Degree At A Glance
- Credits: 30-32
- Tuition per Credit: $775
- Earn Your Masters Degree Online
- Rolling admissions
- 16 week courses, 14 week summer courses
- Enroll from a diversity of backgrounds and majors
Use the links below to apply online at no cost, or to request further information.Apply Online Request Information
Data Science is a rapidly growing field in which there are many job opportunities emerging across many disciplines. The program is designed for individuals with bachelor’s degrees who want to learn data science, with no prerequisites of courses in computer science or statistics. The program will be 30-32 credits of courses taught fully online, with optional once per semester weekend boot-camps for cohort building and software setup. Faculty from the IT/CS and Mathematics departments who will teach classes include Loren Rhodes, Kim Roth, Melissa Innerst, Jerry Kruse, Bill Thomas, and John Wright.
A Distinct Experience
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Once per semester, classmates and faculty will collaborate for cohort building and to correctly set software environments up for the semester's work. These boot-camps will be hosted at Juniata College's campus, but a remote access option will be made available for those that cannot attend in person.
This program will be 100% on-line and can serve a variety of students. Foundational courses will allow students to enroll from a diversity of backgrounds and majors, with no prerequisites of courses in computer science or statistics.
A Juniata data science student will obtain the knowledge and skills to:
- Acquire, prepare, analyze, and present data to support decision making
- Understand the different disciplines and application of information technology and data sciences
- Apply data mining methods utilizing a variety of modern tools, technologies, and scripting and programming languages
- Manage and access database environments
- Learn techniques and tools to manipulate/transform data from structured and non-structured sources
- Identify patterns in datasets
- Understand the different types of machine learning algorithms
- Understand appropriate data visualization techniques
- Communicate technical information clearly to specific audiences (oral and written)
- Understand ethical guidelines applicable to data management, algorithm development, and data analysis
|Course||MDS course #||Credits|
|Data Science Fundamentals||DS 500||4|
|Computer Science Fundamentals||DS 510||4|
|Mathematics Fundamentals||DS 516||3|
|Statistics Fundamentals||DS 520||3|
|Data Acquisition and Visualization||DS 525||3|
|Multivariate Techniques||DS 530||3|
|Data Mining||DS 552||3|
|Database Systems||DS 570||3|
|Big Data Techniques||DS 575||3|
Requirements for degree:
Data Science Fundamentals(DS 500)
Computer Science Fundamentals(DS 510)
Mathematics Fundamentals(DS 516)
Statistics Fundamentals(DS 520)
Data Acquisition and Visualization(DS 525)
12 credits of electives.
Total: 30-32 credits
Fundamentals Courses can be waived if student has appropriate background experience and replaced with electives.
For more information, please contact:
Assistant Director of Graduate Admissions