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.
Students are required to have a bachelor’s degree in any major to qualify for the program.
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
DS-500 Data Science Fundamentals
A graduate level introduction to data science through a focus on the language R. Support tools and libraries such as Rstudio and the tidyverse will be emphasized. Students will complete the data science boot camp (a weekend in person intensive or online equivalent) at the start of this online course.
DS-510 Computer Science Fundamentals
A graduate-level introduction to Computer Science Fundamentals through a focus on the Python language. Students will complete the data science boot camp (a weekend in-person intensive or online equivalent) at the start of this online course.
DS-516 Mathematics Fundamentals
Selected topics of discrete mathematics and linear algebra related to data science analysis techniques and algorithms.
DS-520 Statistics Fundamentals
Overview of basic statistical techniques including descriptive statistics, hypothesis testing, and regression.
DS-525 Data Acquisition & Visualization
A graduate-level introduction to retrieving, cleaning, and visualizing data from widely varied sources and formats. The student will use common data science languages and tools for extraction, transformation, loading and visualizing data sets. Project presentations will have an emphasis on communication skills. Tableau visualization tools and Python libraries are used.
DS-570 Database Systems
This course focuses on database design and relational structures, data warehousing and access through SQL. Students will use SQL to create and pull data from database systems. NoSQL and data warehousing are also covered to give students the necessary background in database systems.
3 Credits Pre-Req: DS-510
DS-580 Data Science Capstone
Data science practicum requiring completion of a large-scale analysis project of a given data set. Written and oral communication skills emphasized.
3 CreditsPrerequisites: DS-500, DS-510, DS-516, and DS-520, or instructor permission.
DS-530 Multivariate Techniques
Multivariate statistical techniques including multivariate regression, logistic regression, and dimension reduction techniques. Students will get hands-on experience applying the topics covered to real datasets using R, a powerful and popular open-source statistical computing language.
3.00 CreditsPrereqs: DS-516 and DS-520.
DS-552 Data Mining
This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in wide-ranging datasets, from the simple tables to complex data sets and big data situations. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Python, Matlab, Weka and others.
3.00 CreditsPrerequisite: DS-500, DS-510, or by permission
DS-575 Big Data Techniques
This course considers the management and processing of large data sets, structured, semi-structured, and unstructured. The course focuses on modern, big data platforms such as Hadoop and NoSQL frameworks. Students will gain experience using a variety of programming tools and paradigms for manipulating big data sets on local servers and cloud platforms.
3 Credits Prerequisite: DS-500 or DS-510
MBA-511 Quantitative Analysis & Research Methods
Quantitative Analysis and Research Methods will examine some of the principle analytical tools for decision-making in business and investigation in the social sciences.
BIN-500 Bioinformatics Fundamentals
Bioinformatics is the science of collecting and analyzing complex biological data. It is an interdisciplinary field that develops and applies methods and software tools for understanding biological data.
4 CreditsNPre-req: BI-105, BI-106, BI-121, BI-122, CH-142,CH-143, CH-242, CH-243
BIN-580 Advanced Research Methods
This class will provide training in advanced modern molecular wet lab, statistical and/or informatics tools. Bioinformatics skills will be related to assembly, annotation, variant characterization, and/or comparison of eukaryotic genomes and populations. Statistical analyses will be performed in R. Molecular tools may include DNA and RNA isolation, electrophoresis, restriction digests, DNA isolation from gels, PCR, sequencing, next generation sequencing and equipment maintenance. Core bioinformatics learning objectives will receive special attention. General skills include training students in the process and procedures of conducting meaningful and responsible research in Biology, including: deriving research objectives, experimental design, problem solving skills, responsible conduct.
BIN-600 Environmental Genomics
This course will utilize Microbial Community Analysis leveraging high-throughput sequencing technology to identify the microbes present in naturally occurring our man-made ecosystems. Students will learn both molecular and bioinformatics skill sets, as well as microbial ecology principles throughout this course.
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