Program Codes:
BSDS
Bachelor of Science
Introduction
The department offers BS in Cyber security and BS in Data Science. The goal of the Computing & Information Science Department at Mercyhurst University is to be a center of excellence in cyber security and data science education that is recognized by employers and graduate schools across the country and the world.
Mission Statement
The Department of Computing & Information Science is committed to:
Majors and Minors
Majors: Cyber Security and Data Science
Minors: Computer Science, Cyber Security, and Data Science
This course introduces to fundamental concepts in computer science. Topics include: problem solving, algorithm development, computer programming in a high level language, debugging programs, abstract data types, variables, assignment, loops, conditionals, functions. Students who have successfully completed MATH 146 should not take this course.
For students in any discipline wishing to develop data science skills. We will examine several aspects of the data science workflow, including transformation of data, exploration of data, data modeling, and data visualization. Participants will gain experience with a variety of data science technology. Possible topics include basic programming, data visualization, and machine learning.
This course introduces to linear and non-linear data structures and algorithm analysis. Topics include: arrays, linked lists, stacks, queues, trees (search, balanced), heaps, hash tables, graphs, numpy arrays, dataframes, asymptotic analysis including big-Oh notation, and techniques for measuring algorithm complexity. Students who have successfuly completed MIS 190 should not take this course.
This is the second course in the data structures sequence that introduces students to non-linear data structures: trees (binary, balanced, and n-ary) and graphs and how to use them to design efficient algorithms to solve fundamental computing problems such as sorting data and searching for information.
Students will learn to produce a wide variety of visualizations of structured data. There will be particular emphasis on producing dashboard-like interactive reports.
This is a study of the concepts, procedures, design, implementation and maintenance of a relational data base management system. Topics include normalization, database design, entity-relationship modeling, performance measures, data security, concurrence, integrity and Structured Query Language. MySQL will be the database management system used in this course. Students who have successfully completed MIS 350 should not take this course.
This is a course on big data tools on the Google Cloud Platform. Students will begin by studying transformations and actions on Spark RDDs, moving on to Spark data frames and Spark SQL. The majority of the class will be programming in Python with PySpark, with an introduction to Scala. Other topics include Spark streaming, BigQuery ML, Apache Beam, and possibly Pub/Sub. All work will be done on the Google Cloud Platform and Google Data Studio.
This course introduces the students to the principles and techniques of ethical hacking. The course topics include reconnaissance, scanning, exploitation, and result reporting. The student will have hands on experience in exploiting various vulnerabilities and weaknesses in computer systems and networks.
This course provides students a basic understanding of the subject of machine learning. Topics include: supervised learning (regression, logistics regression, support vector machines, tree classification methods, and ensemble methods, such a bagging, boosting, and random forests), unsupervised learning (k-means, hierarchal, and clustering), and techniques for evaluating machine learning algorithms.
This senior capstone course provides students with a focused, team-based design experience. Students will work with other students, industry mentors, and a faculty advisor to apply computing and information scence methods in solving a real-world problem.
This course will provide an introduction to statistical methods used in research with an emphasis on describing, organizing, applying and interpreting a variety of basic statistical techniques. Topics include measurement scales, elements of experimental design, probability, hypothesis testing, descriptive statistics, correlation, t-tests, analysis of variance, chi-square tests, regression techniques, and non-parametric statistical methods. Students will gain experience in basic data management using a data entry platform, such as Excel.
All Data Science majors are encouraged to pursue an additional minor or (preferably) an additional major in another discipline. All Data Science majors must maintain a GPA in the major of 2.0 or higher. A student who does not satisfy this requirement may be dismissed from the major and/or prohibited from graduating with the major. A student who receives a C or below in CIS 210: Non-linear Data Structures must have the department’s permission to continue in the major.
Computer Science Minor (5 courses):
Required: CIS 100, CIS 200, CIS 210
Choose at least two other CIS courses (except CIS 201).
Data Science Minor (5 courses):
Required: CIS 100, CIS 150, CIS 200, STAT 120 or CIS 201
Choose at least one of: CIS 210, CIS 280, or CIS 350
Cybersecurity Minor (6 courses):
Required: CIS 100, CIS 160, CIS 230, CIS 261, CIS 360, CIS 361