Economics & Computation

• CIS 1501: Computer Science I for Data Scientists [F]

  This course provides a foundation for further studies in computer and information science and emphasizes a structured approach to problem solving and algorithm development using a high-level language more suited to data science applications. Topics include principles of program design, coding, debugging, testing, and documentation. Students are introduced to the Unified Modeling Language for requirements analysis using use-cases and activity diagrams, an object-oriented programming language for data science applications, and the fundamentals of computer hardware, system software, and components. The course will consist of three lecture hours and one two-hour laboratory. The labs will cover various data science applications. (4 credits)
  Prerequisites: MATH 115* or MATH 113* or MPLS with a score of 116. 

• CIS 2001: Computer Science II [W]

  This course presents techniques for the design, writing, testing, and debugging of medium-sized programs, and an introduction to data structures (stacks, queues, linked lists) using an object-oriented programming language for data science applications. Topics covered include recursion, inheritance, and abstract classes. The principles of UML modeling are continued. This course will consist of three lecture hours and one two-hour laboratory. The labs will cover various data science applications. (4 credits)
  Prerequisites: CIS 1501 and (MATH 115 or MATH 113 or MPLS with a score of 116)

• ECON 201: Principles of Macroeconomics [F,W,S]

  Together with ECON 202, this course serves to introduce the student to the basic ideas and concepts of modern economic analysis, and applies them to current economic problems, policies and issues. The focus of this course is on macroeconomics: income and wealth, employment, and prices at the national level in the United States economy. It is recommended that students take ECON 201 before ECON 202. MATH 105 is highly recommended but not required. 

• ECON 202: Principles of Microeconomics [F,W,S]

  Together with ECON 201, this course serves to introduce the student to the basic ideas and concepts of modern economic analysis, and applies them to current economic problems, policies, and issues. The focus of this course is on microeconomics, the behavior of consumers and firms and their interactions in specific markets. It is recommended that students take ECON 201 before ECON 202. MATH 104 or 105 is highly recommended but not required.

• MATH 115: Calculus I [F,W,S]

  Calculus is the study of change and accumulation in continuously variable quantities. This course covers limits and continuity, derivatives and their applications, and integrals, with algebraic, exponential, and trigonometric functions and their inverses. Students cannot receive credit for both MATH 113 and MATH 115. Prerequisite(s): MATH 105 or (MATH 104 and MATH 1045) or Mathematics Placement with a score of 115.

• MATH 116: Calculus II [F,W,S]

  This course continues the study of Calculus from Math 115, including applications and techniques of integration, improper integrals, parametric equations, polar coordinates, and sequences and series, including Taylor series. Students cannot receive credit for both MATH 114 and MATH 116. Prerequisite(s): MATH 115 or Mathematics Placement with a score of 116.

• MATH 227: Introduction to Linear Algebra [F,W,S]

  An introduction to the theory and methods of linear algebra with matrices. Topics include: systems of linear equations, algebra of matrices, matrix factorizations, vector spaces, linear transformations, eigenvalues and eigenvectors, science and engineering applications, and computational methods. Students cannot receive credit for both MATH 227 and MATH 217. Prerequisite(s): MATH 116 or Mathematics Placement with a score of 215

• MATH 276 or CIS 275

  • MATH 376: Discrete Math Meth Comptr Engr [F, W, S]

    An introduction to fundamental concepts of discrete mathematics for computer engineering. Topics will be chosen from: set theory, partially ordered sets, lattices, Boolean algebra, semi-groups, rings, graphical representation of algebraic systems, graphs and directed graphs. Applications in various areas of computer engineering will be discussed. Prerequisite(s): MATH 116 or Mathematics Placement with a score of 215

  • CIS 275: Discrete Structures I [F, W, S]                                                                                            

    This course introduces students to various topics in discrete mathematics, such as set theory, mathematical logic, trees, and graph theory. Applications to relational databases, modeling reactive systems and program verification are also discussed. Prerequisite(s): (MATH 115 or Mathematics Placement with a score of 116) and CIS 200*

• STAT 305: Intro to Data Science [YR]

  With increasing availability of data, companies, governments, and nonprofits alike are striving to convert this data into knowledge and insight. This course will provide students with the basic skill set needed to handle such data. The course will focus on three broad areas-inferential thinking, computational thinking, and real-word applications. We will discuss data collection, data cleaning and exploratory analysis of data so that students can connect the data to the underlying phenomena and be able to think critically about the conclusions that are drawn from the data analysis. The students will also learn how to write short programs to be able to automate the data analysis process developing an applied understanding of different analytics methods, including linear regression, logistic regression, clustering, data visualization, etc. Most of the material will be taught using real world data. 

Core Courses (20 credit hours)

• CIS 350: Data Struc and Algorithm Anlys [F,WS]

  This course focuses on data design and algorithm design. Data design topics include object-oriented discussions of hashing, advanced tree structures, graphs, and sets. Algorithm design topics include the greedy, divide-and-conquer, dynamic programming, backtracking and branch-and-bound techniques. A significant discussion of algorithm complexity theory, including time and space trade-offs and elementary computability theory, is included. Prerequisite(s): (MATH 115 or Mathematics Placement with a score of 116) and (CIS 200 or IMSE 200) and CIS 275

• CIS 421: Database Mgmt Systems 

  An introduction to database systems, concepts, and techniques. Topics covered include: database environments, ER modeling, relational data model, object-oriented databases, database design theory and methodology, database languages, query processing and optimization, concurrency control, database recovery, and database security. Prerequisite(s): CIS 350 or CIS 3501 or IMSE 351 or (ECE 370 and MATH 276)

• ECON 301: Intermediate Macroeconomics [F,W]

  A systematic study of the determinants of national output, economic growth, inflation, and unemployment. The effects of monetary policy, fiscal policy and other economic factors are analyzed for both the long run and short run. Debates about various approaches to macroeconomics policy are also discussed. Prerequisite(s): ECON 201 and ECON 202 and (MATH 104 or MATH 105 or MATH 113 or MATH 115 or Mathematics Placement with a score of 113)

• ECON 302: Intermediate Microeconomics [F,W]

  A systematic study of the role of prices in organizing economic activity. The tools necessary for such study will be developed and applied to the analysis of the household, the firm, and the market under varying degrees of competition and monopoly. Prerequisite(s): ECON 201 and ECON 202 and (MATH 104 or MATH 105 or MATH 113 or MATH 115 or Mathematics Placement with a score of 113)

• STAT 325: Applied Statistics I [F,W]

  This course studies the principles and applications of statistics. Topics include descriptive statistics, random variables, probability distributions, sampling distributions, the central limit theorem, confidence intervals, hypothesis testing for mean and variance and the use of normal, chi-square, F and t distributions in statistical problems. Other topics are selected from regression and correlation, the design of experiments and analysis of variance. Students can receive credit for only one of STAT 301 and STAT 325. Prerequisite(s): MATH 113 or MATH 115 or Mathematics Placement with a score of 116

Economics and Computation Electives

• Economics electives: Select 4 additional upper level ECON courses (300/400/4000+ level; excluding ECON 305 and ECON 499)
• Computation Electives: Select one courses from the following:
  • CIS 3200: Data Science II
  • CIS 405: Algorithm Analysis & Design
  • CIS 479: Intro to Artificial Intel
  • CIS 483: Deep Learning
  • STAT 327: Statistical Computing

Capstone Course

• One 4000+ level ECON course (4 credit hours)