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Current Student Resources

Math and Computer Science

From lab schedules to career resources, explore the resources and opportunities available to our current students for their academic and professional growth.


Math Program

Advising Guidelines for Biostatistics (MATH 318)

History

In the mid-2000’s some nursing students, who were required to take MATH 210 Introduction to Probability Statistics (lower division) were hearing from graduate programs that they needed to take an upper division statistics course to qualify for the program. The nursing department investigated and found out that 7 of 22 graduate nursing programs surveyed required an upper division course. Prior to MATH 318, Biola had no upper division entry-level statistics course that was not major specific. MATH 318 was created to fill this gap. It is NOT major specific and is open to all able students.

Purpose

To provide an upper division entry-level statistics course to the Biola student that will satisfy the math/science requirement and may, therefore, be taken in replacement of MATH 210 --Introduction to Probability and Statistics — there are no prerequisites.

Idea of Course

Since it was prompted by the needs of the nursing department, it has been named Biostatistics and will be run with an emphasis on applications to the biological and health-related sciences. It is, however, a thoroughgoing introduction to statistics, with the requisite probabilistic background covered. It is designed for students with a stronger quantitative background than is required for MATH 210. It will cover the usual introductory statistical topics in descriptive and inferential statistics, including hypothesis testing with linear models (regression and ANOVA). In general, MATH 210 will emphasize more of a conceptual understanding of statistics whereas MATH 318 will emphasize the specific skills needed to both analyze and interpret data. MATH 318 will require, and provide instruction on the use of statistical software.

Target Audience

Nursing majors, biology majors, and other quantitatively oriented students who are considering taking MATH 210

​​For more specific questions, please contact the Professor, Jason Wilson jason.wilson@biola.edu or x 5145.

Business Math Course Information

Biola MATH 210 Introduction to Probability and Statistics, AP Statistics score of 3 or higher, or equivalent may be substituted. Similar statistics courses from other schools can be substituted. Compare the catalog descriptions.

Course Offerings

Every Fall and Spring Math Courses

  • MATH 124 MATH 124 Quantitative Reasoning for the Real World
  • MATH 190 Business Statistics
  • MATH 210 Intro to Probability and Statistics
  • MATH 318 Biostatistics
  • MATH 370 Readings in Mathematics
  • MATH 380 Statistical Consulting Practicum
  • MATH 470 Data Science Capstone

Every Fall Math Courses

  • MATH 117 Fundamentals of Math for Elementary Teachers I
  • MATH 150 Calculus I
  • MATH 250 Calculus III
  • MATH 291 Linear Algebra

Every Spring Math Courses

  • MATH 118 Fundamentals of Math for Elementary Teachers II
  • MATH 151 Calculus II
  • MATH 203 Discrete Structures
  • MATH 204 Introduction to Abstract Math
  • MATH 333 Operations Research
  • MATH 335 Ordinary Differential Equations

"Even Years" Fall Math Courses (2022, 2024, 2026…)

  • MATH 140 Fundamentals of Calculus
  • MATH 319 Statistics II
  • MATH 321 Numerical Analysis
  • MATH 341 Classical Geometry
  • MATH 450 Abstract Algebra II

"Odd Years" Fall Math Courses (2023, 2025, 2027…)

  • MATH 331 Probability
  • MATH 410 Intro to Real Analysis II
  • MATH 415 Number Theory and the History of Math

"Even Years" Spring Math Courses (2024, 2026, 2028…)

  • MATH 125 Precalculus
  • MATH 315 Abstract Algebra I
  • MATH 332 Mathematical Statistics
  • MATH 440 Complex Variables

"Odd Years" Spring Math Courses (2023, 2025, 2027…)

  • MATH 305 Intro to Real Analysis
  • BBST 465 God and Math

Math Research Seminar Courses

These courses are offered when there is enough student interest or need and faculty are available to teach them. Contact the Math and Computer Science Department for more information.

