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Graduates of a master’s in data science earn salaries well above the national average. Discover the benefits of a master’s degree in data science.
Infamously dubbed the sexiest job of the 21st century back in 2012 by Harvard Business Review, data scientists are still in high demand across diverse tech sectors today. The Bureau of Labor Statistics (BLS) projects data science jobs will increase by a whopping 35% by 2032, particularly for those with master’s degrees.
A master’s degree in data science is still the gold standard: The majority of data scientists hold a graduate degree. Whether you have a background in data science, STEM, or an unrelated major, earning a graduate degree can qualify you for a next-level master’s in data science salary or promotion in a lucrative tech job.
What Can I Do With a Master’s in Data Science?
The starting salary for a graduate of a master’s in data science salary exceeds the average income nationally, which was $74,580 in 2022, according to the U.S. Census Bureau. Earning a master’s in data science can qualify you for roles including business intelligence developer and head of data science, with salaries ranging from $87,000 to $189,000, respectively.
Senior or supervisory roles in data science tend to draw the highest salaries, though income rates depend on factors like location, education, and experience. A master’s degree and extensive experience would likely be required for high-paying jobs such as data science director or chief data scientist, which yield annual median salaries of $165,000 and $172,000, respectively.
Even jobs with salaries considered “modest” by data science standards out-earn comparable positions in other fields and industries. Employees with a graduate degree in data science meet the minimum education requirement to become statisticians, machine learning engineers, or actuarial scientists, all of which earn more than $100,000 a year as a starting base pay.
Note: Salaries marked with an asterisk in the following table are based on 2023 data.
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Do I Need a Master’s for a Data Science Career?
Simply put, you should consider your own personal interests, skill set, and career goals when deciding whether to pursue a master’s in data science.
If you already earned a bachelor’s in data science, computer science, or STEM, earning a master’s in data science can qualify you for broader job opportunities and higher salaries. While bachelor’s degrees in data science are growing slightly in popularity, most employers still require or prefer candidates with a master’s degree for the most desirable data science positions.
Regardless of your major or background, you may qualify for a master’s in data science salary if you have hands-on experience implementing the latest and most in-demand programming languages like Python or R. You might be less desirable to employers if you are a less experienced candidate with a master’s degree.
Others pursue non-traditional paths toward a data science career. For example, you might advance your career with an adjacent computer science degree, particularly in a specialization like machine learning, DevOps, or artificial intelligence (AI), all of which are skyrocketing in popularity. You can also consider bootcamps to help develop your existing data science skills.
What Can I Expect From a Master’s in Data Science Program?
The best master’s degrees in data science help you develop advanced analytical and critical-thinking skills. Many offer optional concentrations like data engineering, business analytics, or AI. Courses cover subjects like applied statistics, machine learning, and data visualization techniques and applications.
Many master’s degrees in data science also include a professional development component, which helps students hone the skills they will need to further their careers in Big Data. Some degrees feature optional experiential learning opportunities, such as a paid internship. Most culminate in a capstone course, which typically consists of a final team project presentation.
Many schools now offer both in-person and online master’s in data science degrees, with a completion time of roughly two years, depending on whether you enroll full- or part-time. Online students can sometimes choose synchronous (live) or asynchronous (self-paced) course delivery. Common prerequisites for admission include calculus, statistics, and coding experience. While costs vary, some online programs offer set rates regardless of your location.
Programming Languages Needed for a Data Science Career
A mastery of the latest, most in-demand programming languages is critical for data scientists. The top jobs in data science commonly require candidates with advanced expertise in languages like Python, R, and SQL, but you may need additional programming skills depending on your individual employer and project requirements.
Python is a versatile, general-purpose programming language known for its simplicity, readability, and adaptability. It is popular because it is an easy-to-learn open-source language that runs on all major platforms.
Python is powerful enough to serve both machine-level data scientists and beginners. This programming language for data science can be used for machine learning, web development for site and software building, data analysis, and task automation.
