Master of Science in Data Analytics
Buffalo, USA
DURATION
1 up to 2 Years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
Request earliest startdate
TUITION FEES
USD 910 / per credit *
STUDY FORMAT
Distance Learning, On-Campus
* for 40 credit hours/Scholarship available
Introduction
Learn to use big data to help companies make better and faster decisions and operate more efficiently. The data analytics master’s degree at Canisius University offers real-world learning opportunities, strong industry connections, and flexible course delivery options to fit your schedule. When you graduate, you’ll be prepared to work as a data specialist across a range of industries and organizations.
Prepare for High-Demand Careers as a Data Specialist
If you’re a keen problem-solver who enjoys working with numbers and is looking to jump into a constantly evolving, in-demand field, consider earning your master’s in data analytics. At Canisius, you will:
- Learn how to analyze large data sets and apply data knowledge within a field of specialization
- Gain programming skills in SAS, R, Python, SQL, Spark, and Tableau
- Build essential teamwork and communication skills to excel within diverse, multidisciplinary organizations
- Develop a solid grounding in data ethics — critical training in an age where the accumulation and storage of personal data is increasing
Our one-year, full-time program is ideal for students coming straight from undergraduate study. We also offer a part-time option designed with evening classes for working professionals, which can be completed in as few as two years. A STEM degree is not required and our program is open to students from any undergraduate background.
Real-world learning opportunities and professional networking
As part of this program, you’ll participate in an internship or supervised project with a local business or nonprofit — gaining hands-on experience carrying out a comprehensive data analytics process. At the same time, you’ll gain a wide range of viewpoints on data analytics, from faculty in areas from business to computer science and mathematics.
Our Data Analytics Advisory Council, comprised of leading data specialists in the region, helps to facilitate internships and experiential learning for students in the program. The Council also invites guest speakers who bring real-world experiences and insights to the classes.
Master’s in Data Analytics Program Highlights
- STEM degree not required
- GMAT/GRE not required
- Strong industry connections within Western New York that lead to internship and job opportunities
- Part-time option with evening courses designed for working professionals
- Funding and scholarship opportunities are available
- Experienced faculty comprised of scholars, big data researchers, and industry professionals
- Ability to tailor course requirements to fit your goals and interests
- The program qualifies for the STEM OPT extension, enabling international students to work for an additional 24 months post-graduation in the U.S. in the data analytics field
Featured Courses
- Data Stewardship: Preparation, Exploration, and Handling of Big Data – Explore the ethical issues surrounding big data and address technical issues related to working with large data sets.
- Data Mining and Machine Learning – Get a broad overview and learn a variety of methods essential to modern data mining and statistical learning.
- Visualization and Presentations of Advanced Analytics – Learn to present complex results from data analytics to a range of audiences in a course that covers both real-time interactive displays and tools.
STEM Designation- International Students
Certain F-1 Students who receive science, technology, engineering, and math (STEM) degrees can apply for a 24-month post-completion OPT extension. This program qualifies as a STEM-designated program. Speak with your admissions counselor to learn more.
Program Outcome
Student Learning Goal 1: Multi-Disciplinary Analytic Capabilities.
- Objective A: Domain Knowledge: Students will be able to apply computational and statistical methods and analytical tools to strategic and tactical decision-making for at least one domain area. In business, for example, this might be accounting, economics, finance, management, or marketing.
- Objective B: Adaptable grounding in applied statistics. Students will be able to use the basic principles of probability theory in a variety of contexts, including both classical statistical approaches and computational-based methods. Students will be familiar with one modern statistical software platform and will be able to readily adapt to others.
- Objective C: Flexible computational skills. Students will have a strong working knowledge of at least one general-purpose programming language and will be able to work with a range of data structures within those languages. Students will also be familiar with databases and the programming techniques needed to work with Big Data.
Student Learning Goal 2: Effective Teamwork.
- Objective A: Students will demonstrate the ability to work in multi-disciplinary teams to address real-world problems.
- Objective B: Students will understand the current theoretical ideas related to the formation of effective collaborative teams.
