Nexford programs are flexibly paced—not self-paced. Study when it works for you, and stay on track with weekly deadlines. Live sessions are optional. Expect to be challenged: and plan to invest at least 10 hours per week. That’s what it takes to build real skills that deliver real career value.
Skills you will learn - and add to your résumé
The MS in Data Analytics develops your expertise in analyzing, visualizing, and interpreting data alongside core business skills to help you transform complex data into actionable business insights.
Analyze Business Data
Use data mining and statistical tools to uncover patterns and guide smarter decisions.
Program in R & Python
Write and adapt code in R and Python to clean, analyze, and automate data workflows.
Build Predictive Models
Apply machine learning algorithms to forecast outcomes and solve real-world problems.
Visualize Data Effectively
Turn complex data into clear, compelling visual stories that support business action.
Communicate with Stakeholders
Translate data insights into decisions through clear, confident communication.
Apply Data Ethics & Law
Ensure responsible use of data by applying legal, ethical, and privacy frameworks.
Analyze Business Data
Use data mining and statistical tools to uncover patterns and guide smarter decisions.
Program in R & Python
Write and adapt code in R and Python to clean, analyze, and automate data workflows.
Build Predictive Models
Apply machine learning algorithms to forecast outcomes and solve real-world problems.
Visualize Data Effectively
Turn complex data into clear, compelling visual stories that support business action.
Communicate with Stakeholders
Translate data insights into decisions through clear, confident communication.
Apply Data Ethics & Law
Ensure responsible use of data by applying legal, ethical, and privacy frameworks.
Courses in the MS in Data Analytics
The MSDA includes 12 required courses. Courses are designed to take two months, but you can finish in just one. You’ll begin with one course at a time and can increase your course load as you progress - at no extra cost.
Program Courses
Program Structure
You need 12 courses to complete your MS in Data Analytics
11 core courses
1 capstone project
Core Courses (11 required)
Business Analytics
This course is designed to provide learners with a comprehensive understanding of the fundamental concepts, techniques, and tools used in the field of business analytics. Learners will learn how to analyze data, make informed decisions, and effectively communicate their findings to stakeholders. This course will cover various analytical methods, including descriptive, diagnostic, predictive, and prescriptive analytics, as well as the role of data visualization in enhancing business insights.
The Art of Communication
What is the number one job skill American and global employees are lacking, according to LinkedIn CEO, Jeff Weiner? Communications. The Art of Communication is designed to strengthen and promote a leadership-driven communications model that extends from an audience-centered communications approach. How do leaders persuade shareholders? How do leaders negotiate with clients and business partners across cultures? How are the psychology and cognitive principles of negotiation influenced by audience awareness? This course introduces learners to a foundation in rhetorical awareness that will transfer through a variety of contexts, including corporate communications, public speaking, negotiation, and a range of executive leadership scenarios. Coursework includes genre analysis, professional writing, presentation design and delivery, and negotiation workshops. Learners interested in developing leadership communications skills in both public and private sectors should attend.
Programming in R & Python
This course in Python and R is designed to connect foundational coding knowledge to its practical application in a data analytics project framework. Each module builds Python and R programming knowledge using selected open educational resources reinforced with explanatory videos and demonstrations. Through authentic assessments, Learners will get hands-on practice using Python and R for data manipulation, data visualization, and statistical modeling as they solve data challenges. Learners will progressively build programming competencies, demonstrate assimilation through knowledge checks, and extend their foundational understanding of Python and R into a practice that resembles how R and Python are used in the workplace.
Data Modeling and Mining
This course provides an opportunity to learn about data mining methods and applications. In this course, learners will learn basic concepts and tools for data mining, including data sources, data cleaning tools, methods, mainstream algorithms for data mining, statistical modeling, and popular tools for mining structured data and unstructured data. Learners will also learn how data mining can be effectively used in various application areas and industries to drive decisions and actions. Learners will complete multiple competency-based assignments to demonstrate their abilities in data modeling and mining by using the tools introduced in this course.
Accounting and Financial Reporting
Accounting and Financial Reporting engages learners in applying the principles of corporate finance and accounting to business decision making. Course topics include financial statements and other reports, the basics of financial forecasting and budgeting, accounting analytics, and the communications and vocabulary related to financial statements. Learners reconcile financial transactions and accounting records and examine the implications of international differences in accounting frameworks and financial reporting requirements.