  • MATH 480 Research Seminar – subjects include but are not restricted to: Putnam Exam Preparation, Linear Algebra II, Topology

Note: Extenuating circumstances sometimes create a change in our scheduling of our courses. Please verify course schedules on Biola’s course schedules in your Biola MyAccount when planning your coursework.

Please contact our department for questions, math.csci.dept@biola.edu.

Liberal Studies Requirements

Math 117 Fundamentals of Mathematics 1

Math 117 reviews the development of elementary math concepts from basic number sense to algebra. Emphasis is on mastery of the content as well as developing an understanding of basic arithmetic algorithms involving whole numbers and rational numbers. How children understand and perform math tasks is studied. This course is designed according to the State of California and the Biola School of Education standards.

Math 118 Fundamentals of Mathematics 2

Math 118 reviews the development of math concepts from algebra to probability and statistics to geometry. Emphasis is on mastery of the content as well as developing an understanding of how children learn and perform math tasks in the above content areas. This course is designed according to the State of California and the Biola School of Education standards.

Focused Area of Study Requirements

The requirements for the mathematics focused area of study consists of three lower division courses and one upper division course selected from the approved list:

  • Lower Division
    • Math 101 — Precalculus
    • Math 103 — Calculus for Management Sciences
    • Math 105 — Calculus 1
    • Math 106 — Calculus 2
    • Math 112 — Discrete Structures
    • Math 130 — Honors Nature of Math
    • Math 210 — Introduction to Probability & Statistics
    • Math 291 — Linear Algebra
  • Upper Division
    • Math 315 — Abstract Algebra I
    • Math 318 — Biostatistics
    • Math 331 — Probability
    • Math 332 — Statistics
    • Math 341 — Classical Geometry
    • Math 415 — Number Theory & History of Math

Rationale

The rationale for including three lower division courses in the requirement is that these courses present significant extensions of the problems and concepts required for elementary and middle school mathematics. At the upper division, many courses are so abstract as to provide little help for the prospective elementary teacher. Also, many upper division courses require at least 2 or 3 semesters of calculus (and sometimes additional courses) as prerequisites. Inclusion of such courses as a requirement would make it very unlikely for many students to pursue this focused area of study. On the other hand, a well-qualified student may choose to select two lower division courses and two upper division courses.

The list of approved courses provides alternatives for students from which to choose based on their background and interests. In order to serve as a guide for those choices and illustrate the common combinations, five options have been indicated below.

All options provide the student with exposure to statistics. Options 1-3 are the broadest, and would probably appeal to the largest percentage of students. Carefully selected courses allow students to pursue particular curricular areas in greater depth. Options 4 and 5 provide opportunities of this type, and may appeal particularly to students with an especially strong background in mathematics from high school. Option 4 requires the strongest prerequisite background, and provides the student with an especially strong concentration in probability and statistics. Option 5 has an advanced algebra focus.

Option 1Option 2Option 3Option 4Option 5
Math 101Math 101, 103 or 105Math 101, 103 or 105Math 105Math 112
Math 103 or 105Math 112Math 112Math 106Math 210
Math 112 or 130Math 130 or 291Math 210Math 201 or 318Math 291
Math 318Math 318Math 341 or 415Math 331Math 315


The following will outline how the courses proposed would provide a student with the background to develop core curriculum in mathematics for grades K-8, based on the Mathematics Framework for California Public Schools.

Math 101 builds on the base of Math 117 and Math 118 (required for all Liberal Studies students) to develop and illustrate the concept of function and to show how functions are used to model real-world situations in disciplines such as business, physics chemistry, and geology. Coordinate geometry is developed, and related to problem solving. Algebraic and geometric solutions to problems are compared.

Math 103 continues and extends the study of functions (including logarithmic and exponential functions) and coordinate geometry. Basic techniques of graphing and algebra are used extensively. Problem-solving and applications are stressed.

Math 105 continues and extends the study of functions and coordinate geometry described above under Math 101. Basic techniques of graphing and algebra are used extensively. Problem-solving and applications are stressed. Technology is utilized.

