Data Science and Analytics, a field that also goes by the names of Data Science, Data Analytics, and Predictive Analytics — is also related to “Big Data,” “Machine Learning”, or “Artificial Intelligence”. In the Bachelor’s in Computer Science Data Analytics degree program, students combine computer science, statistics, and project management with in-depth knowledge of a specific discipline to become expert analysts. They then apply their expertise in the real world through internships that prepare them for exciting careers in their chosen field.
This major is brought to Woodbury University by a $3 Million US Department of Education Title V Grant.Apply Request Information Take a Tour
Big data has immense opportunities. Technological advancement also creates new and exciting opportunities for those who use, analyze, and understand the ever-changing technology. The Bureau of Labor Statistics reports enormous projected job growth in the field of mathematical science—as much as 27.9 percent from 2016 to 2026. Data Scientists are being employed in nearly every industry today, from social media to pharmacology to transportation and automation.
The labor market demand for computer science degrees and related disciplines is growing substantially
The core courses in the CSDA major comprise a 15-unit core in common with the Applied Computer Science program. The curriculum also includes the core of mathematics courses required of majors in mathematics, sciences, engineering and technology.
Computer Science I (Lab, 3 units)
This class provides a foundation in computational literacy, allowing students from a variety of disciplines to read, write, and interpret code. The course will inform through assigned readings, lectures, and workshops that programming is not only technical skill but an essential form of literacy. It serves as a standalone course for those seeking to understand the basics of programming. The course structure is based on the “creative coding” model in which students work with programming languages to produce interactive graphics beginning on the first day of class. Principles such as conditional statements, Boolean operations, loops, functions, and classes will be covered in an applied manner, allowing students to tie syntax and semantics of code to real-time graphics. Prerequisite: None.
Computer Science II (Lab, 3 units)
This course is a continuation of CORE 101: Computer Science I. This course introduces basic principles of algorithmic and object-oriented problem solving, programming language concepts, including control structures, data types, and classes. It also provides an introduction to Arrays, Inheritance, File I/O, and GUIs. Problem analysis, program design, development and implementation, and related topics are covered. Students complete several programming projects using an appropriate computer language. Prerequisite: CORE 101, Computer Science I
Data Structures and Algorithms (Lab, 3 units)
This course provides a study of algorithms and their related data structures, including linear lists, linked lists, trees, graphs, sorting techniques, and dynamic storage allocation. The algorithms are used to manipulate these structures and their applications. Applications are implemented using an appropriate computer language. Prerequisite: CORE 102, Computer Science II
Windows-Based Application Development (Lecture, 3 units)
In this course, students will learn how to create Windows-based applications using Visual Studio and the .NET Framework. This course teaches the fundamental concepts behind these applications, including event-driven programming, and will use both C# and Visual Basic .NET languages. Students will also create frontends to databases, design games, build their controls, and write programs that interact with Microsoft Office software. Prerequisite: CORE 102, Computer Science II
Big Data Learning Analytics (Lecture, 3 units)
This course provides in-depth coverage of various topics in big data, from data generation, storage, management, transfer to analytics, with a focus on the state-of-the-art technologies, tools, architectures, and systems that constitute big-data computing solutions in high-performance networks. Real-life big data applications and workflows in various domains are introduced as use-cases to illustrate the development, deployment, and execution of a broad spectrum of emerging big-data solutions. Prerequisite: CORE 102, Computer Science II
Database Design and Programming (Lecture, 3 units)
This course provides in-depth coverage of database concepts, relational and non-relational database systems, database environment, theory, and applications. The design, development, and implementation of database systems are included. A practical database project is developed by students utilizing a popular database development system. Students generate user interfaces and reports. Prerequisite: CORE 102 Computer Science II.