R language also includes predictive analytics, which can help businesses forecast trends. Some of the key elements of R include machine-learning algorithms like linear regression, random forest, and neural networks.
SQL is a commonly used programming language for “relatable” databases and other storage-and-retrieval-based applications. SQL creates queries to pull information from data sets in tabular form, such as rows and tables. NoSQL stores and retrieves data from non-relational databases.
SQL is ideal for structured data, while NoSQL also works for unstructured or semi-structured data sets. The former is more commonly used in complex database management, such as organizing large corporate records or files online, and the latter is preferred for less structured web applications or mobile apps.
Java is among the most widely-used programming languages, popular for its speed, security, and reliability across multiple applications, including game development, cloud computing, and Big Data.
Java is known among programmers as being easy to use and extremely versatile. This programming language offers built-in features and libraries and can run on a variety of platforms, meaning programmers are not required to code each function from scratch.
C/C++ is a general-purpose programming language, ideal for applications that require fast and efficient performance like video games, operating systems, and online search tools. C/C++ is a low-level, object/oriented language widely preferred by data scientists in various settings.
C/C++ is similar to C# and Java, allowing code to be reused and adapted to multiple platforms. Aspiring data scientists typically do not need prior experience to start learning this portable and versatile programming language.
Julia programming language can be used for general-purpose computing but is especially popular for numerical systems and tasks. Data scientists might prefer Julia for tasks that involve scientific computing, medical or pharmaceutical research, robotics, or game development.
Julia includes a built-in feature that allows for faster testing of code, which means developers can write the code much faster than some other applications. This programming language is especially beneficial for coders in biotechnology, banking, and finance.
JS is the most widely used programming language, affecting the client-facing operations of nearly every website on the internet. It is known to be highly versatile and dynamic, making it user-friendly for both experts and emerging data scientists.
JS is used for a variety of interactive web pages, from games to virtual reality to mobile apps. For example, this ubiquitous programming language appears in multi-functional web tools like Facebook, Reddit, and Netflix.
MATLAB, an abbreviation for “matrix laboratory,” is a high-level language especially beneficial for computational and technical tasks. MATLAB is an increasingly popular choice for machine learning, data analytics, and AI.
MATLAB is especially preferred by data scientists in aerospace, communications, and electronics industries. Similar to Julia but with different syntax and matrix functions, some of the key features of MATLAB include computation, visualization, data analysis, and algorithm development.
Scala is another high-level, multi-purpose programming language, used primarily for organizing and processing Big Data quickly and efficiently. Since Scala is statistically typed, it supports both object-oriented and functional programming tasks.
Scala is renowned by data engineers for its reliability and elegant presentation, with modular and scalable components that can sort and retrieve Big Data queries and functions. Scala is still popular in the finance industry, especially for banking, brokerage, and trading sites.
Go is a high-level language with a syntax similar to the C series but with additional features. Go includes more memory safety than C/C++ and is concurrent, which means it supports multiple lines of code and can run multiple operations at the same time.
Go was designed to perform the same type of broad functions as Java, Python, and C/C++. This programming language is especially beneficial for large-scale projects such as building network servers, cloud storage systems, and media performance platforms.
Other Essential Data Science Skills
A wide variety of industries increasingly rely on data scientists to help manage Big Data. These professionals need advanced expertise in the following core skills to excel in their field.
Expertise in statistics and advanced math is critical to any data science position, which is why the best master’s degrees in data science emphasize prerequisites and graduate-level coursework in these subjects. Data scientists often come by these skills somewhat naturally.
However, applying these skills in the workplace requires additional critical thinking, problem-solving, and exploratory analysis. Candidates must be able to identify patterns and relationships between data sets and use these statistics to test and build new data solutions.
Data visualization is the process of interpreting information through visual means, often with a visual aid such as a chart or graph. Data scientists need excellent data visualization skills, and a knack for storytelling, to excel in this field.
Data visualization trains data scientists to use images and visual aids to track patterns and relationships in Big Data, but it is not enough for a data scientist to possess a comprehensive understanding of the information; they must also be able to create a clear and compelling narrative to explain their results.