Math 106 continues and extends the study of functions (including stress on inverse functions) and coordinate geometry described above under Math 105. Basic techniques of graphing and algebra are used extensively. Problem-solving and applications are stressed.

Math 112 builds on the base of Math 117 to develop the fundamental concepts of sets, relations and functions, especially in a discrete setting [especially appropriate for the kindergarten through grade 8 learners]. Number base systems are discussed, as are combinatorial concepts and problem-solving. Basic concepts of probability are also introduced in the discrete context, building on Math 118.

Math 130 includes topics from number theory, Euclidean and non-Euclidean geometry, algebra, analytic geometry, probability, set theory and infinite sets. Math is viewed in its cultural and historical settings with connections to literature and philosophy.

Math 210 develops the basic concepts of probability and both descriptive and inferential statistics. A broad variety of applications are illustrated.

Math 291 develops the study of problems from algebra and coordinate geometry, such as system of linear equations, coordinate transformations, etc.

Math 315 develops some concepts foundational to various number systems, including finite systems. Some concepts also apply to geometrical problems.

Math 318 develops the basic concepts of probability and both descriptive and inferential statistics. A variety of applications are presented from the biological and health sciences.

Math 331 includes a treatment of basic set theory and combinatorics for use in discrete probability. Continuous random variables are also studied. This treatment is much more in-dept that in Math 118 and Math 112. Problem-solving and conceptual understanding are both stressed as a mature level.

Math 332 continues Math 331, and provides a study of the theory of mathematical statistics. The student who completes Math 331 and 332 would have excellent background to develop the expanding probability and statistics portion of the elementary and middle school curriculum.

Math 341 develops elementary and advance tops of Euclidean geometry well beyond Math 118. Compass and straightedge constructions in plane geometry are studied, giving geometry a hands-on approach. Problem-solving and conceptual understanding are both stressed.

Math 415 develops classical number theory on the foundation of such principles as the Fundamental Theorem and Arithmetic and the Division Algorithm. The properties of greatest common divisor and lease common multiple are studied, as are divisibility issues. Prime numbers are studied extensively. A significant focus of this course is on the development and examination of conjectures by students. The role of induction and deduction is discussed. This course also includes a study of the history of mathematics. In this context, the value of mathematics as it has been perceived in different cultures is considered. Students are required to write a research paper, and make oral presentations.

Math Lab Schedule

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View Math Lab Schedule

Math Socieites

Matheology Historical Timeline

The “Matheology: A Historical Timeline of the Relationship between Mathematics and Theology” is an ongoing project of Biola University’s Math and Computer Science students. View the project below.

View Matheology Timeline

Putnam Exam

The Putnam Exam is a national mathematics contest for undergraduate students. It is held each year on the first Saturday of December. Questions range over all subjects covered in undergraduate math courses: calculus, linear algebra, abstract algebra, number theory, probability, combinatorics, geometry, etc.

Each exam is given in two sessions: one three-hour session in the morning, and one three-hour session in the afternoon. Six problems are given during each session. Up to 10 points can be earned on each problem, so there is a total of 120 points available to be earned. The questions are (usually) extremely difficult, and receiving just 1 point on the Putnam exam is considered an achievement.

Roughly 3000 undergraduate mathematics students take the Putnam exam each year. The names of the top 500 finishers are published, with extra acclaim going to those in the top 200. These lists are sent to graduate schools across the country; those who do well on the Putnam exam will have a much stronger chance of getting into the graduate school of their choice.

As an added benefit, the Biola math department offers a small cash prize for those that score at least 6 points on the Putnam exam. The first five points are worth nothing; the next five points are worth $5 each, and every subsequent point is worth $15.

Biola offers a 1 unit Putnam review class (Math 480) every fall semester; we discuss various problems from past exams, and strategies that can be used for solving them.

Check out this Putnam Archive webpage for a list of all past Putnam problems and solutions used since 1995.

If you are a current Biola student and wish to take the Putnam exam this December, please contact Joseph DiMuro at joseph.dimuro@biola.edu.

