Data Science

What Do You Learn With a Master’s Degree in Data Analytics?

What Do You Learn With a Master’s Degree in Data Analytics?
You can be part of this revolution… if you have the skills, training, and credentials. A Master of Science in Data Analytics program offers all three. Image from Unsplash
Eddie Huffman profile
Eddie Huffman January 25, 2023

The expertise in data analytics you develop through a master's program can help you advance in such diverse fields as machine learning, data visualization, financial analytics, and bioinformatics.

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Forbes calls data “the oil of the fourth industrial revolution.” That’s an apt comparison: it’s the power source without which the rest of the revolution is possible.

Data science and analytics have shaken up the worlds of finance, medicine, business, and government, culling useful information from the 2.5 quintillion bytes of data created every day by 4.66 billion internet users. Data analysts help organizations identify problems and make important decisions about which investments to make, which products to develop, which markets to explore, and which customers to target.

“Gone are the days when traditional financial reporting, such as the profit-and-loss statement, balance sheet, cash flow, and simple variance analysis are enough,” says EY (formerly Ernst & Young), one of the world’s Big Four accounting firms. “Business leaders are now looking for in-depth insights that allow them to connect business activity to long-term value, model scenarios in real-time, and efficiently allocate resources.”

In medicine, data science and artificial intelligence allow medical personnel to focus more on personalized patient care and improved efficiency and profitability. “AI frees up doctors and other healthcare professionals to focus on the patient, where momentarily they are spending 75 percent of their time analyzing data and doing paperwork,” says Patrick Bangert, VP of AI at Samsung SDSA, which specializes in enterprise software solutions. “Patients have to wait four to six weeks, unaware of the results of a biopsy. If those are available instantly the treatment can begin sooner, leading to better outcomes.”

In business, Mastercard teamed up with Google to track retail sales based on transactions. “This shows that Google’s collaboration with financial services players is raising the bar for a new, innovative way of working,” according to the Accenture Banking Blog. “An understanding of the right data sources can drive new product design decisions.”

The U.S. government made a $10 million investment in data storage in 2021 “to consolidate their data analytics infrastructure into a single, flash-based storage system that will support the needs of grand-challenge data science,” according to VAST Data, the creator of the storage platform. The upgraded system should “unlock the secrets hidden within vast reserves of biological, population and health data.”

You can be part of this revolution… if you have the skills, training, and credentials. A Master of Science in Data Analytics program offers all three. What will you learn in a data analytics master’s program? This article answers that question. It also covers the following topics:

  • Why get a master’s degree in data analytics?
  • What will you learn in a data analytics master’s program?
  • Can you get a master’s degree in data analytics online?
  • Master of Science in Data Analytics admissions requirements

Why get a master’s degree in data analytics?

A decade ago, Harvard Business Review called data scientist “the sexiest job of the 21st century.” Ten years on, that assessment still holds, as data science and analytics continue to break new ground and open new career opportunities.

Who hires people with data analytics degrees? A study by Burning Glass, IBM, and the Business Higher Education Forum found the most job openings in:

  • Finance and Insurance: This industry relies on data analytics for risk assessment, fraud detection, portfolio management, and customer insights. Analysts help in developing strategies to minimize risk and maximize profits, optimizing investment portfolios, and enhancing customer service through personalized financial advice.
  • Professional, Scientific, and Technical Services: Firms in this sector utilize data analytics to drive research and development, improve service delivery, and foster innovation. Data analysts play a key role in analyzing market trends, consumer behavior, and operational efficiency to support decision-making and strategic planning.
  • Information: The information industry, encompassing media, telecommunications, and technology companies, hires data analytics professionals to manage and interpret large datasets. They help in understanding audience preferences, optimizing content delivery, improving network performance, and personalizing user experiences.
  • Management of Companies and Enterprises: Data analytics degree-holders support corporate management by providing insights into business performance, market trends, and strategic opportunities. They analyze data from various departments to aid in decision-making, improve operational efficiency, and drive growth strategies.
  • Manufacturing: In the manufacturing industry, data analytics is crucial for optimizing production processes, improving supply chain efficiency, and ensuring quality control. Analysts use data to predict equipment failures, streamline operations, reduce costs, and enhance product quality, leading to more efficient and competitive manufacturing operations.

