Minor in Applied Statistics and Data Science
Overview
Statistics and data drive culture, industry and new ideas. Journalists, businesses, public and private organizations rely on data and statistics to make informed decisions, to communicate new discoveries and emerging issues, and to introduce new products and ideas into the marketplace. Biola’s applied statistics and data science minor empowers students to look for hidden information or patterns and to discover how they influence key decisions. This minor is designed to be interdisciplinary — which means — you may be a journalist who discovers your next big story within a statistical pattern or a future psychologist who recognizes mental health and wellness data trends which inform your practice. Through our practicum course, you’ll practice applying what you’ve learned to real-life situations. Integrating your biblical training throughout our technical program, you’ll learn how to be a pivotal resource in your career field.
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
| 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. | |
| 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 319 | Statistics II |
A second course in statistics; covers statistical analysis in greater depth. Topics will generally include multiple regression, time series analysis, designing observational and experimental studies, data science, and ethics. Final set of topics is subject to variation due to class interest. 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. | |
| MATH 470 | Statistics and Data Science Capstone |
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. Formal culmination of statistics program. | |