If you're looking to enter a high-demand, high-impact field that offers competitive salaries, look no further than data science. According to Quanthub, open data science positions outnumber qualified data scientists and likely will continue to do so for some time. US Bureau of Labor Statistics data support that assessment, indicating that the job demand in data science should increase by 27.9 percent through 2026. That's more than seven times the growth rate for the overall job market.
If you are interested in data science and have the skills required to succeed in this field, now is a great time to consider pursuing a master's degree in data science. Graduates with a data science degree can apply their skills to many different fields, including technology, industrial manufacturing, financial services, and healthcare.
Do you have what it takes to build a career in data science? If you're unsure, read on; this article addresses many questions you may be asking yourself. It covers:
Data science is a computer-science discipline that exploits software, machine learning tools, and statistical practices and theory to collect, analyze, and interpret complex data sets. Successful data scientists are data-driven professionals who excel in data analysis, computer science, mathematics, and statistics.
Organizations rely on data scientists to interpret and leverage complex data to make critical decisions. Many data scientists hone their skills within a specialization area. There are good reasons for this: first, data science is a big field, impossible to master in its entirety. Second, an effective data scientist needs expertise not only in data practices but also in the enterprise to which their practices are applied. A data scientist employed to improve supply chain management, for example, can be more effective and productive if they understand supply chain as well as the database practices, statistical analyses, and reporting protocols they implement.
AI enables machines to imitate human functions (think Amazon's Alexa or Apple's Siri). AI offers many benefits to both consumers and organizations. Consumers use AI daily within their smart devices and streaming services, while organizations utilize AI to research and define opportunity.
From healthcare to retail, many career paths require AI. Some examples of job titles within AI are:
Big data analytics describes the collection and analysis of massive data sets, a process that enables organizations to identify opportunities and influence behavior. For example, organizations use data analytics to inform marketing practices, identify new revenue opportunities, and improve customer service. The demand for big data and data analytics is prevalent across industries. Job titles include:
Bioinformatics is an interdisciplinary specialty combining biology and computer science to drive biological discoveries. Professionals in bioinformatics typically spend time looking for patterns within sequences of DNA or protein. Bioinformatics is a growth field with high demand for professionals with real-world experience in biology, statistics, and computer science. Career options for those interested in bioinformatics include:
Also known as financial engineering, computational finance combines mathematics, statistical modeling, and computing to inform the finance field. Computational finance professionals apply expertise in financial data science and quantitative risk management; many pursue computer science or finance for their bachelor's degrees. Job focused on computational finance include:
Cybersecurity is the practice of protecting systems and networks against cyberattacks. Roles within cybersecurity offer competitive salaries and robust opportunities. With the growing threat of cyberattacks, the demand is rapidly increasing for cybersecurity professionals. According to the US Bureau of Labor Statistics, cybersecurity jobs should grow by 31 percent between 2019 and 2029. Cyber Defense Magazine projects a need for "1.8 million additional cybersecurity professionals to fill the workforce gap by 2022." Jobs that require cybersecurity skills go by a range of titles. Popular job titles within cybersecurity include:
Data engineers build data pipelines and organize data for analysis. A data engineer typically has deep experience in system architecture programming, database design and configuration, and interface and sensor configuration. To become a data engineer, you will need to complete a data science master's program and take courses that focus on programming skills. Data engineering jobs include:
Data modeling is an essential skill for a data scientist. Data modeling's primary goal is to ensure that complex data is stored and retrieved in the most efficient way possible. To become a data modeler, you will need to be an abstract thinker with skills in information science, applied mathematics, and computer studies. Here are some jobs that you can expect to find within data modeling:
In many ways, artificial intelligence (AI) and machine learning go hand-in-hand. Machine learning allows artificial systems to learn from data and complete human-like tasks. To get started in AI and machine learning, you need to have the necessary mathematics and computing skills. Jobs in machine learning include:
A master's in data science is a graduate-level degree covering the practice and theory of gathering, organizing, interpreting, and applying data. The best data science programs include coursework in high-level mathematics, data visualization, data mining, and IT ethics.
Many students enter these programs with previous education and professional experience in analytics, advanced technology, IT, computer science, engineering, and/or mathematics. Students who complete this graduate program in data science might also explore careers in computer science, data management, or information research.
An MS and BS in data science typically result in different career opportunities. Those with a BS in data science qualify for entry-level positions in tech-related fields, while those with an MSDS qualify for advanced roles with higher pay.
Bachelor's degree programs typically provide a foundation in such areas as linear algebra, data structures and algorithms, calculus, geometry, statistics, and computer programming. Master of Science programs assume students have this knowledge and so focus on higher-level content. Most master's programs require a capstone project, allowing students to gain real-world data science experience.
A PhD is the highest-level graduate degree in data science (it is sometimes referred to as a "terminal degree" for this reason). It qualifies holders for high-level professional roles in private enterprises and other institutions. Many PhDs engage in cutting-edge research, either at a university or other institutions (e.g., healthcare organizations, government agencies). A PhD can take six years to complete (it can take even longer, in fact; most schools set a six-year limit but will extend it for many candidates).
A master's degree is often a prerequisite degree for a PhD. Master's holders more frequently enter the corporate and nonprofit world in many of the roles identified earlier in this article. Full-time master's candidates can complete the degree in 18 months to two years (faster in an accelerated program); part-time students may take three years or longer.
The cost of an MS in data science varies depending on whether the school is private or public and the length of time one takes to complete the program. Graduate school doesn't have to break the bank, as there are plenty of affordable data science programs.
Students can find some of the most affordable online data science graduate programs at:
A master's in data science should boost your earning potential. Data scientists can earn salaries in the six-figure range; many such jobs require at least a master's degree. According to Labor Insight, the national median salary for a data scientist is $114,888. Bachelor degree holders average at $113,200, while those with a master's degree average $116,700. PhDs earn, on average, $116,900.
Enrollment into a data science master's program requires specific admission prerequisites. These prerequisites vary based on the program, but in general, these are the requirements you are likely to face:
Every data science program requires a unique set of standards for degree completion. Most data science programs require specific core courses that involve a final thesis or research project. Some universities, like the University of Washington, require a capstone project, while other universities, like Georgia Institute of Technology, require a practicum to develop real-world analytics experience.
A typical data science curriculum includes core courses related to mathematics, programming, data, and analytics. Most programs require a specified number of electives and completion of a final research project.
A typical MSDS core curriculum covers big data analytics, visual analytics, computing, statistics, and operations research. The core curricula of MSDS programs differ based on the specific program and specialty area you pursue.
For example, the curriculum at the University of Virginia (Main Campus) combines technical, quantitative, and philosophical study. Students complete the program in two years with 14 graded courses and 32 credit hours and take the following core requirements:
The MSDS at the Georgia Institute of Technology takes one-to-two years to complete and consists of 15 hours of core coursework covering big data analytics in business, visual analytics, computing, statistics, and operations research. The following core courses are required:
Most MSDS programs offer electives that allow students to focus their degree toward a specific area. For example, at Stevens Institute of Technology, students can choose from a list of interdisciplinary electives related to topics in business application, industry, or academia. These electives include:
Many MSDS programs require a final research project/thesis. For example, Indiana University and Oregon State University require a final capstone project, while Stevens Institute of Technology allows students to complete a final thesis for six credits as part of their electives.
Earning a master's degree in data science will provide you with the knowledge you need to excel in the field. From big data to data analytics, there are many different degree paths you can take while pursuing a master's in data science program.
Top Master of Science in Data Science programs include:
The best online Master of Science in Data Science programs include:
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