Regardless of the field, most data analytics jobs share a few common requirements, according to Talend, a company that provides cloud data integration and integrity solutions for organizations. They include:

  • The ability to tell a story with numbers
  • Some coding experience, particularly with SQL
  • Communication skills and curiosity
  • The ability to effectively sort and clean data

Carl Howe is director of education at RStudio, which provides open-source and enterprise tools for use with the R programming language, a key analytics tool. He spoke to Talend about the importance of getting into the weeds when it comes to data: “One irony of both data science and analytics is that while you need to know a great deal about models and machine learning, you could spend a great deal of your time cleaning real-world data before you analyze it. It’s the old story of ‘garbage in, garbage out.’ You need clean data to work with before you can model it.”

Job prospects in data analytics include:

  • Analytics associate: Assists in analyzing data to identify trends, develop business insights, and make recommendations based on findings to improve business performance.
  • Big data analyst: Specializes in analyzing complex, large-volume data sets, or “big data,” to uncover hidden patterns, correlations, and insights to inform decision-making.
  • Big data analytics architect: Designs and implements big data solutions, creating the framework and systems that allow for the efficient analysis and processing of large data sets.
  • Big data analytics business consultant: Provides strategic advice to businesses on how to leverage big data analytics for competitive advantage, including identifying business opportunities and optimizing data-driven strategies.
  • Big data engineer: Develops, constructs, tests, and maintains architectures such as databases and large-scale processing systems, ensuring that data flows smoothly from source to database to analytics applications.
  • Big data solution architect: Designs and manages the solution architecture of big data applications, focusing on integrating various technologies and ensuring that the big data solutions align with business goals.
  • Business analyst: Evaluates business processes, uncovering areas for improvement, and develops solutions to enhance the company’s efficiency and profitability.
  • Business intelligence and analytics consultant: Uses data analytics and business intelligence tools to provide actionable insights that help companies make strategic decisions based on data.
  • Clinical or healthcare data analyst: Analyzes clinical or healthcare data to improve patient outcomes, enhance operational efficiency, and inform healthcare practices and policies.
  • Data analyst: Processes and performs statistical analysis on large datasets to help companies make informed decisions by identifying trends, patterns, and insights.
  • Data architect: Designs, creates, deploys, and manages an organization’s data architecture, defining how data is stored, consumed, integrated, and managed across the organization.
  • Data engineer: Focuses on preparing big data and building and maintaining the infrastructure that allows for large-scale data processing and analysis.
  • Director of analytics: Leads the analytics department, overseeing data analysis and interpretation to guide strategic decisions and policy formulations within an organization.
  • Financial analyst: Evaluates financial data and investment opportunities to provide recommendations on investment strategies, market trends, and financial planning.
  • IT analyst: Analyzes and designs information systems solutions to help organizations operate more efficiently and effectively, focusing on hardware, software, and network systems.
  • Market research analyst: Studies market conditions to examine potential sales of a product or service, using data analytics to understand market trends, consumer preferences, and competitive landscapes.
  • Metrics and analytics specialist: Focuses on developing and tracking key performance indicators (KPIs) and metrics to evaluate success and performance across various aspects of the business.
  • Operations research analyst: Uses advanced mathematical and analytical methods to help organizations investigate complex issues, identify and solve problems, and make better decisions.
  • Pricing analyst: Analyzes industry and consumer data to recommend pricing strategies for products or services to maximize revenue and market share.
  • Risk management analyst: Assesses and manages the financial risks faced by an organization, developing strategies to mitigate those risks through insurance, financial instruments, or operational changes.
  • Sales analyst: Examines sales data and trends to help sales teams optimize their performance, forecast sales, and assess the effectiveness of sales strategies and tactics.
  • Systems analyst: Evaluates and improves computer systems, software, and applications to ensure they meet the organization’s and users’ needs, focusing on system design and implementation.

Not only can a master’s degree in data analytics open career doors, it can also enhance valuable skills that extend beyond your profession. These include problem solving, communication, and leadership, according to SAS, a developer of analytics tools. If you’re intellectually curious, know something about programming, and have strong math skills and an analytical mind, this could be your field.

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What will you learn in a data analytics master’s program?

We’ve already looked at some of the big-picture lessons you’ll learn while earning a master’s in data analytics, such as problem solving, communication, and telling a story with numbers. Master’s programs offer a mix of hard and soft skills.

At Butler University, for example, students learn “technical skills, like predictive analytics and effective visualization techniques, and enhance their soft skills, like critical reasoning and ethical decision-making, through an interdisciplinary curriculum.”

