Data Science

Who Gets a Big Data Master’s Degree, Anyway?

Who Gets a Big Data Master’s Degree, Anyway?
As human data production grows, so too do opportunities to exploit Big Data for business and scientific discoveries. The US currently suffers a shortage of professionals in this crucial field. Image from Pexels
Lucien Formichella profile
Lucien Formichella January 29, 2021

If you are a data professional with two or more years of experience in analytics, engineering, or architecture, earning a master's in data science can lift your career to new highs.

Data Science Programs You Should Consider

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Projections indicate that humans will generate 463 exabytes of data per day by 2025. That’s about 500 billion gigabytes, enough to fill the hard drives of one trillion top-of-the-line MacBook Pros, which would create a stack about 10 million miles high (about 20 round trips to the moon).

Impressed yet? TL/DR: that’s a whole lot of data.

There’s gold hidden in all that data. That’s why companies seek, and often struggle to hire, professionals with the right data management skills. There simply aren’t enough qualified applicants to meet growing data needs.

This shortage raises the question: do you even need a master’s degree? Experts on KDnuggets, a leading site in data and analytics, debate whether prospective data scientists should pursue a graduate degree. The answer? Individual study, open coursework, and certifications can help a diligent student land a dream job. That said, a master’s provides excellent learning and job opportunities. For many, it is the surest road to success.

Those who earn a Master of Science in Data Science can benefit from alumni and corporate connections unavailable to self-taught students. Graduates pursue data careers and develop the skills for interdisciplinary applications in fields like finance and healthcare. Those with a master’s can land six-figure data jobs, including careers in:

  • Artificial Intelligence (AI) Career: Professionals in this field focus on creating systems that can perform tasks that would normally require human intelligence. This includes activities like learning, decision-making, problem-solving, and more. AI experts work on developing algorithms and models for applications like machine learning, robotics, and natural language processing.
  • Big Data Architecture: Big data architects design and manage massive, complex data infrastructures. They are responsible for creating the architecture of big data solutions and ensuring that the big data ecosystem is scalable, efficient, and secure. This involves working with large-scale databases, data processing frameworks, and cloud storage systems.
  • Business Intelligence: Careers in business intelligence involve analyzing data to help organizations make more informed business decisions. Professionals in this field use a variety of tools and methods to collect, process, and analyze data, creating reports, dashboards, and data visualizations to present their findings.
  • Data Science: Data scientists analyze and interpret complex digital data, such as the usage statistics of a website, sales figures, logistics, or customer engagement data, to assist in business decision-making. They use techniques from statistics, machine learning, and predictive modeling to extract insights from data.
  • Data Visualization: Professionals in data visualization focus on converting complex data sets into understandable, graphical representations. This field involves creating charts, graphs, and other visuals that help users see patterns, trends, and insights in data, often using tools like Tableau or D3.js or even ChatGPT. This role is crucial in making data accessible and actionable for decision-makers.

So, who gets a big data master’s degree, anyway? Read on to learn more about what it’s like to earn a data science master’s and where it can lead. This article covers:

  • What is data science?
  • Data science specializations
  • What is a master’s in data science?
  • Who gets a master’s in data science?
  • Requirements to complete a data science master’s degree program
  • Best Master of Science in Data Science programs
  • Best online Master of Science in Data Science programs

What is data science?

Data science is one of the main branches of computer science. Data scientists typically utilize:

  • Advanced Math: This encompasses a range of complex mathematical topics beyond basic arithmetic, including calculus, linear algebra, differential equations, complex variables, and discrete mathematics. Advanced math is foundational in many fields, particularly in science, engineering, and technology, where it’s used to model, analyze, and solve complex problems.
  • Cloud Computing: Cloud computing involves delivering various services over the internet, including data storage, servers, databases, networking, and software. Rather than owning their computing infrastructure or data centers, companies can rent access to anything from applications to storage from a cloud service provider, leading to flexibility, scalability, and cost efficiency.
  • Data Mining Techniques: Data mining is the process of discovering patterns and extracting valuable information from large datasets using statistical methods, machine learning, and database systems. It involves techniques for analyzing data, ranging from simple data aggregation and statistics to complex predictive and descriptive modeling.
  • Data Visualization: Data visualization is the practice of converting data into a graphical or visual context, like charts, graphs, and maps, to make data easier to understand and interpret. Effective visualization helps to detect patterns, trends, and outliers in groups of data and is essential in data analysis and decision making.
  • Engineering: Engineering is a broad discipline that involves the application of science and mathematics to solve problems. It encompasses various fields, including mechanical, civil, electrical, and chemical engineering, each focusing on the design, construction, and maintenance of systems, structures, and devices.
  • Multiple Programming Languages (Python, R, C++, and Java, for example): Proficiency in multiple programming languages means having the skills to write, understand, and debug code in more than one language. Each language has its unique use-cases and advantages: Python is known for simplicity and versatility, R for statistical analysis, C++ for system/software development, and Java for its portability and wide use in enterprise environments. Being skilled in multiple languages allows for greater flexibility and adaptability in various software development contexts.

