Minor in Statistics
Overview
Statistics can shape how we view society and culture –– identifying trends, finding patterns and making decisions in the face of uncertainty. Statisticians interpret raw data and create meaning out of numbers, helping audiences understand why it matters and what it says about our world. Biola’s statistics minor gives students the tools to look at data critically and in new and thoughtful ways. You’ll learn how to analyze data, graph and create clear-cut and dynamic presentations for target audiences. This rigorous minor offers a deep dive into theory. Through our practicum class, you’ll practice applying the theories you’re learning to real-life situations. Because of our distinctive apprentice approach in the practicum class, you will learn the full arc of completing statistical projects. Integrating your biblical training throughout our technical program, you’ll be prepared to work in the field of statistics or pursue a graduate degree.
Courses
Below are the course requirements for this academic program. In addition to these program-specific requirements, all majors include Biola's traditional undergraduate core curriculum. For more program details, including a sample course sequence, visit Biola's academic catalog.
Minor
Choose one of MATH 318 or MATH 332. Choose one of MATH 319, MATH 380, CSCI 106, CSCI 402, CSCI 440, BUSN 323 or BUSN 423.
| 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. | |
| BUSN 423 | Advanced Business Analytics |
| This course will continue to prepare students for a career in business analytics. Using case studies, students will synthesize and apply a variety of business analysis methodologies. Students will learn needed database concepts, data mining methods, and other digital technologies needed to work with large, unstructured data sets. Grade Mode: A. | |
| 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 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 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 205 | Calculus III |
| Functions of two and three variables, partial differentiation, multiple integration, curves and surfaces in three dimensional space. 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. | |
| MATH 331 | Probability |
| Samples spaces, axioms and elementary theorems of probability, combinatorics, independence, conditional probability, Bayes' Theorem, one and higher dimensional random variables, special and multivariate distributions. When Offered: Alternate years. Grade Mode: A. | |
| MATH 332 | Statistics |
| Estimation: consistency, unbiasedness, maximum likelihood, confidence intervals. Hypothesis-testing; type I and II errors, likelihood ratio tests, test for means and variances; regression and correlation, Chi-square tests, decision theory, nonparametric statistics; application of statistical methods. When Offered: Alternate years. Grade Mode: A. | |
| MATH 380 | Statistical Consulting Practicum |
| Practical experience of applying statistical methods to real-world statistical consulting problems. Initial meeting with client, converting problem to solvable form, conducting analysis, and presenting results to client. Attention given to 'soft' (consultant-client interaction, effective group work, presentation skills) and 'hard' (analysis use of statistical software) aspects of consulting process. Notes: Special approval required. Grade Mode: A. | |