Department Website:

https://www.juniata.edu/academics/graduate-programs/data-science.php

Faculty:

Background Information:

 

Special programs, facilities, or equipment:

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Individualized Programs of Emphasis:

Secondary Emphasis:

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Courses:

DS-110   Intro to Data Science (Fall; Yearly; 3.00 Credits; N) This course introduces the student to the emerging field of data science through the presentation of basic math and statistics principles, an introduction to the computer tools and software commonly used to perform the data analytics, and a general overview of the machine learning techniques commonly applied to datasets for knowledge discovery. The students will identify a dataset for a final project that will require them to perform preparation, cleaning, simple visualization and analysis of the data with such tools as Excel and R. Understanding the varied nature of data, their acquisition and preliminary analysis provides the requisite skills to succeed in further study and application of the data science field. Prerequisite: comfort with pre-calculus topics and use of computers.

DS-210   Data Acquisition (Fall & Spring; All Years; 3.00 Credits; N) Students will understand how to access various data types and sources, from flat file formats to databases to big storage data architecture. Students will perform transformations, cleaning, and merging of datasets in preparation for data mining and analysis. PRE-REQ: CS 110 and DS 110.

DS-352   Machine Learning (Fall; Variable; 3.00 Credits; N) This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in datasets from the simple tables through complex and big data sets. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Matlab, Weka and others. Simple visualization and data exploration will be covered in support of the DM. Software techniques implemented the emerging storage and hardware structures are introduced for handling big data. Prerequisite: CS 110, DS 110, and an approved statistics course: MA 220, BI 305, PY 214 or EB 211.

DS-500   Data Science Fundamentals (Fall & Spring; Yearly; 4.00 Credits) 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.