RAMP Donations

Dear Potential Donor:

In 2009, the LORD brought us a visionary donor with start-up funds, and a strategy, to attract top students to our thoroughly Christian program. Before sharing the strategy, let me first tell you a little about our university.

The goal of Biola is to glorify God in all that we do. Biola offers a distinctly biblical education, whose mission is “equipping men and women in mind and character to impact the world for the Lord Jesus Christ.” In order to keep our Christian commitments as directed by God, we accept no government funding. Unfortunately however, in order to advance today’s scientific marketplace, many top Christian students regularly select secular research universities over Christian liberal arts universities because of finances or better perceived research opportunities. This weakens the body of Christ in the long run because of the humanistic worldview adopted by many students trained in such programs. Our goal is to attract some of these students to Biola’s math department, which integrates a biblical worldview into all classes and provides a godly campus atmosphere conducive to spiritual growth.

In consultation with the visionary donor mentioned above, the Research Assistant in Mathematics Program (RAMP) was formed. The strategy involves a spiral with three parts.

  1. Attract a few top students to our department with RAMP-assisted research funding or scholarships.

  2. Prayerfully cultivate student success in research and the national math competition

  3. Use the research and competition fruit to attract more students -- throughout this upwards spiraling process, students will be continually discipled in Biola’s dynamic Christian environment.

The cost of supporting one research assistant, or one RAMP scholarship, is $1000/semester, or $2000/year. We currently have funds for one research assistant per year for five years. Because the program is embedded within our existing structure, 100% of donated funds go to RAMP students. If you would like to help build the RAMP to launch the next generation of Christian mathematicians to the next level, here’s how:

  1. Pray for God’s blessing and spiritual protection of our department and students.

  2. Give financially. Biola is a 501(c)(3) non-profit organization and all donations to Biola and RAMP are tax deductible. There are two ways to give:
    • Online: Go to https://giving.biola.edu/, click “Give Now”, and in the “Designation” menu select “Other” and put RAMP – Math Department.

    • Mail: Make your check payable to “Biola University”, put “RAMP” or “Math Research” on the comment line, and mail to:
      Biola Math Department, 13800 Biola Ave., La Mirada, CA 90639

  3. Spread the news about what God is doing in the Biola math/computer science department.

We welcome input from donors and would be glad to provide more information or discuss these things with you. Thank you very much for your interest in our program.

Yours in Christ,
Jason Wilson
Secretary, RAMP Committee

Research Assistant in Math Program (RAMP)

Goal

The goal of the Research Assistant in Math Program (RAMP) is to create an undergraduate research environment where Biola math and computer science majors are supported in research projects that (i) complement their academic goals and (ii) facilitate department growth, which together further Biola’s mission. RAMP is funded by a visionary donor to the department. Research Assistants (RA’s) through RAMP are paid to assist faculty with research.

RAMP Committee

  • Joseph Dimuro, Ph.D (Co-Chair)
  • John Kwak, Ed.D (Co-chair)
  • Jason Wilson, Ph.D (Secretary)

Funding

Biola is a private, non-profit 501(c)(3), Christian University and does not accept governmental funding. RAMP is supported exclusively through private donors and all donations are tax deductible. We believe that the greater the success of RAMP, the more equipped our students will be to fulfill the biblically mandated mission of Biola University, which is to “equip men and women in mind and character to impact the world for the Lord Jesus Christ.” If you would like to help ‘build the RAMP,’ please go to the financial information page for donation information.

