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Data Science

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:

  • Preparing its cyber security, data science, and information technology graduates for immediate employment in their fields or for continued graduate study in cyber security and data science;
  • Providing Mercyhurst University students in Biology, Biochemistry, Chemistry, Physics, Forensic Science, and Public Health the computing education and resources they need to be successful in their chosen fields;
  • Providing Mercyhurst University students in Intelligence Studies computing education opportunities and resources related to their discipline;
  • Providing Mercyhurst University students in Graphic Design, Art, Music, and Dance computing education opportunities and resources related to their disciplines;
  • Providing all Mercyhurst students with opportunities to improve their basic computing skills;
  • Providing all Mercyhurst students with opportunities to broaden their appreciation of computing & information science;
  • Contributing to the advancement of computing and information science through published research, conference presentation, and scholarly collaboration;
  • Sharing educational innovation through publication and conference presentation.

Majors and Minors
Majors: Cyber security and data science
Minors: Computer science, cyber security, and data science

Program Outcomes:
  • Describe all steps of the data cycle;
  • Elicit client requirements and form a clear empirical question;
  • Pre-process data so that it can be stored and analyzed;
  • Choose and evaluate appropriate mathematical, statistical, and machine learning models depending on the specific information needs of the project;
  • Design and implement efficient computer programs to clean, analyze, and visualize data;
  • Communicate the results and limitations of data analysis and modeling to a non-technical audience; and
  • Handle all data and communications professionally, ethically, and securely.
REACH
Mercyhurst - REACH and outREACH
39
INTRODUCTION TO COMPUTER SCIENCE
Course Code:
CIS 100
Active Term:
All Terms
/ All Years
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 100
INTRODUCTION TO COMPUTER SCIENCE
3
INTRO TO DATA SCIENCE
Course Code:
CIS 150
Active Term:
All Terms
/ All Years
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 150
INTRO TO DATA SCIENCE
3
LINEAR DATA STRUCTURES
Course Code:
CIS 200
Active Term:
Spring Term
/ All Years
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 200
LINEAR DATA STRUCTURES
3
NON-LINEAR DATA STRUCTURES
Course Code:
CIS 210
Active Term:
Fall Term
/ All Years
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 210
NON-LINEAR DATA STRUCTURES
3
DATA VISUALIZATION & COMMUNICATION
Course Code:
CIS 280
Active Term:
Randomly
/ Odd Years
Course Description:

Students will learn to produce a wide variety of visualizations of structured data. There will be particular emphasis on producing dashboard-like interactive reports.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 280
DATA VISUALIZATION & COMMUNICATION
3
RELATIONAL DATABASE SYSTEMS
Course Code:
CIS 350
Active Term:
Fall Term
/ Randomly
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 350
RELATIONAL DATABASE SYSTEMS
3
BIG DATA ANALYTICS
Course Code:
CIS 352
Active Term:
Randomly
/ Randomly
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 352
BIG DATA ANALYTICS
3
ETHICS IN COMPUTING
Course Code:
CIS 400
Active Term:
Fall Term
/ All Years
Course Description:

This course introduces students to the issue of ethics in the profession of computing. These include: electronic privacy, security, information ethics, existing and emerging cyber law, environments of trust, risk management and operation feasibility issues.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 400
ETHICS IN COMPUTING
3
MACHINE LEARNING
Course Code:
CIS 471
Active Term:
Fall Term
/ Even Years
Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 471
MACHINE LEARNING
3
SENIOR PROJECT
Course Code:
CIS 499
Active Term:
Spring Term
/ All Years
Course Description:

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.

Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
CIS 499
SENIOR PROJECT
3
PROBABILITY & STATISTICS FOR DATA SCIENCE
Course Code:
CIS 201
Credit:
3
x
CIS 201
PROBABILITY & STATISTICS FOR DATA SCIENCE
3

OR

STATISTICS FOR THE SCIENCES
Course Code:
STAT 139
Active Term:
Randomly
/ All Years
Course Description:
This course is an introduction to statistical methods that are of particular interest to biology and natural science majors. We will explore the use of statistical methodology in designing, analyzing, interpreting, and presenting biological experiments and observations. We will cover descriptive statistics, elements of experimental design, probability, hypothesis testing and statistical inference, analysis of variance, correlation, regression techniques, and non-parametric statistical methods. Throughout the course the application of statistical techniques within a natural science context will be emphasized, using data from laboratory and field studies. Prerequisite: MATH Placement score of at least 46.
Credit:
3
Instruction methods:
Lecture
Total hours: 45
x
STAT 139
STATISTICS FOR THE SCIENCES
3

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 139 or CIS 201
Choose at least one of: CIS 210, CIS 280, or CIS 350

Cybersecurity Minor (5 courses):
Required: CIS 100, CIS 160, CIS 261, CIS 360, CIS 361

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