Analytic Geometry I (Lecture, 5 units)
This course covers limits, derivatives, applications of differentiation, integrals, and the fundamental theorem of calculus. Proofs of primary calculus theorems are reviewed. Pre-requisites: MATH 251, Trigonometry with a grade of “C” or better
Analytic Geometry II (Lecture, 5 units)
Techniques of integration, numerical integration, improper integrals, and applications of the integral. Taylor polynomials, sequences and series, and power series are also studied. Pre-requisites: MATH 260, Analytical Geometry I with a grade of “C” or better
Linear Algebra (Lecture, 3 units)
An introduction to matrix algebra, vector spaces, and linear transformations. Topics include systems of linear equations, subspaces, linear independence, bases and dimension, abstract vector spaces, orthogonality, least-squares methods, inner product spaces, determinants, eigenvalues, and diagonalization. Pre-requisites: MATH 260, Analytical Geometry I with a grade of “C” or better
Discrete Math (Lecture, 3 units)
An introduction to the mathematics needed in computer science. Logic and boolean algebra, discrete logic circuits (apps of and/or/nor), number systems, proofs, set theory, matrix theory, counting methods, discrete probability, sequences, induction, recursion, counting, and graph theory (including trees). Pre-requisites: MATH 149, Intermediate Algebra with a grade of “C” or better, or Placement
Applied Artificial Intelligence (Lecture, 3 units)
This course provides an introduction to the basic principles, techniques, and applications of Artificial Intelligence. Some of the specific topics include knowledge representation, logic, inference, problem solving, search algorithms, game theory, perception, learning, planning, and agent design. Students will experience programming in AI language tools. Potential areas of further exploration include expert systems, neural networks, fuzzy logic, robotics, natural language processing, and computer vision. Prerequisites: CORE 201, Data Structures and Algorithms.
Advanced Data Structures and Algorithm Analysis (Lecture, 3 units)
This course is a continuation of CORE 201. The course explores the advanced data structures (including trees and graphs), the algorithms used to manipulate these structures, and their application to solving practical computer science and data analytics problems. A vital element of the course is the role of advanced data structures in algorithm design and the use of amortized complexity analysis to determine how data structures affect performance. Prerequisites: CORE 201, Data Structures and Algorithms.
Advanced Database Development (Lecture, 3 units)
This course explores advanced topics in client server and database development. It covers the programming and administration of database systems and includes views, stored procedures, triggers, indexes, constraints, security, roles, logs, maintenance, transaction processing, XML, reporting, and other relevant topics. Students will be exposed to several database packages and will perform considerable database programming. Pre-requisites: CSDA 210, Database Design and Programming
Data Mining (Lecture, 3 units)
An introduction to basic concepts behind data mining. Survey of data mining applications, techniques, and models. Discussion of ethics and privacy issues concerning invasive use. Introduction to data mining software suite. CSDA 400, Advanced Database Development.
Machine Learning (Lecture, 3 units)
Machine learning uses interdisciplinary techniques, such as statistics, linear algebra, optimization, and computer science, to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. This course introduces several fundamental concepts and methods for machine learning. The objective is to familiarize the students with some basic learning algorithms and techniques and their applications, as well as general questions related to analyzing and handling large data sets. Several software libraries and data sets publicly available will be used to illustrate the application of these algorithms. The emphasis will be thus on machine learning algorithms and applications, with some broad explanation of the underlying principles. CORE 201, Data Structures and Algorithms.
Senior Project (Lecture, 3 units)
This course provides an opportunity for students to apply theories, ideas, principles, and skills learned in the classroom to a project of problem solving in practice. Using the internship, students further develop skills for becoming data analytics professionals. The internship experience is about understanding data analytics and business needs and practices within an organizational context, including their culture, computing and management systems, operations, resources, products, services, markets, service areas, and specialty areas. The experience is obtained in organizations approved by the CSDA Department under the guidance of a Woodbury faculty supervisor and a qualified mentor at the selected organization. Senior Standing; Computer Science in Data Analytics Major.
Internship (Lecture, 5 units)
A work experience is a graduation requirement of all CSDA students. CSDA 490X, Internship is a co-requisite to apply for internship hours. Students will keep and submit internship journals as part of this course. Students will also fulfill internship requirements, such as obtaining signed evaluations from host company supervisors indicating that they have completed the accompanying internship successfully and demonstrated appropriate professional conduct. Students may enroll in CSDA 490, Internship for additional credit hours with the permission of the chair.