Completed Projects

  • Brian Zarske (2020). Detecting Illegal Sign Stealing in Baseball — Following the publication of Major League Baseball Commissioner's reports in 2020 of sanctions against the Houston Astros for illegal sign stealing in 2017 and Boston Red Sox in 2018, we investigated whether our Quality of Pitch (QOP) statistic could be used to detect the sign stealing activity. The detection was successful in both cases. The Astros signaled whether the next pitch was a fastball or off-speed pitch, at home games, to a subset of hitters via bangs from the dugout. The Red Sox signaled the next pitch to all batters, through the runner on second, at home games. In order to achieve detection, we had to tailor the search to sign stealing method.
  • Joseph Lane, Brian Zarske, Colin Van Meter (2019). Developing a Mathematical Model to Quantify Deception in Baseball, Phase 1 — We researched pitcher deception tactics and studied the relationship between pitch trajectory differentials, speed differential, and location differential, all with respect to batter performance. Results were documented in anticipation of Phase 2.
  • Joseph Lane (2019). Explaining the 2019 MLB Home Run Record with Quality of Pitch — Although home runs were down in 2018, the 2017 record was smashed in 2019 with further documented changes to the ball. While most commentators overlooked the ball, we showed that the record all time low quality of pitch (QOP) accompanied the home run record. The primary change was found to be location – pulling the ball from the middle of the strike zone to low and inside. Joseph performed calculations for the project, as well as editorial work.
  • Jeremiah Chuang (2018). Explaining the 2017 MLB Home Run Record with Quality of Pitch — There was a surge in home runs in major league baseball in 2017 and almost everyone attributed it to changes in the ball, and the batter’s approach to hitting. In this research, we showed that the quality of pitch (QOP) was also a factor. In 2017 QOP was down, primarily due to a decrease in vertical break. Jeremiah performed calculations for the project, as well as editorial work.
  • Brian Queme; Angela Cuerpo; JT Yarter (2017). Distances to Grocery and Convenience Stores in Low- and High-Income Neighborhoods: A Limiting Factor to Accessing Healthy Foods — Studies have shown that diet related disease is more prevalent in low-income neighborhoods and that there is a relationship between diet related disease and the absence of grocery stores. Twenty-five random GPS residential locations in twelve cities in Los Angeles County were generated, and the distances to the two closest grocery stores (i.e. stores with more than five kinds of fresh fruits and vegetables) and convenience stores were measured. After statistical analysis, it was found that homes in low-income neighborhoods are significantly closer to convenience stores than grocery stores, whereas homes in high-income neighborhoods are closer to grocery stores.
  • Jeremiah Chuang (2017). Quality of Pitch (QOP) — Investigations into major league baseball using our Quality of Pitch (QOP) statistic. Does a batter’s handedness influence a pitcher’s QOP? Can the QOP average of a team help predict the likelihood of victory in a major league baseball game?
  • Pixler (2015). Quality of Pitch (QOP) – Calculating a Single Number to Rate the Quality of a Baseball Pitch — Throughout 2015, Josh Pixler assisted Dr. Wilson in the development of the Quality of Pitch statistic (QOP). Using PITCHf/x data from major league baseball stadiums, they extracted the pitch trajectory, location, and speed, and combined this information into a single number to quantify pitch quality. Joel assisted in the development of the case study on the LA Dodgers 2014 season and accompanied Dr. Wilson to the Society for American Baseball Research where it was presented. See Presenter's List: http://sabr.org/latest/2015-sabr-analytics-conference-research-presentations; See Presentation: Pixler (2015). Quality of Pitch (QOP) – Calculating a Single Number to Rate the Quality of a Baseball Pitch
  • Molly Folkert (2014). Gospel Inventory Factor Analysis -- In Fall 2013, an inventory to assess Biola student's understanding of the gospel was conducted by Dr. Wilson on a random sample of 110 Biola students. In this work, Molly Folkert performed a Factor Analysis on the data to test Dr. Wilson's hypothesis that the 23 inventory items could be well explained by just three underlying factors. PDF of Paper: A Survey in Factor Analysis.
  • Sam Britton, Daniel Lundstrom, Joshua Sansonetti (2013). Probabilities of Qwirkle Hand Values — Calculations of the theoretical probabilities of various Qwirkle hands, supported by Monte Carlo simulations of the empirical probabilities.
