Data Analytics Courses
Below are all of the courses you have to choose from in this academic major. Some are required while others are electives. Please view the course catalog to see what is required to earn a degree in this major.
Principles of Accounting I (SBU 100)
General introduction to accounting principles and bookkeeping methods; the theory of debit and credit; financial statements; working papers; adjusting and closing entries. Fall semester and ADP sessions 1 and 3. 3 credits.
Principles of Accounting II (SBU 101)
An examination of depreciation theory; liabilities; voucher system; payroll; partnership; corporation; consolidated statements; statement of cash flow. Prerequisite: SBU100. Spring semester and ADP sessions 2 and 4. 3 credits.
Principles of Management (SBU 180)
Process of management in both profit and non-profit organizations. Emphasis on major functions of management, with decision-making as integral part of each, including planning, organizing, leading, staffing and training, development, and marketing. Fall and spring semesters and ADP sessions 1, 2, 3, and 5. 3 credits.
Prog 1: Intro to Development (SCS 131)
Students learn the fundamentals of computer science and of Mac programming. This course uses the Objective C programming language and the XCode development environment. Students learn specific skills in Mac programming and the Objective C language. They also learn fundamental programming skills which are highly transferable to other languages, such as how to use branches and loops. Fall and spring semesters. 3 credits.
Java Programming (SCS 220)
Covers fundamental concepts of object-oriented programming using Java. Topics include objects, classes, constructors, methods, and instance variables. Programming projects include linked lists, stacks, queues, searching, and recursion. Students learn the basics of graphical user interfaces and Java applets. All programming is done in Java. Fall semester, even-numbered years. Prerequisite: SCS131. 3 credits.
Database Management Systems (SCS 230)
Introduces the student to the process of relational database development, including data modeling, database design, implementation, and administration. Topics include: the relational model, E-R Model, relational database design, normal forms, functional dependencies, relational algebra and calculus, SQL, query processing, crash recovery, concurrency control, security, and integrity. Students are expected to complete projects using Microsoft Access and SQL. Spring semester, odd-numbered years. 3 credits.
Machine Learning (SCS 235)
A first course in machine learning and artificial learning for Data Analytics majors, but open to any student. Topics include: data preparation, information-based learning, the ID3 algorithm. nearest-neighbor algorithm, probability-based learning, Bayes' theorem and prediction, multilinear regression-gradient descent method, and misclassification rate. Fall Semester. Prerequisite: SCS250. 3 credits.
Programming Languages (SCS 250)
The syntax and semantics of programming languages. Topics include formal specification syntax, declarations, binding, allocation, data structures and data types, control structures, control and data flow, the implementation and execution of programs, functional programming, and imperative programming. Other topics include non-procedural and logic programming. Programming projects provide experience in a variety of high level languages. Writing Intensive course. Prerequisite: SCS220 or SCS290. Fall semester, odd-numbered years. 3 credits.
Introduction to Data Analytics (SDT 100)
An initial introduction to the concepts of data analysis through the investigation of data-driven questions in a wide array of fields - economic, historical, social biological, and others. An emphasis is placed on insight and presentation. Fall semester. 3 credits.
Practical Data Analytics (SDT 200)
A problem- and project-driven course that considers the implications of data and marketing recommendations from data. Data clearing. misrepresentation of data, data mining, and other assorted topics are incorporated in the curriculum. This course prepares students for an internship. Spring semester. Prerequisites: SCS131, SDT100, and SMA265. 3 credits.
Advanced Data Analytics (SDT 250)
Designed to develop students' understanding of the methods and algorithms of data analytics and how to answer questions about real-world problems. The course is project-based and includes topics as clustering, classification, and network analysis. Writing Intensive. Spring semester. Prerequisites: SCS250 and SDT200 or permission of instructor. 4 credits.
Capstone (SDT 300)
This is a capstone seminar for the Data Analytics major. Students work collaboratively on cumulative research projects based on all of their previously learned techniques and internship experiences. Emphasis is on producing research reports and collective problem solving. Prerequisites: SCS250, SDT250, and SMA271. 4 credits.
Internship (SDT 430)
An internship in data analytics, with practical skills and applications of the techniques from the student's course work. Fall and spring semesters. Prerequisite: SDT300 or permission of instructor. 3 credits.
Calculus 1 with Analytic Geometry (SMA 130)
Real numbers, sets, relations, and functions. The calculus of one variable. Satisfies the Mathematics requirement of the Liberal Arts Curriculum. Calculator required. Prerequisite: a working knowledge of algebra and trigonometry. Fall and spring semesters. 4 credits.
Calculus 2 (SMA 140)
Continuation of SMA130. The calculus of one variable with applications, parametric equations, polar coordinates, and infinite sequences and series. Prerequisite: SMA130. Fall semester. 4 credits.
Applied Math for Computer Science (SMA 215)
This course introduces key ideas from the fields of probability, linear algebra, and graph theory along with applications of those ideas to computer science. Prerequisite: SMA205. Spring semester, even-numbered years. 3 credits.
Applied Statistics (SMA 265)
A first course for Data Analytics and Actuary Science majors in applied statistics with statistical software. Topics include: least square estimates of parameters, single and multiple linear regression, hypotheses and confidence in regression models, testing of models, and appropriateness of models. An emphasis on comprehension and interpretation of data. Spring semester. Prerequisite: SMA130. 3 credits.
Applied Statistics & Modeling (SMA 271)
Advanced regression modeling for Data Analytics and Actuary Science majors with statistical software. Topics include: multiple linear regression and analysis, linear time series models, moving average, regression-based and/or ARIMA models, estimation, data analysis and forecasting with time series models, forecast errors, and confidence intervals. Prerequisites: SMA140 and SMA265. Spring semester. 3 credits.