Online Program at a Glance

In the Katz School’s online Master’s in Data Analytics and Visualization, you’ll learn to transform raw data into insightful output to be used by man and machine. Build data solutions and products, starting by translating real-world phenomena into “systems of systems.” Master the underlying science of predictive and exploratory analyses, as well as the techniques for creating compelling visualizations and data narratives.

Learn tools like Python, SQL/NoSQL, Tableau, AWS, and AutoML. And, unlike other data analytics programs, you’ll experiment with basic AI and machine learning techniques.

Key Dates and Deadlines

Priority Submit Deadline
June 29, 2020
Final Application Deadline
August 3, 2020
Classes Start
September 8, 2020

Who Should Apply

Just breaking into analytics?

No matter which industry you choose, you know data is in your future. Master the practice and science of data analytics, and transition into a high-paying and fulfilling career.

Looking for bigger opportunities?

If you’re already doing related work, a graduate degree can help you gain recognition at your current job, move to a new company, or increase your earning potential.

Need convenience and flexibility?

Finding the time to come to campus can be a challenge. With an online master’s program at Yeshiva, you can earn your degree from anywhere, on your schedule.

Learn more about scholarship opportunities for Spring 2020

Schedule A Call

Program Objectives

Set up for career success

Learn today’s in-demand techniques and technologies – to prepare for tomorrow’s emerging trends. Gain a competitive edge and outpace the competition.

Impact your company

Develop your skills and confidence, and enhance the contributions you make in your organization. Take a leadership role and increase your earning potential.

Follow your passions

Work on problems that matter to you; from creating a recommender system for Amazon, identifying the most profitable products and markets, or analyzing data to make cities smarter.

Study on your schedule

With convenient and flexible online classes, you can complete your degree at a US News Top 100 University – after work, on the weekend, at home, or on the road.

Curriculum and Coursework

The MS in Data Analytics and Visualization is a 10 course (30 credit) program. Each semester is 15 weeks and students will take one or two courses per semester. You will complete your degree in as quickly as 18 months.

Analytics Programming

Code-based solutions can be richer, more accurate, and more flexible than those that rely on off-the-shelf software and analytic packages. This course teaches the programming skills that data analysts need to prepare structured and unstructured data for downstream analysis. Students will learn to use high-level programming languages to create rich data analysis workflows.

Visual Design and Storytelling

Analysts must present their data in effective and compelling visualizations. This course combines the best heuristics for data presentation with hands-on experience in creating spreadsheet charts and data visualizations from a variety of source data. Students will learn how to combine text and visualizations to craft stories that promote deeper engagement with data analyses and conclusions.

Computational Math and Statistics

Deeper math literacy and computational thinking are essential for deeper data literacy. Probability, statistics, and mathematics—especially fundamental linear algebra—are critical to the success of data analysts as they implement increasingly complex solutions. This course is designed to give the non-mathematician practice using mathematical and statistical computational methods in the service of data analytics solutions.

Structured Data Management

Organizations require reports and analyses that are both accurate and useful. This course emphasizes the skills that database developers rely on to create information architectures and reporting databases, manage data sources, perform data integration, and create data reports. Students will gain the essential theory and hands-on practice they need to manage data in support of organizational decisions.

Data Science

Recent fast-paced tool development and abstraction now allow motivated data analysts to perform useful and rigorous predictive analyses using high level languages and their rich scientific ecosystems. This course will cover classification, regression, and clustering methods, and students will apply these methods in designing, modeling, and building model applications that use natural language processing and recommender systems.

Data Product Design

Successful entrepreneurs and consultants create value. Data analysts who can work alongside or act as value architects create more organizational value, more quickly. Today, this means using data, analysis, and experimentation to better understand customer goals and preferences. In this course, students learn analytical frameworks for using data in the service of customer insight, customer development, value proposition refinement, and product development.

Data Driven Organizations

The best data analysis projects are implemented in the context of an organization’s business model, culture, key strategic initiatives, and processes. Data analysts who understand these contexts are more likely to see their efforts lead to improved organizational processes and/or decision-making. This course examines three important organizational-level analytical frameworks and emphasizes using data, analysis, and experimentation within each of these frameworks. Students will also be introduced to centralized data warehouses.

AI Studio

Design and build AI and machine learning applications using methods such as supervised learning, unsupervised learning, transfer learning, and reinforcement learning. As part of the studio, you will identify a problem or need, frame a solution, gather requirements and then design and build a functioning prototype using automated cloud-based tools. Projects will be showcased to the university community, and the top three will receive an award.

Information Architectures

Organizations combine data from many different sources, including spreadsheets, databases, and data warehouses. As the volume, variety, and velocity of data increases, more enterprise data is stored in cloud-based distributed data stores. In this course, students will learn to design, populate, and report on these enterprise data architectures.

Capstone Project

Integrates the skills developed in the previous classes into a comprehensive body of knowledge and provides tangible evidence of analytic and visualization competencies.


Complete an internship in a different industry or in your own company. You’ll work closely with your faculty mentor to develop a project that relates on-the-job experience to the science of data analytics.

Meet Our Faculty

Andy Catlin, Professor and Program Director

Andy Catlin is an entrepreneurial data scientist who focuses on helping groups of organizations in the same market space create value from data-driven collective intelligence. He’s built or co-founded successful start-ups in business intelligence and management analytics. His customers have included Union Bank of Switzerland, Nestle Purina, the National Football League, TV Guide Magazine, Reuters, and Microsoft. Andy teaches graduate courses in analytics programming, information architecture, recommendation systems, and neural networks, and has mentored numerous start up founders and CIOs.