Let’s take a deeper dive into the broad spectrum of coursework you can expect.

Core curriculum

First, the tech side. In addition to fundamental data analysis classes, expect to take such classes as:

  • Conceptual Foundations of Information and Data: Explores the theoretical underpinnings of information and data, including their nature, how they are structured, and how they can be effectively managed and utilized in various contexts. This course provides a framework for understanding the role of data in society and organizations.
  • Python Programming and Data Management: Teaches Python programming skills with a focus on data management tasks. Students learn to write Python code to automate data processing, manage data collections, and perform data cleansing and transformation operations.
  • Introduction to Data Mining: Introduces the concepts, techniques, and tools used in data mining. Covers methodologies for discovering patterns and knowledge from large datasets, including classification, clustering, association rule mining, and anomaly detection.
  • Advanced Analytics, Predictive Modeling, and Decision Making: Focuses on advanced techniques in analytics and predictive modeling to support decision-making processes. Topics include complex statistical methods, machine learning algorithms, and the application of these methods to solve real-world problems.
  • Programming and Data Structures: Covers the fundamentals of programming and the use of data structures to organize and process data efficiently. Topics include arrays, lists, stacks, queues, trees, and graphs, along with algorithms for searching, sorting, and data manipulation.
  • Applied Machine Learning: Provides practical experience with machine learning algorithms and techniques. Students learn to apply supervised and unsupervised learning methods to real-world datasets, evaluate model performance, and implement solutions that can adapt and improve over time.
  • Database Design and SQL: Focuses on the principles of database design and the use of SQL (Structured Query Language) for data definition, manipulation, and query. Topics include relational database models, normalization, SQL syntax, and database administration.
  • Harvesting, Storing, and Retrieving Data: Examines methods and technologies for data collection, storage, and retrieval. This includes web scraping, working with APIs, using database management systems, and implementing efficient data storage solutions.
  • Decision Management Systems: Explores the design and implementation of systems that automate and improve decision-making processes in organizations. Topics include rule-based systems, decision models, and the integration of analytics into decision management.
  • Data Management and Data Visualization: Teaches techniques for managing and visualizing data to communicate insights effectively. Covers data preparation, exploration, and the use of visualization tools and libraries to create interactive dashboards and reports.
  • Predictive Modeling: Introduces the concepts and techniques of predictive modeling. Students learn to build models that can predict future events or outcomes based on historical data, using methods such as regression analysis, time series analysis, and machine learning.
  • Big Data Analytics: Focuses on the challenges and technologies associated with analyzing big data. Topics include big data frameworks, such as Hadoop and Spark, and techniques for processing and analyzing large-scale datasets to uncover insights, trends, and patterns.

Electives and specializations

If you specialize in business analytics, classes may include:

  • Introduction To Business Analytics: This course provides an overview of business analytics, introducing the concepts, methods, and tools used to analyze business data and translate insights into strategic decisions. It covers foundational topics such as data analysis, statistical methods, and the role of analytics in solving business problems across various functions.
  • Marketing Analytics: Focuses on the application of analytical techniques to marketing problems and decision-making. Topics include customer segmentation, predictive modeling for customer behavior, campaign effectiveness analysis, pricing strategies, and digital marketing analytics. Students learn how to use data to inform marketing strategies, optimize marketing efforts, and enhance customer relationships.
  • Supply Chain Analytics: Examines the use of analytics in optimizing supply chain processes, including procurement, production, distribution, and logistics. The course covers forecasting, inventory management, demand planning, and logistics optimization. Students learn to analyze supply chain data to improve efficiency, reduce costs, and enhance operational performance.
  • Data and Visual Analytics: Teaches techniques for analyzing and visualizing data to make informed decisions. The course covers data exploration, analysis techniques, and the use of visualization tools and software to create meaningful representations of complex datasets. Students learn how to present data in a way that is both informative and engaging to support business decision-making.
  • Management Consulting: Provides an introduction to the field of management consulting, focusing on how consultants help organizations solve complex business problems and improve performance. Topics include the consulting process, problem-solving frameworks, project management, and communication skills. Students learn about the role of analytics in consulting projects and how to apply analytical thinking to develop strategic solutions for clients.