The Laws & Ethics of Information Technology
This course offers a comprehensive understanding of the rapidly evolving field of IT's legal and ethical aspects, exploring the interplay between technology, law, and ethics. Topics covered include intellectual property rights, data privacy, cybersecurity, digital rights management, social media, artificial intelligence, and the societal impacts of emerging technologies. Learners will delve into ethical considerations and legal frameworks, guiding IT decision-making.
Statistics for Business Analytics
This course provides learners with a comprehensive understanding of statistical principles and their application in the field of business analytics. Through a combination of theoretical concepts and practical exercises, learners will develop the skills necessary to collect, analyze, and interpret data, enabling them to make informed decisions in a data-driven business environment. Topics covered include probability theory, descriptive statistics, hypothesis testing, regression analysis, and data visualization. By the end of the course, learners will be equipped with the knowledge and tools to utilize statistical techniques effectively, communicate insights to stakeholders, and contribute to data-informed decision-making processes.
Data Sciences for Decision Making
Data Sciences for Decision Making provides a foundation for learners to apply advanced analytics skills to complex data analysis and modes. Learners build an understanding of design, data analytics tools, and advanced analytics translators to communicate complex data-related requirements between data engineers, business, and IT stakeholders. Learners examine four major areas. First, there are momentum gains in the data and analytics revolution. Advances in data collection, machine learning, and computational power have fueled progress due to an exponential growth in the volume of data, continual advances in algorithms, and greater computational power and storage. Second, there are five elements of successful data analytics transformation: cases/sources of value, data ecosystems, modeling insights, workflow integration, and adoption. Third, the mapping value in data ecosystems includes data generation and collection, data aggregation, and data analysis. Fourth, models of distribution are fueled by big data analytics as business models are enabled by orthogonal data, hyper scale, real- time matching, radical personalization, massive data integration capabilities, data-driven discovery, and enhanced decision making.
Information Visualization & Communication
Analyzing and presenting data visually has become one of the most important skills for professionals who work with data. This course demonstrates how to design effective interactive visualizations of complex data for data discovery, analysis, and presentation. Learners utilize the information visualization and communication knowledge, and techniques presented in this course to create visuals from the information. This course develops a holistic set of skills to ideate, design, implement, and evaluate sophisticated data visualization projects. Concepts including visual encoding, human perception and visualization techniques are introduced so learners can develop effective interactive data visualizations for analysis and presentation through the skills and techniques used in data visualization. The course is structured to leverage authentic and realistic data and use case studies and other resources from the workplace to demonstrate the value of information visualization and communication.
Corporate Sustainability
Before the start of the 21st Century, environmental management for a business was typically driven by the need to respond to restrictions imposed by environmental regulation; however, at the dawn of the new millennium, leading businesses began to change their concept of environmental management to look beyond simply meeting governmental dictates. In today's rapidly changing world, the need for corporate sustainability has never been more crucial. Companies that prioritize sustainability as a core tenet of their strategy are not only positively impacting the environment but also driving strong financial results. In the course "Corporate Sustainability," you will dive deep into the world of sustainable business practices and explore how corporations are making a significant impact on the environment. Through case studies and real-world examples, you will examine the strategies employed by successful businesses that have integrated sustainability into their DNA. Discover the exciting opportunities that sustainability challenges present for businesses, entrepreneurs, and individuals. By exploring emerging trends and technologies, you will be equipped with the knowledge to identify and seize these opportunities.
Applied Machine Learning for Business Analytics
This course is designed to provide learners with a deep understanding of the core principles, techniques, and applications of machine learning in the context of data-driven analytics. The course focuses on equipping learners with the knowledge and skills required to develop, implement, and evaluate machine learning models that can be used to solve complex real-world problems in various industries, such as finance, healthcare, marketing, and supply chain management. Learners will explore a range of machine learning algorithms, including supervised and unsupervised learning techniques, reinforcement learning, and deep learning frameworks. They will also learn how to preprocess and analyze data, select appropriate features, and optimize model performance using evaluation metrics and validation strategies. Additionally, the course will cover ethical considerations and best practices in developing and deploying machine learning models.