While data scientist may be the job most linked to data science, it’s far from the only one.

Definition of data science

According to the tech company Oracle, data science “combines multiple fields, including statistics, scientific methods, and data analysis to extract value from data.”

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Data science specializations

While each item on this list is a data science specialization, it’s important to note that even specialized jobs require multiple skills. For instance, artificial intelligence and machine learning are related subjects, and professionals who specialize in one also typically utilize the other. Most of the sample positions come from Indeed, the job search website.

Artificial intelligence

What is artificial intelligence?

Artificial intelligence is the process of designing computers to think like humans. It involves creating algorithms and systems that enable machines to perform tasks that typically require human intelligence. This includes problem-solving, recognizing speech, understanding natural language, and making decisions. AI technology is used in various applications, ranging from everyday devices like smartphones and voice assistants to complex systems in healthcare, finance, and autonomous vehicles.

What jobs have this focus?

  • AI Architect: An AI Architect designs and oversees the infrastructure and strategy for implementing artificial intelligence in an organization’s systems. This role involves ensuring the AI architecture aligns with business requirements, integrating AI solutions with existing systems, and setting the vision for AI initiatives. It requires a deep understanding of AI technologies, data structures, and system integration.
  • AI Imaging Scientist: This professional specializes in developing and improving AI technologies focused on imaging, such as image recognition and analysis. They work in fields like medical imaging, surveillance, and automated inspection systems, where they develop algorithms to interpret and process visual data from images or video feeds.
  • AI Software Engineer: An AI Software Engineer develops software solutions that incorporate artificial intelligence. They work on creating AI models, coding, testing, and deploying AI applications. This role involves a blend of software development skills and knowledge of machine learning, neural networks, and other AI methodologies.
  • Robotics Engineer: Robotics engineers design, build, and maintain robots and robotic systems. They integrate various disciplines, including AI, to create intelligent machines capable of performing tasks autonomously or assisting humans. Their work spans across programming, mechanical design, and electrical systems to achieve functional robotics solutions.
  • NLP Engineer (Natural Language Processing Engineer): NLP Engineers specialize in developing systems that understand, interpret, and respond to human language. Their work involves machine learning, linguistics, and computer science to enable computers to process and analyze large amounts of natural language data, and can be applied in areas like voice recognition systems, chatbots, and text analytics.

Big Data and data analytics

What is Big Data and analytics?

Big data is just like regular data, except you need specific computational methods to interpret it. Those who work with big data and data analytics have excellent data mining and data visualization skills.

Analytics education is tough to describe with a single statement, given how much variation the field has. A bachelor’s in analytics can be all you need to qualify for great positions. Conversely, there can be significant overlap between data analytics and data science master’s programs. Master’s in business analytics degrees, such as the one at Tulane University of Louisiana, leverage data techniques for business solutions. It may be useful to determine your exact goals before pursuing a Big Data master’s.

What jobs have this focus?

  • Big Data Engineer: A Big Data Engineer is responsible for designing, implementing, managing, and optimizing data processing systems. They work with large and complex datasets and use various big data tools and technologies to convert raw data into usable information for data scientists and business analysts. Their role involves ensuring the scalability and efficiency of data storage and processing systems.
  • Business Analyst: A Business Analyst acts as a bridge between business problems and technology solutions. They analyze an organization’s processes, systems, and models, identifying areas for improvement and developing strategies to address business challenges. Business Analysts work closely with stakeholders to understand their needs and translate these into requirements for IT teams to implement.
  • Data Analyst: A Data Analyst collects, processes, and performs statistical analyses on large datasets. They interpret results to identify trends, create data visualizations, and provide reports that help businesses make more informed decisions. Data Analysts use a variety of tools and techniques to extract insights from data, often focusing on specific areas such as customer behavior, market trends, or operational efficiency.

Bioinformatics

What is bioinformatics?

