scientist?
Typically, accredited university master’s programs will look for a four-year bachelor’s degree with strong math prerequisites. As a professional Data scientist, you’ll need a graduate-level understanding of the following subjects:
Foundational Computation & Math Skills
One prerequisite for a data science career is an understanding of the foundations of computer science and data analysis. You’ll need to understand advanced mathematics, statistics, computer programming, and statistical analysis.
Many of these skills can be learned through a bachelor’s degree in mathematics, computer science, or statistics. However, many non-Computer Science, non-STEM students and career changers with non-technical degrees are able to acquire these skills through bootcamps and/or self-study. For example, Rice offers dozens of highly-rated, free online non-credit courses ranging from Python Programming Essentials to Algorithmic Thinking, and more.
Deep Knowledge of Programming Languages
Programming may not be a daily responsibility for all Data scientists, but just over 70% of Data scientist job postings ask for Python programming skills, among other programming languages. Data scientists should have basic-to-serviceable programming skills to help mine and manipulate big data.
Practical, Real-World Data Science Skills
Many employers look for applicants that have demonstrated real-world experience solving data science problems. The best way to prepare yourself for a career in data science is to seek out projects that will help sharpen your skillset and build a portfolio.
These projects will give you experience working with different types of data ranging from structured and unstructured data sets to video and images. The more types of data you work with before entering the field, the better prepared you’ll be to take on different, more challenging tasks in the workforce.
In the Master of Data Science program at Rice, our students gain valuable project experience via the Capstone course. They put theory and knowledge into practice on real-world data science applications from business, government and non-profit sectors, all with the individualized support and backing of expert Rice faculty.
Data Visualization Skills
Part of every Data scientist’s job is to create intuitive visual presentations of the data they are managing and analyzing. This allows subject matter experts (from physicians and doctors, to business executives) to better understand the underlying data and strategic recommendations.
You’ll need a firm understanding of common data visualization tools like Tableau, PowerBI, Infogram, and Plotly. You can learn to use these programs on your own, but you’ll also gain hands-on experience with these platforms during the coursework for your Master of Data Science degree.
How Long Does It Take to Become a Data Scientist?
The amount of time it will take for you to become a Data scientist largely depends on the education path you choose, the speed at which you complete the program (full-time or part-time), and how many years of work experience you have. If you have a background in computer science and already have math and/or programming foundations, completing additional coursework and entering the workforce can happen more quickly.
However, if you’re working full-time while pursuing an online Master of Data Science degree, it may take two to three years to complete your master’s program.
Data Science Career Paths & Job Outlook
Demand for Data scientists is increasing by the year as more organizations recognize the power of big data to achieve competitive advantage. With the necessary education, prerequisites and/or experience, you’ll be able to enter the field and pursue different career options as a Data scientist. These include:
- Data Developers: Data developers are responsible for designing, building, and maintaining databases to ensure maximum efficiency and improved productivity.
- Data Engineers: Data engineers create customized data management tools, analytical systems, and databases that businesses can use to collect and store data. These roles are ideal for those with a strong computer science background.
- Data Architect: Data architects create the plan or blueprint for data engineers to follow when building data management frameworks.
- Machine Learning Engineers: Machine learning engineers design complex software programs and applications that allow databases, machines, and robots to interpret data and identify ways to run through processes more efficiently by learning from past performance.
- Business Intelligence Developer: Business intelligence developers identify ways for businesses to improve the data research process and share the information they receive through that analysis with businesses to implement long-term improvements.
- Statisticians: Statisticians interpret data, identify and develop new data sampling techniques, create new analytical methods, and aid data scientists and analysts in their efforts every day. They use their advanced mathematics skills to help businesses answer complex questions, looking at trends, and interpreting raw data.
These are just a few of the specialized career paths available to Data scientists across most industries. You’ll be able to follow your passions and choose a position in an industry that will help you reach your long-term career goals.
Become a Data Scientist: Pursue a Data Science Master Degree at Rice University
If you’re interested in becoming a Data scientist, investing in your education and portfolio is a great best place to start. Learn more about the online MDS@Rice program today. When you enroll, you’ll be able to take advantage of a curriculum that matches what employers want, work with the top experts in the fields of Machine Learning/AI and Business Analytics, and surround yourself with smart, driven peers who will push you to do your best work each day.