Capstone project (1 required)
Data Analytics Capstone
The Data Analytics Capstone allows the learner to apply what they learned about making data-driven decisions to a real business challenge faced by global technology companies alike. At the end of this capstone, learners will be able to ask the right questions about the data and know how to use data effectively to address business challenges of their own. They will understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions. Designed with companies in mind to provide invaluable experience in evaluating and creating data-driven decisions, the Data Analytics Capstone Project provides the chance to devise a plan of action for optimizing data itself to provide key insights and analysis, and to describe the interaction between key financial and non-financial indicators. Once learners complete their analysis, they'll be better prepared to make data-driven business decisions.
You need 12 courses to complete your MS in Data Analytics
11 core courses
1 capstone project
Business Analytics
This course is designed to provide learners with a comprehensive understanding of the fundamental concepts, techniques, and tools used in the field of business analytics. Learners will learn how to analyze data, make informed decisions, and effectively communicate their findings to stakeholders. This course will cover various analytical methods, including descriptive, diagnostic, predictive, and prescriptive analytics, as well as the role of data visualization in enhancing business insights.
The Art of Communication
What is the number one job skill American and global employees are lacking, according to LinkedIn CEO, Jeff Weiner? Communications. The Art of Communication is designed to strengthen and promote a leadership-driven communications model that extends from an audience-centered communications approach. How do leaders persuade shareholders? How do leaders negotiate with clients and business partners across cultures? How are the psychology and cognitive principles of negotiation influenced by audience awareness? This course introduces learners to a foundation in rhetorical awareness that will transfer through a variety of contexts, including corporate communications, public speaking, negotiation, and a range of executive leadership scenarios. Coursework includes genre analysis, professional writing, presentation design and delivery, and negotiation workshops. Learners interested in developing leadership communications skills in both public and private sectors should attend.
Programming in R & Python
This course in Python and R is designed to connect foundational coding knowledge to its practical application in a data analytics project framework. Each module builds Python and R programming knowledge using selected open educational resources reinforced with explanatory videos and demonstrations. Through authentic assessments, Learners will get hands-on practice using Python and R for data manipulation, data visualization, and statistical modeling as they solve data challenges. Learners will progressively build programming competencies, demonstrate assimilation through knowledge checks, and extend their foundational understanding of Python and R into a practice that resembles how R and Python are used in the workplace.
Data Modeling and Mining
This course provides an opportunity to learn about data mining methods and applications. In this course, learners will learn basic concepts and tools for data mining, including data sources, data cleaning tools, methods, mainstream algorithms for data mining, statistical modeling, and popular tools for mining structured data and unstructured data. Learners will also learn how data mining can be effectively used in various application areas and industries to drive decisions and actions. Learners will complete multiple competency-based assignments to demonstrate their abilities in data modeling and mining by using the tools introduced in this course.
Accounting and Financial Reporting
Accounting and Financial Reporting engages learners in applying the principles of corporate finance and accounting to business decision making. Course topics include financial statements and other reports, the basics of financial forecasting and budgeting, accounting analytics, and the communications and vocabulary related to financial statements. Learners reconcile financial transactions and accounting records and examine the implications of international differences in accounting frameworks and financial reporting requirements.
The Laws & Ethics of Information Technology
This course offers a comprehensive understanding of the rapidly evolving field of IT's legal and ethical aspects, exploring the interplay between technology, law, and ethics. Topics covered include intellectual property rights, data privacy, cybersecurity, digital rights management, social media, artificial intelligence, and the societal impacts of emerging technologies. Learners will delve into ethical considerations and legal frameworks, guiding IT decision-making.
Statistics for Business Analytics
This course provides learners with a comprehensive understanding of statistical principles and their application in the field of business analytics. Through a combination of theoretical concepts and practical exercises, learners will develop the skills necessary to collect, analyze, and interpret data, enabling them to make informed decisions in a data-driven business environment. Topics covered include probability theory, descriptive statistics, hypothesis testing, regression analysis, and data visualization. By the end of the course, learners will be equipped with the knowledge and tools to utilize statistical techniques effectively, communicate insights to stakeholders, and contribute to data-informed decision-making processes.