This field combines biology and data. Its many applications include analyzing drug efficacy, studying climate change, and even engineering insect-resisting plants. This field involves the use of computational tools and statistical techniques to understand complex biological data, such as genetic sequences, metabolic pathways, or protein structures. By integrating biological data with computational methods, bioinformatics plays a crucial role in advancing scientific research, personalized medicine, agriculture, and environmental science.

What jobs have this focus?

  • Bioinformatics Analyst: A Bioinformatics Analyst specializes in analyzing complex biological data. They use computational tools and statistical techniques to interpret data from sources like genomic sequencing, protein structure analysis, or biological pathway modeling. Their role is crucial in understanding genetic information, discovering new biomarkers, and aiding in drug development.
  • Bioinformatics Programmer: A Bioinformatics Programmer develops software and algorithms specific to the needs of bioinformatics research and analysis. They write custom code and scripts to handle, process, and analyze large biological datasets, such as genomic data. Their work often involves improving the efficiency and functionality of bioinformatics tools and integrating various data analysis methods into cohesive software solutions.
  • Bioinformatics Software Engineer: Similar to a bioinformatics programmer, a Bioinformatics Software Engineer is more involved in the full life cycle of software development. They design, develop, and maintain robust software systems for bioinformatics applications. This includes not just writing code, but also working on software architecture, user interface design, and system optimization to ensure the software is scalable, efficient, and user-friendly for biological data analysis.

Computational finance

What is computational finance?

Computational finance utilizes mathematical processes to solve financial equations and determine whether to buy or sell at a given time. There are great jobs in finance for those with data backgrounds—especially those rooted in math and statistics.

What jobs have this focus?

  • Financial Modeler: A Financial Modeler specializes in creating detailed mathematical models for financial decision-making. Their models are used to simulate the financial performance of a company, investment, or any financial asset. They often work on forecasting future revenues, analyzing risks and returns, and evaluating financial strategies. Their role is crucial in guiding businesses and investors in making informed financial decisions.
  • Quantitative Analyst: Often referred to as a “Quant,” a Quantitative Analyst applies mathematical and statistical methods to financial and risk management problems. They develop and implement complex models used to price financial derivatives, assess market risks, or select profitable investments. Quants are typically found in banks, investment firms, or hedge funds and play a key role in high-frequency trading, portfolio optimization, and risk management.
  • Valuation Analyst: A Valuation Analyst specializes in determining the value of a business, security, or asset. They use various valuation methods, such as discounted cash flow analysis, comparative company analysis, or precedent transactions, to estimate the value of a company for purposes like mergers and acquisitions, investment analysis, or financial reporting. Their analysis helps companies and investors understand the worth of an asset, make investment decisions, and negotiate transactions.

Cybersecurity

What is cybersecurity?

Protecting data is challenging and essential. Cyber security professionals work to prevent data breeches and enhance security. Earning a master’s in cyber security can launch a great career.

Cybersecurity is a critical and evolving field focused on protecting data, which is both challenging and essential. Cybersecurity professionals dedicate their efforts to preventing data breaches and enhancing the security of information systems. They deal with a variety of threats including hacking, malware, and phishing, and employ a range of strategies from encryption to network defense to safeguard digital assets. Pursuing a master’s degree in cybersecurity equips individuals with advanced skills and knowledge, positioning them for a rewarding career in this vital sector. Plus, this higher education can lead to roles in various industries, ensuring the safety of sensitive data and combating the ever-growing spectrum of cyber threats.

What jobs have this focus?

  • Computer Information Systems Manager: This role involves overseeing an organization’s computer-related activities. A Computer Information Systems Manager is responsible for planning, coordinating, and directing all computer-related activities within a company. They manage the IT staff, coordinate technology-related matters with top executives, oversee computer system and software maintenance and upgrade schedules, and maintain information security.
  • Cyber Security Architect: A Cyber Security Architect designs and oversees the implementation of network and computer security for an organization. They develop the overall security architecture and ensure that the system is protected against unauthorized access, data breaches, and cyber attacks. This role involves staying abreast of the latest security trends and technologies, developing security policies and procedures, and working closely with IT teams to implement security solutions.
  • Information Security Analyst: An Information Security Analyst focuses on protecting an organization’s computer systems and networks. Their duties include monitoring networks for security breaches, installing and maintaining security measures like firewalls and data encryption programs, and developing organization-wide best security practices. They also often play a role in developing the organization’s disaster recovery plan, a process for responding to data loss from security breaches.