Student Learning Goal 3: Effective Business Communication.
- Objective A: Students will be able to identify the needs of different audiences, and effectively present complex information in ways that suit the needs of multiple audiences.
- Objective B: Students will be able to write effectively to convey data analytic results in business or other domain contexts.
- Objective C: Students will be able to create and deliver effective oral presentations, as well as present ideas in less formal oral settings.
- Objective D: Students will be able to create effective graphics, both static and real-time active displays, that convey results to business or other domain audiences.
Student Learning Goal 4: Ethical Data Stewardship.
- Objective A: Students will have an awareness of the ethical and moral issues that arise in working with large data sets, and understand the steps that need to be taken to protect the rights and privacy of the individuals involved.
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Curriculum
This program is divided into three distinct components, comprising a total of at least 30 credit hours. The Preparatory Courses are base levels of knowledge and skill required before proceeding with the Core Competencies portion of the program. Up to 10 hours (3 courses) of the Preparatory Courses may be waived based on the student's prior background and coursework. Students with exceptionally strong backgrounds may substitute other domain courses (typically graduate business courses) for Preparatory courses. For example, this might occur for a student with an engineering degree, and thus strong computational and mathematical skills, or a finance degree with strong business and mathematical grounding.
Core Competencies portion consists of 5 courses, all of which were developed exclusively for the Data Analytics program. They cover advanced statistics, topics on managing data, as well as visualization/presentation.
The students will also participate in integrative projects in data analytics, gaining valuable hands-on experience and connections at companies in the Buffalo area and beyond.
Preparatory Courses (taken the year or summer prior to cohort start)
- DAT 501 Statistics and Econometrics 3
- CSC 511 & 511L Introduction to Programming and Introduction to Programming Lab 3
- CSC 512 & 512L Data Structures and Algorithms and Data Structures and Algorithms Lab 3
Summer
- MAT 500 Topics in Applied Mathematics 4
- DAT 500 Interactive Graphical Case Studies in Big Data 1
Elective (Domain specific) 3 Fall
- DAT 511 Data Stewardship: Preparation, Exploration, and Handling of Big Data 3
- CSC 610 & 610L Database Management and Database Management Lab 3
- DAT 521 Applied Integrative Projects in Data Analytics I 3
Elective (Domain Specific) 3 Spring
- DAT 512 Statistical Approaches to Big Data 3
- DAT 514 Data Mining and Machine Learning 3
- DAT 515 Visualization and Presentation of Advanced Analytics 3
- DAT 522 Applied Integrative Projects in Data Analytics II 3
Total Credits 41
Up to 10 credits of coursework (from those noted) may be waived by the program director based on a student's preparation and experience.
Domain Courses
Students will take at least two domain courses drawn from the courses below. Students may apply to the program director to take graduate-level courses drawn from other domain areas or more advanced courses for which they have adequate preparation.
Business and Finance Domain
- ACC 505 Financial Accounting
- ECO 503 Statistics for Managers with Excel
- FIN 608 Corporate Finance
- FIN 617 Portfolio Analysis
- FIN 619 Financial Modeling
- FIN 620 Investment Management
- FIN 623 Fixed Income Securities
- FIN 628 Derivative Securities
Admissions
Career Opportunities
Data specialists are in high demand because they help businesses to plan better and work more efficiently.
According to the U.S. Bureau of Labor Statistics (BLS), employment of operations and research analysts (which includes data analysts and data scientists) is expected to grow 23% over the next decade, making this one of the fastest-growing fields in the nation. It’s also one of the highest-paying — Glassdoor ranks data scientists among the Top 25 Highest Paying Jobs in America, with an average salary of $97,027. The median salary for operations and research analysts in general, according to the BLS, is $82,360.
Our master’s in data analytics program graduates work in all types of industries, including banking, finance, insurance, advertising, and health care. They go on to successful careers as data analysts, business analysts, quantitative analysts, programmers, and engineers. Other jobs with a master’s in data analytics include:
- Data Specialist
- Data Engineer
- IT systems analyst
- Data analytics consultant
- Digital marketing engineer
English Language Requirements
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