  • Jolene Houtsma (Spring 2013). Integration of Faith and Learning Assessment -- In Spring 2012, an inventory to assess the Integration of Faith and Learning was conducted by Dr. Wilson on a random sample of 269 Biola undergraduate students. In Spring 2013, Jolene Houtsma administrated the 12-month follow-up study. Her tasks included organizing and communicating with ten undergraduate volunteers from Dr. Wilson’s classes who contacted the respondents. PDF of Article: Houtsma (2013), Integration of Faith and Learning Assessment.
  • Jolene Houtsma (Spring 2013) Educational Tolerance — In Spring 2013, a student of Dr. Patrick Wolf, University of Arkansas College of Education named Albert Cheng selected Biola university as an ideal population to conduct an educational tolerance study. The reason is that we have a sizable population of public, private, and homeschool students, which is uncommon. Mr. Cheng solicited Dr. Wilson’s help in collecting the data. The data collection became a joint effort between volunteers from Dr. Wilson’s statistics classes and his Research Assistant, Jolene Houtsma. The students were: Daniel Chapman, Lauren Chen, SueZen Chew, Sarah Hurlburt, Garret Huckaby, Scarlett Liu, Katie McCusker, Joshua Sansonetti, Maddison Salcido, Chloe Willms, and Samantha Wilson. Jolene administrated the data collection process from start to finish, including data cleaning. See full text article: Houtsma (2013), Educational Tolerance.
  • Steven Oatey (Fall 2013, Spring 2013) Business As Mission -- We were contacted by Business ProfessorSteven Rundle to analyze data from a survey. The respondents were from businesses sponsored by churches, para-church organizations, and privately run businesses in a foreign country whose purpose is to promote the gospel. The task was to answer specific questions from the data, as well as provide summary tables of the results: Business As Mission Summary Tables.
  • Mary Frank (Summer 2012) Crime Scoring -- We were contacted by Biola Alumnus Joe Silva to develop a proprietary algorithm regarding crime. Dr. Wilson guided the research, but the project took Mary through a literature search, detailed analysis of more crime types than we’d ever want to know about, eventual creation of the algorithm, and summary into a report for the client. The plan is for the algorithm’s eventual use in web-based application. The report is not included here due to the proprietary nature of the work.
  • Laura Evans and Courtney Turek (Fall 2011), What Do Biolans Think Of Torrey Students? A Survey -- This article describes a statistics project completed by two Research Assistants in Fall 2011. The the survey was designed by students in Dr. Wilson's Introduction to Probability Statistics class, who also collected the data by passing out surveys in selected classes at Biola University. Respondents were asked general questions about themselves as well as their opinions of Torrey students (the honors program at the University). The data was then analyzed using the statistical software R. PDF of Article: Evans and Turek (2011), What Do Biolans Think Of Torrey Students? A Survey.
  • Kirk Spicer (Fall 2011), Analysis of Scarlet Macaw Data -- This project originated when Biola’s new Environmental Science professor, Mark McReynolds approached Dr. Wilson for statistical consultation regarding the analysis of data he had collected on Scarlet Macaws from the mountain rain forest of Beliz in 2008-09. Wanting to help, but not having adequate time to conduct the analyses himself, Dr. Wilson recruited Kirk Spicer as an RA for the project. Kirk cleaned the data (over 5000 rows and 4 columns on one of several Excel spreadsheets), loaded it into R, and condensed it into useable forms. He also conducted routine analyses and did some exploration. The entire process was guided by Dr. McReynolds original four questions, which form the basis of the report written summarizing the work. The results were used in Mr. McReynolds’ dissertation which he went on to successfully defend to earn his Ph.D in Environmental Studies.View Analysis: PDF of Article:Spicer (2011), Scarlet Macaw Data. After finishing the Scarlet Macaw project, Kirk continued RA work for Dr. Wilson in the areas of proving the asymptotic normality of the Probability of Correction Statistic, and the stages of cognitive development in geometry and other branches of math.
  • Erin Tao and Stephanie Greer (Spring 2011), Enhancing Multiple Testing -- This project started with Erin’s request to do her Torrey Honor’s Thesis with Dr. Wilson in something related to statistics. Stephanie Greer (Spring 2011) had already worked with Dr. Wilson as an RA applying her computer science skills to obtain the neuroimaging data for the project. The process extended Dr. Wilson’s primary technical research area of the Probability of Correct Selection statistic and combined Erin’s skills with R, Matlab, and writing. The paper is currently under review with the student research journal Involve. PDF of Article: Tao and Greer (2011), Enhancing Multiple Testing.