Jeffrey Nieman, Data Scientist, Ford Motor Company

An expert in operations, project management, and data analysis, Jeff Nieman has decades of experience wrangling, analyzing and visualizing data for major corporations including Cru Global, Cisco, and Ford Motor Company. He has also taught programming in the graduate data analytics program at the City University of New York School of Professional Studies. Jeff has a B.S. in Chemical Engineering from the University of Michigan, and an M.S. in Data Science from the City University of New York.

Joy Payton, Data Scientist, Educator, and Engineer, Children’s Hospital of Philadelphia

Joy Payton supports researchers in mastering the tools of reproducible, computational research. She is an expert in curriculum development and web design. Joy has a B.A. in Applied Mathematics from Agnes College, and an M.S. in Data Science from the City University of New York. She has also done advanced graduate work at Pennsylvania State University, and Comillas Pontifical University in Madrid.

The Job Market

There are more than 2.3 million data analytics and data science jobs today, and the number is growing fast. But these positions remain unfilled because analytics professional with advanced tech skills are hard to find.

2.3 Million

Total Job Postings


Salary Range


Projected Five-Year Growth

Frequently Asked Questions

Modern businesses need skilled experts to sift through and interpret mass amounts of data in order to inform strategy and help guide decisions.The Katz School’s MS in Data Analytics and Visualization focuses on data management, modeling, interpretation, and reporting, as well as algorithm development and data science. Some career outcomes for this field include: Data Analyst; Data Scientist; Data Product Manager; Startup Founder; Business Intelligence Analyst; Strategy Analyst.

The MS can be completed in 1.5-2.5 years. To earn your degree, you must complete 10 courses, including a capstone project. You may substitute an internship as an elective, with Program Director approval.

The program is offered part-time only, 6 credits per semester. You should expect to spend 6-8 hours each week per course. In addition, each course will meet live online weekly for approximately 60-75 minutes; typically held during weekday evenings Eastern Standard Time. Meetings will be recorded in case you have to miss one.

Students in the program will participate in classes with an average class size of 15 students.

The total cost of tuition is $1,090/credit (total of $32,700). There is also a $55 application fee, $65 registration fee, and $125 technology fee.

The current deadlines are listed below:

  • April 15: Priority Scholarship Deadline
  • May 31: Priority Application Deadline (final date to use the application fee waiver code earlyadmit)
  • July 10: Final Application Deadline

You are not required to submit GRE or GMAT scores, but you will be required to demonstrate quantitative aptitude through satisfactory completion of a diagnostic exam, which must be submitted as part of the application.

Yes. The resume should list a candidate’s relevant work experience, internships, research, awards, and/or publications, as well as educational history. A curriculum vitae (CV) is also accepted.

No, however our most competitive applicants have at least a 3.5 GPA. Applicants who have lower than a 3.0 GPA are encouraged to submit an optional statement discussing the lower GPA and how they plan to be successful in this program.

You must submit two letters of recommendation. We prefer one professional reference and one academic reference from a faculty member or academic advisor who is familiar with your work. If you have been out of school for more than two years, it is acceptable to submit two professional references.

You can apply without US citizenship, however we are unable to sponsor student visas for online programs.

Application fee waivers are available for applicants who qualify for one of the following:

Service-based Fee Waivers

Application fee waivers are available for applicants who have participated in one or more years of full-time service in the U.S. Military, AmeriCorps, Peace Corps, City Year, or Teach for America. Documentation of proof of service is required.

Affiliate Fee Waivers

Students, staff, and alumni receive a fee waiver upon request.

Need-based Fee Waivers

If a student is a U.S. citizen or legal permanent resident with demonstrated financial hardship, they may request a waiver based on financial need. They must be able to provide proof that paying the fee would cause financial hardship. Acceptable documentation includes, but is not limited to:

  • Income tax returns
  • Current student financial aid statement
  • Proof of income statement

Applicants should submit a request for a fee waiver to with proper documentation. Waiver requests will be reviewed by the Office of Admissions and response notification will be sent within 10 business days.

The Katz School’s deferral policy allows a student to defer their start date one time within one year of the original cohort they were admitted into. In order to defer, a student must first pay their deposit. After the deposit is made the student can then defer their start date. Students who are unable to meet the deferral policy must re-submit their application.

Acceptance of transfer credits for graduate courses completed at another institution is determined by the academic department during the application review process.

After your application file is complete and all required information from your application checklist has been received, the Graduate Admissions Committee will review your file and make a decision. Decisions are made within a week or two after submission.

You may apply to a graduate program during your last term of undergraduate study. If you are admitted, you will be required to submit an official transcript conferring your undergraduate degree within your first semester of study.

All applicants are automatically considered for scholarships and graduate assistantships. You do not need to submit any additional information. Awards are determined during the application review process. Please contact us if you have questions about financial aid opportunities and financing your graduate degree. We would be happy to conduct a preliminary transcript review and discuss your admissions and financing options.

Yes. The Katz School’s online MS in MS in Data Analytics in Visualization is also offered on campus. To learn more visit: MS in Data Analytics in Visualization.

Earn your Master’s Degree online at Katz

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