Data engineering

What is data engineering?

This specialty is focused on data-gathering infrastructure, rather than interpretation. It plays a large role in generating usable data. It plays a significant role in generating usable data for organizations. Data engineers design and implement robust systems to collect, manage, and convert raw data into a format suitable for analysis. This involves creating and managing databases and data storage solutions, ensuring the efficient and reliable flow of data through pipelines, and preparing data for analytical or operational uses. Their work is crucial in providing clean and well-structured data, which is foundational for data analytics and business intelligence.

What jobs have this focus?

  • Data Engineer: A Data Engineer focuses on the preparation and optimization of data for analytical or operational uses. Their responsibilities include building, testing, and maintaining scalable data management systems, ensuring that data flows smoothly from various sources to databases and data warehouses. They work with large and complex datasets, develop data set processes, and are often involved in the design of big data solutions using tools like Hadoop, Spark, and SQL.
  • Hadoop Developer: A Hadoop Developer specializes in designing, building, and maintaining systems and solutions using the Hadoop framework, which is used for processing and storing big data. They write, manage, and optimize Hadoop MapReduce jobs and scripts, and work with data storage and processing technologies like HDFS, HBase, Hive, Pig, and Sqoop. Their role is crucial in enabling businesses to handle large volumes of data efficiently.
  • Technical Architect: A Technical Architect, or IT Systems Architect, is responsible for designing and implementing information systems and technology solutions in alignment with an organization’s business goals. This role involves creating a comprehensive architecture for software solutions, including deciding on systems and platforms, ensuring that new technologies integrate with existing systems, and often overseeing the development process from a technical standpoint. They are also involved in problem-solving and decision-making regarding technical challenges and complexities.

Machine learning

What is machine learning?

Closely linked to AI, machine learning is the process of teaching technology to recognize patterns and make predictions—a good example is the Netflix algorithm that predicts what you might enjoy watching next.

What jobs have this focus?

  • Data Scientist: A Data Scientist is a professional skilled in extracting insights and knowledge from data. They use a blend of various techniques from statistics, machine learning, and data analysis to process, model, and interpret large datasets. Data Scientists help in identifying patterns, making predictions, and assisting organizations in making evidence-based decisions. They often work with complex data sets to derive actionable insights for business strategies, policy-making, or technological innovations.
  • Machine Learning Specialist: A Machine Learning Specialist focuses on designing, implementing, and maintaining machine learning models and algorithms. Their work involves using statistical techniques to create systems that can learn from and make predictions or decisions based on data. They are skilled in various aspects of machine learning such as supervised and unsupervised learning, neural networks, and deep learning. Machine Learning Specialists apply their skills in a range of applications, from natural language processing and image recognition to predictive analytics.
  • NLP Scientist (Natural Language Processing Scientist): An NLP Scientist specializes in the intersection of computer science, artificial intelligence, and linguistics, working to enable computers to understand, interpret, and respond to human language in a valuable way. Their role involves developing algorithms and models that can process, analyze, and generate human language, whether in text or spoken form. This includes tasks like language translation, sentiment analysis, and speech recognition. NLP Scientists play a crucial role in improving human-computer interactions and are instrumental in developing technologies like chatbots, voice assistants, and automated translation services.

Modeling

What is modeling?

Data modeling is the intricate process of organizing and juxtaposing data, serving as the foundation for most data science practices. It involves creating visual representations of data systems, defining how different data elements relate to each other and to the properties of real-world entities. This process not only helps in structuring and managing data but also plays a crucial role in designing databases and data warehouses. Effective data models facilitate easy data retrieval, consistency, and integrity, and are essential in developing sophisticated data-driven systems for analytics and decision-making. Through data modeling, complex data relationships are simplified, making it easier for data scientists and analysts to interpret and utilize the data effectively.

What jobs have this focus?

