Courses | B.S. in Computer Science
Below are some of the courses you'll have an opportunity to take as a student in this program. Take a look at the list below to get an idea of the types of available courses. Also, be sure to review core curriculum requirements and the official program requirements in the Biola University catalog.
Major Courses
All concentrations must include 24 upper-division credits. The following courses are required.
| CSCI 105 | Introduction to Computer Science |
| Introduction to computer hardware and software. Problem solving methods. Elementary concepts of algorithm development. C++ programming. Lecture/Lab Hours: Three hours lecture, one hour lab. Grade Mode: A. | |
| CSCI 106 | Data Structures |
| Linear lists, strings, arrays and orthogonal lists; graphs, trees, binary trees, multi-linked structures, searching and sorting techniques, dynamic storage allocation; applications. Grade Mode: A. | |
| CSCI 220 | Computer Organization and Assembly Language Programming |
| Fundamentals of digital logic and the architecture of modern computer systems, machine level representation of data, memory system organization, structure of machine languages, assembly language programming. Grade Mode: A. | |
| CSCI 230 | Programming Languages |
| Organization and structure of programming languages. Runtime behavior and requirements of programs. Introduction to programming language specifications and analysis. Study of various alternative languages such as Java, C++ and Python. Grade Mode: A. | |
| CSCI 430 | Computer Communications |
| Concepts of computer communications, local area networks, seven layers of communication protocols, global networks. When Offered: Spring. Grade Mode: A. | |
| CSCI 450 | Software Engineering |
| Concepts, principles, techniques, and documents of software engineering. Emphasis on systematic approaches to software engineering and the software life cycle. Team project required. Grade Mode: A. | |
Concentrations
Standard Computer Science
| CSCI 400 | Theory of Algorithms |
| Various types of algorithms, analytic techniques for the determination of algorithmic efficiency, NP-complete problems, complexity hierarchies, and intractable problems. When Offered: Alternate years. Grade Mode: A. | |
| CSCI 440 | Topics in Computer Science |
Topics are selected from the following:
Notes: Course may be taken multiple times for credit with different content. Grade Mode: A. | |
| MATH 105 | Calculus I |
| Limits, differentiation and integration of rational and trigonometric functions, with applications. Notes: Approved for Core Curriculum Math credit. Grade Mode: A. | |
| MATH 106 | Calculus II |
| Differentiation and integration of logarithmic, exponential and inverse trigonometric functions; various methods of integration; infinite sequences and series; parametric equations, polar coordinates. Grade Mode: A. | |
| MATH 112 | Discrete Structures |
| Elementary properties of sets, discrete probability and combinatorial analysis, graphs, relations, orderings, functions, simple algebraic structures, binary arithmetic and other bases, methods of proof. Grade Mode: A. | |
| MATH 291 | Linear Algebra |
| Topics from matrices, determinants, linear transformations and vector spaces. Grade Mode: A. | |
| MATH 321 | Numerical Analysis |
| Functions of one variable, approximate numerical solutions of non-linear equations and systems of linear equations, interpolation theory, numerical differentiation and integration, numerical solutions of ordinary differential equations. When Offered: Alternate years. Grade Mode: A. | |
| MATH 333 | Operations Research |
| Mathematical foundations of model building, optimization, linear programming models, game theoretic models. Grade Mode: A. | |
Data Science and Information Systems
| BUSN 220 | Management Information Systems |
Students will study Information systems, their design, implementation and contribution to management planning, decision-making and control. The impact of information systems on the personal and spiritual lives of students as well as their impact on broader society will also be covered. Students will learn relevant business software applications through hands-on lab assignments. Grade Mode: A. | |
| BUSN 323 | Business Analytics |
| Students will be introduced to the concepts of business analytics. Topics will include business modeling, optimization techniques, advanced spreadsheet techniques, and data visualization. Grade Mode: A. | |
| CSCI 305 | Programming for Data Science I |
Fundamental programming skills for data science applications using a major programming language such as Python or R in the field. Data analysis and information retrieval through data selection, iterative processing, function composition, abstraction, and visualization. Notes: Course may be taken twice for credit if different programming languages are used. Grade Mode: A. | |
| CSCI 402 | Database Management |
| Integrated database systems, logical organization, data description language (DDL), data manipulation language (DML), of hierarchical networks and relational databases, overview of selected database management systems (DBMS). When Offered: Alternate years. Grade Mode: A. | |
| CSCI 440 | Topics in Computer Science |
Topics are selected from the following:
Notes: Course may be taken multiple times for credit with different content. Grade Mode: A. | |
| MATH 203 | Discrete Structures |
Elementary properties of sets, discrete probability and combinatorial analysis, graphs, relations, orderings, functions, simple algebraic structures, binary arithmetic and other bases, methods of proof. Note(s): Completion of three years of high school mathematics strongly recommended. Grade Mode: A. | |
| MATH 210 | Introduction to Probability and Statistics |
| Nature of statistical methods, description of sample data, fundamental concepts of probability, probability distributions, sampling, estimation, correlation and regression, application of same. Notes: Approved for Core Curriculum Math credit. Grade Mode: A. | |
| MATH 318 | Biostatistics |
| Prepares the student for biostatistical application essential to practice in evidence-based professions. Content includes: descriptive statistics; probability theory and rules; discrete and continuous probability distributions; sampling distributions; confidence intervals; hypothesis testing; experimental design; ANOVA; linear and multiple regression; contingency table analysis; non-parametrics; survival analysis; discussion of the use of statistics in journal articles. Notes: Approved for Core Curriculum Math credit. Credit given for only one of 210 and 318. Grade Mode: A. | |