BS in Data Science: Degree Requirements & Courses

BS in Data Science: Degree requirements & courses

Curriculum

The Data Science program combines courses that cover specific topics like data visualization with traditional courses in mathematics and computer and information science.

In mathematics, students will take statistics, probability, linear algebra, scientific computation, and calculus.

In computer science, students will take courses in object-oriented programming, software design, algorithms, data mining, and machine learning.

In addition, students in their senior year will work in teams on real-world capstone projects and learn to communicate their results as a professional data scientist.

Learning outcomes

At the time of graduation, students will:

  • be able to understand and apply contemporary techniques for managing, mining, and analyzing big data across multiple disciplines
  • be able to use computation and computational thinking to gain new knowledge and to solve real-world problems of high complexity
  • have the ability to communicate their ideas and findings persuasively in written, oral and visual form and to work in a diverse team environment
  • be prepared for graduate school or employment and have an appreciation for life-long learning
  • have an appreciation for the professional, societal and ethical considerations of data collection and use

BS in Data Science: 120 Credits

Freshman Year (31 cr)

First Semester

Credits

 

Second Semester

Credits

ENL 101

Critical Writing & Reading I

 

 

3

ENL 102

Critical Writing & Reading II

 

 

3

CIS 180

Object-Oriented Programming I

 

 

4

CIS 181

Object-Oriented Programming II

 

 

4

DSC 101

Introduction to Data Science (US 1E)

 

 

3

 

University Studies 3B

 

 

3

MTH 151

Analytic Geometry & Calculus I

 

 

4

MTH 152

Analytic Geometry & Calculus II

 

 

4

           

University Studies 4B

 

 

3

 

Total

 

 

14

 

Total

 

 

17

 Sophomore Year (31 cr)

First Semester

Credits

 

Second Semester

Credits

MTH 181

Discrete Structures I

 

 

3

MTH 280

Introduction to Scientific Computation

 

 

3

MTH 221

Linear Algebra

 

 

3

MTH 231

Elementary Statistics I: Exploratory Data Analysis

 

 

3

DSC 201

Data Analysis & Visualization

 

 

3

CIS 280

Software Specification & Design

 

 

4

 

Laboratory Science I (US 2A)

 

 

4

 

Laboratory Science II

 

 

4

 

Free Elective

 

 

3

 

 

 

 

 

 

Total

 

 

16

 

Total

 

 

14

 Junior Year (30 cr)

First Semester

Credits

 

Second Semester

Credits

CIS 360

Algorithms and Data Structures

 

 

3

CIS 381

Social & Ethical Aspects of CS (US 2B)

 

 

3

MTH 331

Probability

 

 

3

MTH 332

Mathematical Statistics

 

 

3

DSC 301

Matrix Methods for Data Analysis

 

 

3

 

University Studies 4A

 

 

3

ENL 266

Technical Communications

 

 

3

 

Science/Quantitative Elective (US 2A)

 

 

3

 

University Studies 3A

 

 

3

 

University Studies 4C

 

 

 3

 

Total

 

 

15

 

Total

 

 

15

 Senior Year (28 cr)

First Semester

Credits

 

Second Semester

Credits

DSC 498

Data Science Senior Capstone I

 

 

3

DSC 499

Data Science Senior Capstone II

 

 

2

DSC

DSC UG Technical Elective

 

 

3

DSC

DSC UG Technical Elective

 

 

3

CIS 430/452

Data Mining/Database Design

 

 

3

CIS 490

Machine Learning

 

 

3

 

Free Elective

 

 

3

DSC

DSC UG Technical Elective

 

 

3

 

Free Elective

 

 

3

 

Free Elective

 

 

3

 

Total

 

 

15

 

Total

 

 

14

Course descriptions 

CIS 180 Object-Oriented Programming I  

Course Description: Basic concepts in programming, and introduction to the object paradigm. The course introduces the concept of the object paradigm and teaches how to design and implement simple programs in an object-oriented language. The course also covers the basics of how to use a computer and basic software tools in the process of developing programs.

CIS 181 Course Name: Object-Oriented Programming II

Course Description: Software development using advanced object paradigm concepts. This course introduces threads, networking, generic programming, and exception handling. The course covers in depth the advanced topics of object paradigm such as inheritance and polymorphism. These concepts are introduced in the context of developing software using software tools including libraries of components.

CIS 280 Software Specification & Design            

Course Description: Object-oriented analysis and design: methodologies and tools. The course focuses on methodologies of specification and design of software systems. It addresses the issue of user interface design and software prototyping. The course also presents the state of the art in tools and environments supporting the front end of the software development cycle.