Some schools offer a health data analytics concentration with courses like:

  • Utilization of Health Data: This course focuses on the principles and practices of collecting, analyzing, and using health data for the purpose of improving patient care, health services, and policy making. It covers topics such as data quality, health informatics, electronic health records (EHR), and the use of data in clinical decision support, population health management, and research.
  • Healthcare Data Literacy and Analytics: Designed to equip students with the skills to understand, interpret, and analyze healthcare data. The course covers foundational concepts in data literacy, including statistical methods, data visualization, and the use of analytics tools and software. Students learn how to translate data into actionable insights for healthcare improvement and patient care.
  • Information Systems for Healthcare Management: Explores the design, implementation, and management of information systems in healthcare settings. Topics include health information systems architecture, EHRs, telehealth technologies, data security and privacy, and the role of information systems in supporting healthcare operations, strategic planning, and quality improvement.
  • Health Services Administration: Provides an overview of the management and administration of health services organizations, such as hospitals, clinics, and public health agencies. The course covers topics such as healthcare policy, organizational behavior, financial management, legal and ethical issues in healthcare, and strategies for improving healthcare delivery and outcomes. Students learn about the challenges and opportunities in managing healthcare services in a rapidly changing environment.

Other concentrations include statistics, management, artificial intelligence, data engineering, digital retailing, and computational data analytics.

Then there are electives that help deepen your data analytics skills. Sample courses include:

  • Applied Probability Modeling for Data Analytics: This course focuses on applying probability models to analyze and interpret data across various domains. It covers fundamental concepts of probability, distribution models, stochastic processes, and their applications in real-world data analytics scenarios. Students learn to model uncertainty, make predictions, and derive insights from complex datasets.
  • Bayesian Deep Learning: Explores the integration of Bayesian statistical methods with deep learning frameworks. The course covers Bayesian inference principles, probabilistic models, and how they can be applied to enhance the performance and interpretability of deep learning models. Students learn to implement Bayesian approaches in neural networks to improve decision-making under uncertainty.
  • Deep Learning with Big Data: Focuses on leveraging deep learning techniques to extract insights from large and complex datasets, known as big data. The course covers scalable deep learning models, data preprocessing techniques, and the use of frameworks like TensorFlow and Keras to build models capable of handling big data challenges.
  • Deep Neural Networks: Delves into the architecture, training, and application of deep neural networks. Topics include convolutional neural networks (CNNs), recurrent neural networks (RNNs), optimization algorithms, and techniques for improving model performance. Students gain hands-on experience in designing and deploying neural networks for tasks such as image and speech recognition.
  • Intelligent Systems and Robotics: Introduces concepts and techniques for designing intelligent systems and robots. The course covers artificial intelligence (AI) principles, machine learning algorithms, sensor integration, control systems, and robotics applications. Students learn about the role of data analytics in developing autonomous systems capable of performing complex tasks.
  • Probability: Provides a foundational understanding of probability theory and its applications in data analysis. Topics include random variables, probability distributions, expected values, variance, and the central limit theorem. The course emphasizes the application of probability concepts to model uncertainty and analyze data patterns.
  • Time Series Analysis and Forecasting: Focuses on the analysis of time-ordered data points and forecasting future values. The course covers techniques for modeling time series data, including ARIMA models, seasonal decompositions, and state-space models. Students learn to apply these methods to forecast trends, analyze seasonal effects, and make informed predictions in fields such as finance, economics, and environmental science.

Many schools require completion of a hands-on capstone project, such as working with a team to solve a business problem or creating a technical work proposal.

Master of Science in Data Analytics admissions requirements

Requirements vary from school to school, but the following are common:

  • A minimum GPA from your bachelors’ degree
  • Completion of classes in (or knowledge of) calculus, probability/statistics, basic linear algebra, and a high-level programming language such as Python, a key analytics tool
  • GRE or GMAT scores
  • Resume
  • An essay or interview
  • Two or more letters of recommendation from sources like a recent supervisor or professional colleagues.

Can you get a master’s degree in data analytics online?

Online master’s degree programs abound, offering part-time and full-time options and typically taking two to three years to complete. Here are a few of the many universities where you can obtain a data analytics master’s degree from the comfort of your own hometown:

(Last Updated on February 26, 2024)

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About the Author

Eddie Huffman is the author of John Prine: In Spite of Himself and a forthcoming biography of Doc Watson. He has written for Rolling Stone, the New York Times, Utne Reader, All Music Guide, Goldmine, the Virgin Islands Source, and many other publications.

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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