Data Science

CS 101 Using Computers in Research Settings: 1 semester hour

The course is designed to make students fluent in the use of common office applications in professional settings. We will learn these skills in the context of the analysis and interpretation of real-world data sets that come from the research of the faculty and students of Mount Mercy University. Students who complete this course will be able to be more productive here at Mount Mercy, and more prepared to enter careers or to attend graduate school.

CS 103 Introduction To Web Site Development: 3 semester hours

In Introduction to Web Site Development, students will learn a wide arrange of web-based technologies and scripting languages that are used for the development of internet web sites. The tools discussed in the course will vary in order to stay current with the rapidly changing environment of web development. These tools could include (but are not limited to): wysiwyg html editors, html, css, xml, Flash, java script and dynamic web programming languages. The intent of the course is to give students a broad experience with a wide range of web-based technologies. This course is intended for non-majors who are interested in careers focused on the development of web sites. Computer Science majors may take the course as an elective, but it cannot be used to fulfill any CS graduation requirement or to complete an area of specialization.

CS 105 Fundamentals Of Computer Science: 4 semester hours

This course focuses on the concepts and constructs of computer programming, including program design and decomposition, data types, interactive and file input/output, control structures, and graphical user interface development. Formerly CS 175.

CS 106 Data Structures: 4 semester hours

This course introduces basic concepts of software development, elementary data structures (including sets, lists, stacks, queues, trees, and graphs), recursion, and elementary algorithm analysis. Formerly CS 205. Prerequisites: CS 105, MA 162 (the latter may be taken as a co-requisite).

CS 112 Introduction to Object Oriented Programming: 3 semester hours

This course teaches the concepts and skills of object oriented programming. Topics to be covered include inheritance, abstract fields, methods and classes, encapsulation and polymorphism. Demonstration of significant experience and skills in object oriented programming can be used to pass out of the course. Prerequisite: CS 105.

CS 190 Computer Organization: 4 semester hours

This course covers various hardware aspects of computers. Topics to be covered include number representation, digital logic, Boolean algebra, memory technologies, and management techniques, interrupts, CPU structure, microprogramming, assembly language, and input/output devices. Prerequisite: CS 106.

CS 203 Information Ethics: 3 semester hours

In this course, students will learn to define and analyze ethical, moral, social, and professional issues related to computing and information technology. Topics to be discussed include ethical frameworks for decision making, regulation of the Internet, intellectual property, privacy, security, and codes of conduct. Prerequisite: sophomore standing or consent of instructor.

CS 215 Data Programming Languages: 3 semester hours

This course is an introductory course for using current programming language techniques for Data Science. Students will learn to use a contemporary programming language, like python or R, to solve various data science challenges. The course reinforces the student’s knowledge of objects and control structures. The student will expand this knowledge for data storage, manipulation, visualization, and randomness. These tools and techniques are vital to the data science professional. Prerequisite: CS 105.

CS 226 Programming in Visual Basic: 4 semester hours

This course is an introduction to programming using Visual Basic and the .NET development environment. Topics to be covered include control structures, input/output, graphical user interfaces, and interface with other Microsoft Office applications. This course is for MIS majors. Computer Science majors may take the course as an elective, but it cannot be used to fulfill any CS graduation requirement or to complete an area of specialization.

CS 235 Systems Programming Concepts: 4 semester hours

This course explores topics related to operating systems and network programming, including shell programming, programming with operating systems calls, and programming using network sockets. Other topics include basic structure of operating systems and network software. Prerequisite: CS 190.

CS 302 Programming Languages: 4 semester hours

This course considers the evolution of programming languages. Topics to be discussed include language specification and analysis, syntax, semantics, parameter passing techniques, scope, binding, paradigms (including imperative, functional, and object-oriented), and translation techniques. Prerequisite: CS 190.

CS 315 Web Programming: 4 semester hours

This course explores the development of web-based applications and dynamic web pages using modern development tools and languages. Topics to be covered include basic web site design, scripting languages, web servers, use of databases and SQL in the development of dynamic web sites and web security. Prerequisite: CS 190.

CS 326 Information Systems Analysis: 3 semester hours

This course will focus on management issues in the creation and management of information systems. Broad topics will include system investigation, system and feasibility analysis, system design, system implementation, and system maintenance. Various approaches to systems analysis and design will be considered, as well as tools. Prerequisites: CS 106 for CS students or CS 226 and BN 204 for MIS students.