Writing Competency Paper Template

View and download the template for the math writing competency paper.

View Writing Competency Paper Template

Computer Science Program

Career Resources

Computer Science employment trends: Computer Science related jobs are among the best paid and highest satisfaction jobs of the projected high growth jobs. Among the list of best jobs in America 2014 according to the Wall Street Journal, two out of the top ten are computer science related jobs. It has been reported that three out of the top ten jobs are computer science related and there is a positive job outlook and growth projected by the U.S. Department of Labor between now and 2022. Computer scientists and related computer science occupations are likely to enjoy excellent job prospects because many companies report difficulties finding these highly skilled workers. See the following detailed information compiled by U.S. Department of Labor:

Computer Science careers: There is a wide range of possibilities for computer science graduates. We have alumni working for Boeing, Northrop, Intel, Apple, Microsoft, business corporations, engineering companies, hospitals, and missionary organizations such as Mission Aviation Fellowship and Wycliffe Translators. Many alumni serve as information technology staff, providing system administration, networking, hardware, and software support. Others are programmers, developing web portals and E-commerce applications connecting web interfaces to database systems, conducting data analysis and scientific computing, developing programs for linguistic analysis, designing and implementing computer games, and so forth. We also have graduates going to graduate programs of schools such as University of Southern California, UC Davis, UC Irvine, and UC Santa Barbara.

Biola's Center for Career Development: Find resources for writing your resume and developing skills for job search and interview from Biola's Center for Career Development. Computer Science professional societies and graduate schools: Find out more about Computer Science and graduate schools from the leading professional societies Association for Computing Machinery (ACM) and IEEE Computer Society.

Computer Science Lab Schedule

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View Computer Science Lab Schedule

Course Offerings

Courses offered fall and spring semesters

  • CSCI 104 The Nature of Computing
  • CSCI 105 Intro to Computer Science

Courses offered every fall semester

  • CSCI 230 Programming Languages
  • CSCI 305 Programming for Data Science I
  • CSCI 311 Operating Systems
  • CSCI 335 User Interface Design & Programming
  • CSCI 400 Theory of Algorithms
  • CSCI 402 Database Management

Courses offered every spring semester

  • CSCI 106 Data Structures
  • CSCI 220 Computer Organization and Assembly Language Programming
  • CSCI 430 Computer Communications
  • CSCI 440 Topics in CS: Robotics
  • CSCI 440 Topics in CS: Database Applications with Web and Mobile Interfaces
  • CSCI 450 Software Engineering

Courses offered fall every other year

  • CSCI 440 Topics in CS: Artificial Intelligence
  • CSCI 440 Topics in CS: Machine Learning

Note: Extenuating circumstances sometimes create a change in our scheduling of our courses. Please verify course schedules on Biola’s course schedules in your Biola MyAccount when planning your coursework.

Please contact our department for questions, math.csci.dept@biola.edu.

Internship and Research Opportunities

National Science Foundation REU Programs

(Research Experiences for Undergraduates Programs):
Each summer, National Science Foundation (NSF) provides a variety of research opportunities for undergraduate students through its Research Experiences for Undergraduates (REU) program. Typically a small group of ten or so undergraduates work in each program in the context of a specific research project supervised by the faculty and other researchers of the host institution. Students are granted stipends and assistance with housing and travel. Many Computer Science majors benefit greatly from the REU programs.

Computer Science Research Assistantship at Biola

Advanced students in the computer science program have the opportunities to participate in the research projects of the faculty. Under the supervision of the faculty, these students serve as research assistants and study important recent developments in computer science, including topics in areas such as information security, computer vision, natural language processing, knowledge representation and automatic reasoning, machine learning, and combinatorial optimization.

