21 courses with the subject DS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DS 001Non-Credit Data Science Internship Provides students an overview of an organization
including its structure, strategies and principal lines of activity as well as providing an opportunity for practical application of the skills developed in students' areas of concentration. Note: see the chapter "Academic Programs" inder Internships: listed as Internship(title)must be at least 9 weeks long.
The EB Core or Foundation Courses, sophomore standing and permission.
DS 110Intro to Data Science This course introduces the student to the emerging field of data3
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. (N)
DS 210Data Acquisition Students will understand how to access various data types and sources3
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. (N)
DS 242Information Visualization This course considers the various aspects of presenting digital3
information for public consumption visually. Data formats from binary, text, various file types, to relational databases and web sites are covered to understand the framework of information retrieval for use in visualization tools. Visualization and graphical analyses of data are considered in the context of the human visual system for appropriate information presentation. Various open-source and commercial digital tools are considered for development of visualization projects. Prerequisite: AR-102, CS-110, DS-110, IM-110, or
DS 352Machine Learning This course considers the use of machine learning (ML) and data3
mining (DM) algorithms for the data scientist to discover information embedded in datasets from simple tables through complex and big data sets. Topics include ML and DM techniques such as classification, clustering, and 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 in the emerging storage and hardware structures are introduced for handling big data. Prerequisite: CS-110, DS-110, and an approved statistics course: MA-205, MA-220, BI-305, PY-260, PY-366, or EB-211. (N)
DS 420Data Science Capstone This course is a capstone experience for Data Science POE1
students and must be completed as part of a student's final 30 credits. It represents the summation of a student's Juniata experience and serves as a bridge to their future goals. Students will have the opportunity to both apply their previous data science skills and develop new skills through a data analysis project. or ESS-230 or ESS-309 or PY-361 or SW-215.
DS-110, CS-110, and one course from the following list: MA-220 or MA-205 or EB-211 or BI-305
DS 480Data Science Seminar Data Science Seminar introduces students to research in data1
science and allows students time and a network of peers to help plan a research project. DS Seminar, IT/CS Seminar, and IT/CS Research students meet at the same time and can offer mentoring to the class network, and ultimately share the outcomes of their research projects. Must have junior or senior class standing.
DS 485Data Science Research Under the direction of their advisor, students will complete an3-5
original, independent research project in Data Science. A written report and oral presentation summarizing their research experience and results will be prepared. This course is a requirement for students who are candidates for distinction in Data Science. Instructor permission required.
DS 495Internship Seminar See Internship in the catalog. Corequisite: DS-490. Requires1-6
permission and sophomore, junior, or senior class standing. DS-INS Data Science Independent Study (1-4 credits) ACCOUNTING, BUSINESS, AND ECONOMICS (EB)
DS 500Data Science Fundamentals A graduate level introduction to data science through a4
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 510Computer Science Fundamentals A graduate-level introduction to Computer Science4
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 525Data Acquisition & Visualization A graduate-level introduction to retrieving, cleaning, and3
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 530Multivariate Techniques Multivariate statistical techniques including multivariate3
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. Prereqs: DS-516 and DS-520.
DS 552Data Mining This course considers the use of machine learning (ML) and data mining (DM)3
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. Prerequisite: DS-500, DS-510, or by permission
DS 570Database Systems This course focuses on database design and relational structures3
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. Pre-Req: DS-510
DS 575Big Data Techniques This course considers the management and processing of large data3
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. Prerequisite: DS-500 or DS- 510
DS 580Data Science Capstone Data science practicum requiring completion of a large-scale3
analysis project of a given data set. Written and oral communication skills emphasized. Prerequisites: DS- 500, DS-510, DS-516, and DS-520, or instructor permission. MASTER OF EDUCATION IN SPECIAL EDUCATION (ED)