Data Sciences for Decision Making
Data Sciences for Decision Making provides a foundation for learners to apply advanced analytics skills to complex data analysis and modes. Learners build an understanding of design, data analytics tools, and advanced analytics translators to communicate complex data-related requirements between data engineers, business, and IT stakeholders. Learners examine four major areas. First, there are momentum gains in the data and analytics revolution. Advances in data collection, machine learning, and computational power have fueled progress due to an exponential growth in the volume of data, continual advances in algorithms, and greater computational power and storage. Second, there are five elements of successful data analytics transformation: cases/sources of value, data ecosystems, modeling insights, workflow integration, and adoption. Third, the mapping value in data ecosystems includes data generation and collection, data aggregation, and data analysis. Fourth, models of distribution are fueled by big data analytics as business models are enabled by orthogonal data, hyper scale, real- time matching, radical personalization, massive data integration capabilities, data-driven discovery, and enhanced decision making.
Information Visualization & Communication
Analyzing and presenting data visually has become one of the most important skills for professionals who work with data. This course demonstrates how to design effective interactive visualizations of complex data for data discovery, analysis, and presentation. Learners utilize the information visualization and communication knowledge, and techniques presented in this course to create visuals from the information. This course develops a holistic set of skills to ideate, design, implement, and evaluate sophisticated data visualization projects. Concepts including visual encoding, human perception and visualization techniques are introduced so learners can develop effective interactive data visualizations for analysis and presentation through the skills and techniques used in data visualization. The course is structured to leverage authentic and realistic data and use case studies and other resources from the workplace to demonstrate the value of information visualization and communication.
Corporate Sustainability
Before the start of the 21st Century, environmental management for a business was typically driven by the need to respond to restrictions imposed by environmental regulation; however, at the dawn of the new millennium, leading businesses began to change their concept of environmental management to look beyond simply meeting governmental dictates. In today's rapidly changing world, the need for corporate sustainability has never been more crucial. Companies that prioritize sustainability as a core tenet of their strategy are not only positively impacting the environment but also driving strong financial results. In the course "Corporate Sustainability," you will dive deep into the world of sustainable business practices and explore how corporations are making a significant impact on the environment. Through case studies and real-world examples, you will examine the strategies employed by successful businesses that have integrated sustainability into their DNA. Discover the exciting opportunities that sustainability challenges present for businesses, entrepreneurs, and individuals. By exploring emerging trends and technologies, you will be equipped with the knowledge to identify and seize these opportunities.
Applied Machine Learning for Business Analytics
This course is designed to provide learners with a deep understanding of the core principles, techniques, and applications of machine learning in the context of data-driven analytics. The course focuses on equipping learners with the knowledge and skills required to develop, implement, and evaluate machine learning models that can be used to solve complex real-world problems in various industries, such as finance, healthcare, marketing, and supply chain management. Learners will explore a range of machine learning algorithms, including supervised and unsupervised learning techniques, reinforcement learning, and deep learning frameworks. They will also learn how to preprocess and analyze data, select appropriate features, and optimize model performance using evaluation metrics and validation strategies. Additionally, the course will cover ethical considerations and best practices in developing and deploying machine learning models.
Data Analytics Capstone
The Data Analytics Capstone allows the learner to apply what they learned about making data-driven decisions to a real business challenge faced by global technology companies alike. At the end of this capstone, learners will be able to ask the right questions about the data and know how to use data effectively to address business challenges of their own. They will understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions. Designed with companies in mind to provide invaluable experience in evaluating and creating data-driven decisions, the Data Analytics Capstone Project provides the chance to devise a plan of action for optimizing data itself to provide key insights and analysis, and to describe the interaction between key financial and non-financial indicators. Once learners complete their analysis, they'll be better prepared to make data-driven business decisions.
What’s it like to learn at Nexford?
Real-world projects
Real-world projects
Build skills by doing. Every course includes hands-on projects that mirror real workplace challenges like building financial models plans, data dashboards, or marketing strategies. You won’t just learn about it - you’ll practice doing it. And projects are often curated from the world's largest organizations.