While much fuss has been made about AI replacing Big Data professionals, employers remain committed to investing in highly skilled human resources. Regardless, they also prefer or require candidates who know how to incorporate basic machine learning and AI skills.
Machine learning involves supervised and unsupervised learning techniques and deep-learning algorithms. These skills can be paired with AI and help data scientists determine whether machine learning resources might benefit a particular client and how to deploy those resources, if necessary.
A specific type of AI, deep learning models are trained to process information like humans. This type of machine learning is increasingly valuable for its ability to improve through repetition, critical thinking, and comparison. Data scientists can expect to use these skills often at work.
Deep learning embodies features like natural language processing, image recognition, and text-to-image translations. Expertise in deep learning is sought-after by most tech employers, especially among finance and customer service companies with complex processing requests.
Candidates for data science jobs must understand natural language processing (NLP), which is a subfield of AI that enables computers to process and generate “human-like” speech. Many companies using customer-facing AI aspire to perfect their chatbots’ NLP skills.
Data science job candidates with NLP expertise can go far in a variety of industries and fields. Entities preparing to launch or already using AI technology, from finance to healthcare to education, increasingly require advanced machine learning and NLP expertise.
Data scientists should be intimately acquainted with Big Data, which describes the large sets of data acquired by a business or organization. Most data scientists routinely analyze, process, and store Big Data as part of their primary job responsibilities.
Though Big Data is the general material with which a data scientist works, data scientists must use a variety of skills, such as math and statistics, machine learning, and data visualization, to manipulate the data. Part of a data scientist’s job is also to determine which method of Big Data processing is appropriate for a particular client or project.
Cloud computing skills are necessary for data scientists to understand how to manage Big Data. This field of data science requires programming know-how, platform and database expertise, and cloud security proficiency.
Retail and e-commerce, government, and entertainment platforms rely heavily on cloud computing techniques and require strict cloud security measures. Candidates should also be trained in DevOps, which is a type of software engineering that is typically deployed by cloud-based systems.
Essential Soft Skills for a Data Science Career
Data scientists require a unique combination of technical skills and business savvy to attract the best jobs. While anyone can pursue a master’s degree in data science, employers prefer candidates with highly developed soft skills, especially when it comes to business, communication, and teamwork.
Candidates with soft skills specific to data science are even more desirable to some employers. Data science careers attract employees with a sense of curiosity and scientific method expertise, as well as an interest in data ethics and an awareness of the global impact of Big Data. Problem-solving and critical thinking are also especially crucial to a data science career.
Frequently Asked Questions About Master’s in Data Science Programs
The choice between a master’s degree in data science or an MBA is personal and depends on your individual career goals. While both degrees offer professional development opportunities, graduates of an MS in data science are better equipped for technical jobs in Big Data, whereas MBA candidates can apply their business skills in a variety of career fields.
Most master’s degrees in data science require a GPA between 2.5 and 3.0 for full admission, though some colleges and universities may allow conditional admission for applicants with GPAs below the minimum requirement. Conditional admission allows students into the program, provided they meet certain other prerequisites upon admission or within a set period, such as additional coursework.
While data science and computer science majors cover some similar coursework, a data science degree typically provides more technical expertise in statistics and analytics, whereas a computer science degree might prepare graduates for broader coding and programming jobs. Your individual career goals should dictate which degree is right for you.
As the minimum education requirement for high-level positions in Big Data, a master’s degree in data science can open the door to a wide array of jobs and fetch a lucrative master’s in data science salary; however, a Ph.D. is often required for data-related jobs in academia or scientific research. You should consider which type of career you want before choosing a degree.
Preferred programming languages vary among data scientists, depending on their individual client needs, skills, and experience. While some data scientists might require more specialized languages for certain projects, they should be well-versed in popular languages like Python, R, and JavaScript, as these languages are commonly used in many platforms and applications.
Note: The insights on this page — excluding school descriptions — were reviewed by an independent third party compensated for their time by BestColleges. Page last reviewed June 20, 2024.
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