CIS 360 Algorithms and Data Structures             

Course Description: Comprehensive coverage of all major groups of algorithms, including divide-and- conquer, dynamic programming, greedy, backtracking, branch-and-bound, and parallel algorithms. Discussion of the design and implementation of complex, dynamic data structures.

CIS 381 Social and Ethical Aspects of Computing                      

Course description: Introduction to the social, legal, and ethical issues of computing. Topics include how computer use affects social and work relationships and the uses of computers in society. These will be reviewed in the context of risks, privacy and intrusion, computer crime, intellectual property, and professional decision-making. Students analyze scenarios that allow them to view ethical decision-making as a crucial part of understanding the world of computing.

 

CIS 430 Data Mining and Knowledge Discovery                       

Course description: This course is designed to provide students with a solid background in data mining and knowledge discovery concepts, tools, and methodology, as well as their applicability to real world problems. A variety of data mining techniques will be explored including memory-based reasoning, cluster detection, classification, neural networks, and finding understandable knowledge in large sets of real world examples. Some related topics such as web and multimedia mining will be discussed. Students will gain hands-on experience in data mining techniques using various data mining software packages and tools. Ethical issues will also be discussed.

 

CIS 452 Database Systems                                                                 

Course Description: Use of DBMS software in the development of an information system. Overview of the ANSI/SPARC Study Group on Database Management Systems model. Relational database model techniques. Emphasis on user views necessary to support data management and retrieval.

 

CIS 490 Machine Learning                         

Course Description: Constructing computer programs that automatically improve with experience is the main task of machine learning. The key algorithms in the area are presented. Learning concepts as decision trees, artificial neural networks and Bayesian approach are discussed. The standard iterative dichotomizer (ID3) is presented, the issues of overfitting, attribute selection, and handling missing data are discussed. Neural nets are discussed in detail, examples of supervised and unsupervised learning are presented. Instance-based learning, i.e. k-nearest neighbor learning, case-based reasoning are introduced. Genetic algorithms are discussed on introductory level.

 

MTH 111 Calculus I     

Course Description: An intensive study of differential calculus and its applications, and an introduction to integrals, Topics include: limits, continuity, indeterminate forms, differentiation and integration of algebraic and transcendental functions, implicit and logarithmic differentiation, and applications to science and engineering. This is the first semester of the standard calculus sequence designed for students interested in Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology.

 

MTH 112 Calculus II    

Course Description: An intensive study of the techniques and applications of integration and infinite series. This is the second semester of the standard calculus sequence designed for students interested in Mathematics, Physics, Chemistry, Engineering and Mathematical/Computational Biology.

 

MTH 181 Discrete Mathematics I    

Course Description: An introduction to mathematical reasoning, mathematical logic, and methods of proof. Topics include: properties of numbers, elementary counting methods, discrete structures, Boolean algebra, introduction to directed and undirected graphs, methods of proof, and applications in mathematics and computer science. This is the first semester of a discrete mathematics sequence designed for Mathematics, Computer and Information Sciences majors.

 

MTH 221 Linear Algebra    

Course Description: Required of all second-year mathematics majors and recommended for students in the physical, natural, behavioral and management sciences. Course material includes systems of linear equations, matrix theory, vector spaces, linear transformations, Eigenvalues.

 

MTH 231 Elementary Statistics I: Exploratory Data Analysis

Course Description: Introduction to exploratory data analysis using R, including graphical techniques, confirmatory statistics, interval estimates, hypothesis tests, bootstrap estimates.

 

MTH 280 Scientific Computation

Course Description: Calculus-based programming covering conditionals, loops, arrays, file I/O, libraries, data types, and operating system commands. This course provides a project driven introduction to programming using a selection of mathematics programming tools, scripting languages, and traditional languages. This course requires a strong background in mathematics and is intended for students planning to take upper-level courses in applied or computational mathematics.

 

MTH 331 Probability                                                                         

Course Description: A calculus-based introduction to statistics. This course covers probability and combinatorial problems, discrete and continuous random variables and various distributions including the binomial, Poisson, hypergeometric normal, gamma and chi-square. Moment generating functions, transformation and sampling distributions are studied.

 

MTH 332   Mathematical Statistics        

Course Description: Classical estimation methods and hypothesis testing are studied. This course also covers Chi square tests for goodness-of-fit and independence, regression and correlation analysis, and one-way and two-way analysis of variance including factorial designs and tests for the separation of means.