Internship Opportunities

Many Computer Science majors learn and benefit greatly from internship opportunities on campus and in the industry. For internship information see Biola's Center for Career Development website and explore opportunities at computer industry giant companies such as:

Writing Competency Requirements

The majority of computer science major students will fulfill their writing competency requirement by taking ENGL 313 (Writing in the Disciplines) during their junior year. This became the requirement starting with the 18/19 Biola catalog. If you are in an earlier catalog, you will use the following instructions and submit a paper to the computer science programs administrative coordinator. Also, if you have completed the IGETC, you will use this assignment in place of ENGL 313. If you are a transfer student, consult your computer science faculty advisor to see which method will be required for your situation.

*For Torrey Honors students starting with the 2020–21 Biola catalog, they will take ENGL 313 to fulfill the writing competency requirement. Students in earlier catalog years will follow the instructions below and submit their paper to the Department of Math and Computer Science.

For all students: Do not hesitate to contact your computer science faculty for assistance with this project.

  1. Overview:To fulfill the writing competency requirement in computer science, the student needs to find a well-defined topic in computer science, locate and study the related literature on the subject, write a literature survey paper based on the study, and submit the paper for evaluation and approval by the faculty.
  2. When to start: The student should start working on the writing competency paper in their junior year, and ideally should spend two semesters working on it.
  3. Identifying the topic: The first step of the process is to identify an interesting topic in computer science for an in-depth study. Survey articles in journals such as Communications of the ACM and AI Magazine (both available online from the Biola library) are good sources for identifying possible topics. The student is also encouraged to consult the faculty to look for a topic in the context of an upper-division course in a subject area of interest to the student.
  4. Development of a draft: The student is encouraged to consult the faculty to tailor the direction and the range of the study to cover the topic. The student should have a study plan on the selected topic and devise the framework of the paper to write accordingly. The student should then follow the study plan to flesh out the details of the paper based on the understanding/findings from the study. When the first draft is completed, the student may consult the sponsoring instructor for feedback before submission for evaluation.
  5. Rules of integrity: The paper should be the result of the study project to convey to the reader what the student has learned from the study. Plagiarizing contents from books and articles will end in discipline actions including the total rejection of the paper.
  6. Format and style of the paper:
    1. The paper should have a title, an abstract of its contents, a section of introduction to the main subject of the paper and the approach of the study, followed by the main technical sections to explore the subject, a section of conclusions on the findings through the study, and ended with references to books and articles cited in the paper.
    2. The paper should have at least eight pages in its contents (excluding the title page, the abstract, and the sources), double spaced between lines, using Times New Romans 12-point font or its compatible fonts.
    3. A paper written like a manual of computer systems is not acceptable. The paper should be written as a regular article with all technical terms and acronyms well defined before they are used in paper. The contents of the paper must coherently describe the topic, the study, and the findings.
    4. Instructions for Writing a Literature Survey Paper
    5. Computer Science WCR Paper Template
    6. Sample WCR Paper for Computer Science
  7. Final submission of the paper: The student should improve the draft based on the feedback from the sponsoring instructor and then submit the final paper to the department secretary. The paper should be submitted no later than the first week of the semester in which the student plans to graduate. The paper submitted should be registered by the department secretary first, and then evaluated anonymously by two faculty members. Accordingly the student should only put down the student ID instead of the full name on the paper.
  8. Due date and graduation: The paper should be submitted no later than the first week of the semester in which the student plans to graduate. Submission after the due date does not allow the time needed for the faculty to read and provide feedback and for the student to revise accordingly. Failing to submit the paper by the due date may end in the delay of graduation to a later semester. The paper will be approved as a fulfillment of the writing competency requirement in computer science if it receives at least an average of 3 points from the grading faculty.

Rubrics

Grading rubric: The quality of the paper will be evaluated according to rubric for computer science writing competency paper. The paper will be approved as a fulfillment of the writing competency requirement in computer science if it receives at least an average of 3 points from the grading faculty. If the paper fails to meet the standard, the student has to revise the paper further and resubmit the paper again.

View Rubric for Writing Competency Paper