Probability and Statistics I (Lecture, 3 units)
Introductory probability covering the design of experiments, axioms of probability, sample spaces, probability rules, independence, conditional probability, Bayes’ Theorem, discrete and continuous random variables, expectation, moment generating functions, and central limit theorem. Also covered are various distributions, including joint, binomial, Poisson, geometric, normal, exponential, Chi-square, Student’s t, and uniform. Pre-requisites: MATH 261, Analytical Geometry II with a grade of “C” or better
Probability and Statistics II (Lecture, 3 units)
This is the second course in probability and statistics and covers survey sampling, estimation theory, confidence intervals, hypothesis testing, linear regression, and correlation and analysis of variance, and real-world applications. Prerequisite: MATH 310 with a grade of “C” or better.
Applied Statistical Analysis (Lecture, 3 units)
Review of descriptive statistics, hypothesis testing and estimation, least square method, and Gauss-Markov theorem. SAS and R programming language are taught, including procedures to carry out simple linear regression and multiple linear regression, non-linear regression, model selection and model diagnostics, report generation, and working with large data sets. Prerequisite: MATH 311 with a grade of “C” or better.
Topics in Computer Science in Data Analytics (Lecture/Lab: Varies, 3 units)
Special course offerings dependent upon the interest of students and faculty. Prerequisites: Varies
Independent Study (Lecture/Lab: Varies, 1-6 units)
Individual investigation in an area of special interest selected by the student with the approval of an appropriate member of the faculty. Regular or periodic meetings with the assigned faculty member are required. Thirty hours required for each unit of credit. Prerequisites: Permission of the department chair.
Combinatorics (Lecture, 3 units)
A one-semester introduction to combinatorics. Topics include enumeration, generating functions, recurrence relations, construction of bijections, introduction to graph theory, Polya’s Theorem, network algorithms, and extremal combinatorics. Prerequisites: Mathematical Statistics 2 and Linear Algebra both with a grade of “C” or better.
Statistics (Lecture, 3 units)
This course will introduce students to statistical methods and practices which are most relevant to the analysis of financial and economic data. Topics include autoregressive models, moving average models, and their generalizations. The course develops models that are closely focused on particular features of financial series such as the challenges of time dependent volatility. Prerequisites: Mathematical Statistics 2 and Linear Algebra both with a grade of “C” or better.
Spatial and Geo Statistics (Lecture, 3 units)
Topics cover practical spatial and geostatistical analysis, including spatial and temporal autocorrelation, point patterns, interpolation, and multivariate analysis. Prerequisites: Mathematical Statistics 2 and Linear Algebra both with a grade of “C” or better.
Topics in Mathematical Statistics (Lecture, 3 units)
Topics selected from statistics and/or probability, such as nonparametric statistics, multivariate statistics, experimental design, decision theory and advanced probability theory. Mathematical Statistics 2 and Linear Algebra both with a grade of “C” or better.
Topics in Probability and Statistics (Lecture, 3 units)
This course will cover topics in probability and statistics not covered elsewhere in the program. Part A is usually devoted to multivariate statistics, Part B to stochastic processes, and Part C to probability theory. Part D is left to a topic chosen by the individual instructor. Prerequisites: Mathematical Statistics 2 and Linear Algebra both with a grade of “C” or better.
Many career opportunities exist for Data Science & Analytics degree holders. Depending on your chosen concentration, or area of expertise, career possibilities for Woodbury graduates include:
Are you ready to pursue your Bachelor’s in Computer Science Data Analytics degree? Are you wondering if Woodbury is right for you? Visit us—it’s the best way to get a feel for what the Woodbury community is all about. You can also request more information about the Computer Science Data Analytics program.
Our Computer Science Data Analytics (CSDA) is helping to create leaders with a deep understanding of the mechanics of working with data and the capacity to identify and communicate data-driven insights that ultimately influence decisions. Our CSDA program prepares students with a professionally focused, on-trend educational experience—led by expert faculty. Through experiential learning opportunities, collaboration between a diverse group of fellow students, our CSDA program produces the next generation of knowledgeable, experienced analytics leaders.
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