Data Science Courses

 

DSC 101 Introduction to Data Science (3 credits)

Introduction to data science and big data. Topics include: the varied nature of data; applied data problems; introduction to R; integrating data systems; data frames; descriptive statistics; sampling statistics; large to very large data sets; analysis  of social network data; unstructured data and natural language  texts; data storage, SQL databases; data input and output; combining data and data mash ups; linear  regression; data mining.

 

DSC 201 Data Analysis and Visualization (3 credits)

Prerequisites: CIS 180/181Object-Oriented Programming I/II

An introduction to data analysis with a focus on visualization.  Topics may include: visualization of scalar, vector and tensor data; software tools for image, volume and information visualization and analysis; descriptive statistics; time dependent data; data patterns; analyzing propositions, correlations, and spatial relationships. Application of these topics to natural sciences and engineering are discussed.

DSC 301 Matrix Methods for Data Analysis (3 credits)

Prerequisites: MTH 221, MTH 280

Matrix methods with emphasis on applied data analysis. Matrix norms; LU, QR and SV decomposition of matrices; least squares problems; orthogonal vectors and matrices; applications to data analysis.

 

DSC 498/499 Data Science Capstone Project (2/2 credits)

Prerequisites: DSC 201, DSC 301; Co-requisite: CIS 430

A capstone course for the application of knowledge discovery and data mining tools and techniques to large data repositories or data streams. This project-based course provides students with a framework to gain both understanding and insight into the application of knowledge discovery tools and principles on data within the student's cognate area. This course is intended only for data science majors only.

 

Technical Electives (examples)

MTH 361 – Numerical Analysis I (3 credits)

Prerequisites: MTH 221, 212, CIS 261, (MTH 221 may be taken concurrently)

Theory and computer-oriented practice in obtaining numerical solutions of various problems.  Topics include stability and conditioning, nonlinear equations, systems of linear equations, interpolation and approximation theory.

 

MTH 463 - Math Modeling (3 credits)

Selected topics from the areas of linear programming, dynamic programming, Markov chains and game theory.  Mathematical model building will be developed through the use of numerous case studies from the natural and social sciences, e.g., ecological models, network models, scheduling models, urban structure, traffic flow, growth, etc.

 

CIS 362 - Empirical Methods for Computer Science (3 credits)

Prerequisites: MTH 331

Topics and methods supporting an experimental approach to the study of issues in computer science and software engineering.  Course covers the basic principles of experimental design and case study construction.  Emphasis in the course is on the use of empirical methods for decision making and the evaluation of research in computer science

CIS 431 - Human and Computer Interaction (4 credits)

Prerequisites: CIS 362 or permission of instructor

Theory and principles for constructing usable software systems.  Cognitive and effective aspects of users.  The impact of user characteristics on design decisions.  The construction and evaluation of the user interface.  Sensory and perceptual aspects of interfaces, task structure, input modalities, screen layout, and user documentation.  Individual concerns for systems such as personal productivity tools, real-time control systems, instructional software, and games.

 

CIS 454 - Computer Graphics (3 credits)

Prerequisites: At least junior CIS standing Graphics devices.

Graphics devices.  Two dimensional and three dimensional image representations and transformations.  Graphics systems software architecture; graphics standards; packages.

 

CIS 455 - Bioinformatics (3 credits)

Prerequisites: CIS 360 and CIS 361 or permission of instructor

Introduction to the field of bioinformatics.  This course addresses the analysis of information present in biological systems.  This course presents an overview of the applications of computing technologies such as: analysis of protein sequence, pattern matching, biomodeling and simulation, and biological data visualization.  It also provides algorithms and methods on a selection of computational problems.  Hands on experience with tools and data.

 

MIS 315 - Information Systems (3 credits)

Prerequisites: At least junior standing; MIS 101, or ENL 102, or permission of Assistant Dean for Undergraduate Programs

Provides an understanding of information technology and systems and how information is used in support of decisions and organizational processes.  Emphasis is on how information systems relate to organizational systems and decision making, information systems components, implementation and evaluation of systems performance, and ethical issues related to information systems design and use.  Cannot be used as a Business Elective by Accounting Majors

 

MIS 332/432 - Business Data Systems (3 credits)

Students demonstrate their mastery of the analysis and design processes acquired in earlier courses by designing and constructing databases to meet the information needs of users.  Topics covered include data models and modeling techniques, information engineering, database design and implementation, data quality and security, and the client/server environment.


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