Flexible
Flexible
Collaborative learning
Collaborative learning
Work with peers from around the world on discussions that reflect global workplace dynamics. You won’t learn in a vacuum. Share ideas, get feedback, build a network - and be part of a global community that sharpens how you think.
Rewarding learning
Rewarding learning
Earn certificates and digital badges for the skills you build - long before you finish your degree. Every project moves you forward and every milestone counts. Progress isn’t hidden, its rewarded and you get to share that progress as you grow.
Personalized support
Personalized support
You’re online but never alone. Book 1:1 time with faculty or advisors, join group sessions, or reach out anytime by chat, email, or online calls. Whether it’s academics or career advice, real humans are here - or bots if you prefer.
Technology enabled
Technology enabled
Expect a modern, fully online experience that just works - including on mobile. All required tools are included, and you’ll never pay extra for textbooks or software. No clunky systems or boring PDFs. Just online learning, made simple.
Masters Degree in Data Analytics - Admission requirements
You can upload your documents 100% online today
Proof of Identity
Proof of Identity
As part of the admissions process, applicants are asked to provide a photo or scan of a government-issued form of identification and a passport-style photo or selfie.
Undergraduate degree transcript
Undergraduate degree transcript
Graduate level applicants will need to have completed a bachelor’s degree at an accredited institution in business or a business-related field with a Statistics course.
Proof of English Proficiency
Proof of English Proficiency
If your previous education was in English, your transcript will serve as proof of English proficiency. You can also provide scores from an approved qualification exam.
Where our learners work
Join us for a free virtual tour
Join us live to see how learning at Nexford works, hear from alumni, and get your questions answered by current learners, faculty, and staff.
Free online webinar
Get a live preview of Nexford courses and the tools our learners use. Hear from our team dedicated to your success.
Free online webinar
Get a live preview of Nexford courses and the tools our learners use. Hear from our team dedicated to your success.
Your dedicated career success platform
Your dedicated career success platform
Personalized Career Recommendations
Discover your strengths and uncover your personalized career paths with AI-powered personality and job-fit assessments.
Smart Resume & Interview Tools
Build standout resumes and get real-time interview feedback to ace your next interview.
1:1 Career Coaching
Book time with expert coaches for personalized guidance on job searches, networking, and long-term planning.
Access thousands of additional courses
Access thousands of additional optional courses & certifications to help you level-up at no additional cost.
Real projects you'll complete in your MS in Data Analytics
Quantium: Data-Driven Biz Insights
British Airways: Airline Data Solutions
Commonwealth Bank: Social Media Data Analytics
Quantium: Data-Driven Biz Insights
British Airways: Airline Data Solutions
Commonwealth Bank: Social Media Data Analytics
Your career outlook as an MS in Data Analytics graduate
Every industry values problem-solving leaders with critical and strategic thinking skills, now also powered by AI skills. These are real examples of roles held by our 4,000+ graduates.
Data Science & Analytics
- Chief Information Officer
- Data Analyst
- Insights Manager
- Analytics Manager
Business Intelligence
- BI Developer
- Data Visualization Specialist
- Reporting Analyst
- Business Intelligence Analyst
IT & Systems Analysis
- Systems Analyst
- Data Engineer
- Big Data Engineer
- BI Analyst
Risk & Compliance
- Risk Analyst
- Compliance Analyst
- Fraud Analyst
- Data Governance Specialist
67%
Employers planning to hire data analytics professionals in 2024
30%
Projected job growth in data science and analytics roles through 2032
70%
of organizations report talent shortages in analytics & data science
Sources: Earnest, GMAC (Graduate Management Admission Council), LinkedIn Insights / Emerging Jobs Report, World Economic Forum, Deloitte Insights, PwC CEO Survey, Gartner, IDC (International Data Corporation), Statista, eMarketer, Kauffman Foundation, Crunchbase, OECD, World Bank, UNESCO Graduate Outcomes Survey, IBM Research, Harvard Business Review, National Association of Colleges and Employers, International Labour Organization, HubSpot & LinkedIn Marketing Report, Glassdoor Salary Insights
Hear from our alumni and their employers
Nexford is accredited.
Accredited by DEAC
The International Accreditation Council for Business Education (IACBE)
Accredited by DEAC
The International Accreditation Council for Business Education (IACBE)