CS 388 Database Systems: 4 semester hours

This course emphasizes the concepts and structures necessary to design and implement a database management system. Topics to be covered include the evolution of database systems, the relational database model, query languages, triggers, constraints, views, and other advanced topics as time permits. Prerequisite: CS 326.

CS 389 Algorithm Analysis: 3 semester hours

This course is an introduction to advanced data structures and algorithm analysis techniques. Topics to be covered include asymptotic notation, empirical and theoretical analysis techniques, complexity classes, algorithmic approaches (divide and conquer, greedy), and advanced tree structures. Three hours lecture. Prerequisites: MA 162, CS 106.

CS 399 Special Topics in Computer Science: 3 semester hours

This course provides students the opportunity to take electives in an area of special interest in computer science. When possible, the course will be taught by experts from the field. Topics may include educational software development, artificial intelligence, robotics, embedded systems, bioinformatics, and cryptography. Prerequisite: permission of instructor.

CS 415 Field Experience: 3 semester hours

This course provides students the opportunity to take advantage of internship opportunities that become available. The internships include off-campus supervision at local employers and periodic conferences with the on-campus instructor. One semester hour of credit is assigned for each 45 hours of work per semester at the outside agency.

CS 420 Management Information Systems Senior Thesis: 3 semester hours

The MIS Senior Thesis is intended to be one option for the MIS capstone course specifically suited to students with significant professional experience as a team member on at least one large enterprise software development project. Students in this course will work with a faculty member to select a topic relevant to their education and professional experience, design a plan for researching the topic and produce a thesis that reviews and analyzes the research and integrates the research, the learning they have gained from their educational program and from their professional experience into a solution of the problem defined by the chosen topic.

CS 430 Senior Project: Management Information Systems: 4 semester hours

This is the capstone course for management information system majors. The student will complete a broad and deep software development project as part of a multi-disciplinary team as project managers. Prerequisites: CS 226, CS 326 and BN 377.

CS 435 Senior Project: Computer Science: 4 semester hours

This is the capstone course for computer science majors. The student will complete a broad and deep software development project as part of a multi-disciplinary team. Prerequisites: CS 235 and at least one 300-level CS course.

CS 445 Computer Science Independent Study: 3 semester hours

Study topics will be negotiated by the student and his/ her advisor.

DS 101 Introduction to Data Science I: 3 semester hours

Our world is driven by data. In order to navigate this world and understand the influence data and its science has on modern life, students will learn the core concepts of inference, data analysis, and computing. Students will work with real data sets from a variety of fields such as economics, geography, and sociology. Topics will include basic computing techniques using spreadsheets or other computing software, basic statistical concepts such as Bayes’ Theorem, and the pitfalls of bias inherent in data sets.

DS 301 Introduction to Data Science II: 3 semester hours

Linear regression and associated techniques are some of the most tested and trusted methods in data science and statistics. In this course, we will develop the skills to apply linear methods to investigate relationships between various types of data, visualize data, and consider the responsible use of such models. Topics may include linear and multiple regression, resampling, model and feature selection, representing analyzed data visually, logistic regression, and the data science life cycle. Python/R will be used throughout. Prerequisites: : MA 162, MA 202, MA 214, CS 106, DS 101.

DS 400 Data Science Techniques I: 3 semester hours

In this course, we will learn a variety of techniques often used in data analysis. Methods for classification and regression may be considered. Students will continue to develop deeper mathematical skills, programming skills using Python/R, the ability to produce high-quality documents conveying the results of data-based analysis, and more. Topics may include classification with tree-based methods and support vector machines, clustering (such as k-means, hierarchical, and spectral), and dimension-reduction (such as principal component analysis). Issues regarding the ethical use of data will be explored. Prerequisite: DS 301.

DS 420 Data Science Techniques II: 3 semester hours

This course will be an introduction to deep learning with artificial neural networks. The course will focus on applications and computations with software such as Python/R but will have significant mathematical content. Issues of appropriate uses, un/interpretability, and ethics in data will be considered in the context of employing neural network models. Topics may include single and multi-layer perceptrons, feedforward networks, recurrent neural networks, convolutional neural networks, and corresponding mathematical foundations. Prerequisite: DS 400.