  • Business Analyst: A Business Analyst works at the intersection of business and technology to analyze and assess business processes, systems, and models. Their primary role is to understand business needs, identify how they can be improved, and determine technological solutions to business problems. Business Analysts are involved in gathering requirements, documenting processes, and communicating with stakeholders to implement effective business solutions. They play a key role in bridging the gap between IT and the business using data analytics to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders.
  • Data Analyst: A Data Analyst specializes in collecting, processing, and performing statistical analyses of large datasets. They translate numbers and data into plain English to help organizations understand how to make better business decisions. Their role includes using various tools and methods to analyze data, identify trends and patterns, create data visualizations, and provide detailed reports. Data Analysts are typically focused more on processing and interpreting existing data rather than developing new algorithms or predictive models.
  • Data Scientist: A Data Scientist is an expert in statistical analysis and data modeling, using advanced analytics technologies and scientific principles to analyze large and complex data sets. They not only focus on analyzing data but also on building predictive models and machine-learning algorithms to forecast future trends from insights. Data Scientists often have a strong background in computer science, statistics, and mathematics. They are skilled in coding and using a variety of analytics and data visualization tools, and they play a crucial role in making data-driven decisions and strategies for organizations.

What is a master’s in data science?

Data science degrees focus on applying analytics. According to a Burtch Works study. nearly 90 percent of data scientists hold at least a master’s. Students who get into data science master’s degree programs typically have an existing background in data science and work in the field.

How is an MS in data science different from a BS in data science?

Experience is the biggest difference between a BS and MS. Though core topics can have similarities, undergraduate programs, such as the one at University of Utah, typically focus on introductory courses that establish a foundation in data. MS programs usually build on this foundation and help graduates advance their careers.

How is an MS in data science different from a PhD in data science?

According to Burtch Works, 48 percent of data scientists have a PhD. Career trajectory is the main difference between a PhD and master’s. PhDs are research-focused and often lead to academic and research positions, though not always. PhDs are longer, often five years, though individual factors contribute to how long you’ll spend. Programs can involve direct work with professors, and perhaps the greatest draw of a PhD is the prospect of funding. It’s more common for schools to fund PhD candidates than master’s students.

How long does it take to get an MS in data science?

Full-time programs average two years of study, while part-time programs usually require three or more.

How much does an MS in data science cost?

An MS in data science, like most master’s programs, can be expensive. Top programs can easily cost over $100,000, and that’s not including the opportunity cost of missed work. Still, it’s not all bad news. An affordable online program usually costs under $30,000. The University of Texas at Austin keeps total tuition to $10,000.

Who gets a master’s in data science?

Though expertise can vary, MS applicants should already know how to analyze data and use it to improve decision-making. A firm grasp of core competencies and methodologies, including algorithms, data management, and statistical models, serves you well. While not every data science program has the same admissions standards, prospective students often use a master’s program to further specialize in their field or shift focus to a related area.

Academic background

Academic backgrounds among students in the University of Washington – Seattle Campus 2019 data science master’s class included:

  • Applied Mathematics
  • Computer Science/Engineering
  • Economics
  • Information Technology
  • Statistics

Recently, more schools have begun offering undergraduate data science programs, according to an article in US News & World Report.

Professional experience

Those who enter a master’s in data analytics usually have real-world experience in data analytics, engineering, or architecture across fields like information technology, cloud computing, finance, and even defense. University of San Francisco students have a median of 6 years of work experience, with an upper-range of 10. About one-third of students have a year or less experience. Naturally, each school has different demographics, but these numbers are decently representative.

Age range

There’s no set age range for a master’s in data science. University of California – Berkeley lists students as anywhere from 21 to 67. Considering that most have roughly two years of post-undergraduate work experience, you can expect many of your classmates to be in their mid-twenties—though many will be older.

Country of origin

Master’s programs are a huge draw for international students. The University of Washington class is 59 percent international, and the University of San Francisco is 44 percent, including students from China, India, Italy, and Eritrea.

Gender and racial demographics

White men take up most data science positions, like all STEM careers. Recently, numbers have been shifting at some schools. The University of Washington student body is nearly 60 percent female and 21 percent Asian American; however, only six percent of students are underrepresented minorities. The University of San Francisco data science student body is 71 percent US minority and 40 percent female.

Planning to get a PhD?

Though there’s no single path to admissions, PhD candidates can benefit from having a master’s. The University of Nevada – Las Vegas prefers students with a master’s in math or statistics. Worcester Polytechnic Institute looks for students who meet one of these three criteria:

  • Completed one or more professional certifications
  • Graduated from a relevant master’s or PhD program with a GPA of three or more
  • Worked in the field for three or more years

Requirements to complete a data science master’s degree program

Aside from curriculum requirements, certain master’s programs set timelines for students to complete their degrees. The standard track at Berkeley, which is designed to be completed while working, takes 20 months. The decelerated track takes 32 months, maximum. Not all institutions have strict time requirements.

Most schools ask students to complete a capstone project. Not just a graduation requirement, projects are an excellent way to boost your portfolio and market yourself to potential employers. On-campus students at University of Virginia, for example, begin work on their capstone in the second term, and have opportunities to collaborate with industry members.

Master’s in data science curriculum

Master’s in data science curricula can vary greatly by school and focus. Schools like University of Chicago offer data analytics programs that are essentially data science programs, so it’s important to go by a program’s curriculum outline rather than its title.

What follows is a general description of what you can expect to learn in a data science master’s.

Typical Master of Science in Data Science core curriculum

Remember, MSDS curricula aren’t standardized, so individual programs will vary. Core courses frequently include:

  • Advanced Algorithms: This course delves into complex algorithmic techniques and theories. It covers advanced topics like graph algorithms, sorting and searching, optimization algorithms, and algorithmic complexity analysis. The course aims to equip students with skills to design and analyze efficient algorithms for solving complex computational problems.
  • Artificial Intelligence: This course explores the concepts and methods behind artificial intelligence. Topics typically include knowledge representation, reasoning, machine learning, natural language processing, and AI application areas like robotics and expert systems. The course aims to provide an understanding of how to create systems that exhibit intelligent behavior.
  • Data Visualization: This course focuses on the visual representation of data. It teaches how to communicate information clearly and effectively through graphical means. Topics might include principles of design, tools for visualization, and techniques for turning complex data sets into understandable and insightful visual formats.
  • Machine Learning: This course covers the study and construction of algorithms that can learn from and make predictions on data. It typically includes supervised and unsupervised learning techniques, neural networks, support vector machines, and various other machine learning models. The course emphasizes the practical application of these techniques to real-world problems.
  • Natural Language Processing: This course is about enabling computers to understand, interpret, and respond to human language. Topics include speech recognition, text analysis, machine translation, and sentiment analysis. The course combines linguistics, computer science, and machine learning to process and analyze large amounts of natural language data.
  • Predictive Modeling: This course focuses on creating models that predict future outcomes based on historical data. It includes techniques for building, validating, and deploying models to make predictions. Topics often cover various statistical and machine learning techniques used in predictive analytics.
  • Project Management: This course teaches the skills and techniques needed to effectively plan, implement, and complete projects. Topics typically include project planning, risk management, budgeting, team leadership, and project lifecycle. The course aims to prepare students to manage projects efficiently, meeting their objectives within time and budget constraints.

You may also be able to pick a specialization track. As part of its master’s in data science, Stevens Institute of Technology offers:

  • Business Applications
  • Data Acquisition and Management
  • Data Security
  • Fundamentals of Data Science

Examples of Master of Science in Data Science electives

Contrary to their optional-sounding name, students must complete a certain number of elective credit hours to graduate. These can either concern your chosen speciality, or just be general electives. Tufts University students must complete one course in each topic:

  • Computational and theoretical aspects of data analytics
  • Data analysis and interfaces (one or both subjects)
  • Data infrastructure
  • Data science—either a course or a project

MSDS final project/thesis

Top programs may get institutions to sponsor students’ final projects. University of Washington students have worked with companies like:

  • Adobe
  • Boeing
  • Microsoft

Though there are subtle differences, “capstone,” “final project,” and “thesis” are three terms that often address the same goals. They allow students to showcase their abilities and benefit from real-world learning experiences. Examples of student projects include:

  • Predicting Soil Moisture with Machine Learning
  • Using Artificial Intelligence to Monitor Inventory in Real Time
  • Visualizing Gentrification in Seattle

Best Master of Science in Data Science programs

It’s impossible to say definitively which master’s program is “best” because each student has different needs. Still, knowing about well-established programs can be a useful starting point.

Top-ranked MSDS programs include:

  • Carnegie Mellon University
  • Columbia University
  • University of Rochester
  • University of Virginia
  • University of Washington

Best online Master of Science in Data Science programs

Deciding to earn an online master’s can drastically increase your options.

Top ranked online MSDS programs include:

  • CUNY City College
  • Indiana University – Bloomington
  • Stevens Institute of Technology
  • Tufts University
  • Tulane University of Louisiana
  • University of Oklahoma – Norman Campus
  • University of Illinois at Urbana – Champaign
  • University of Michigan – Ann Arbor
  • University of Virginia

(Updated on January 5, 2024)

Questions or feedback? Email editor@noodle.com

About the Editor

Tom Meltzer spent over 20 years writing and teaching for The Princeton Review, where he was lead author of the company's popular guide to colleges, before joining Noodle.

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