Applied Statistics Minor
From business to healthcare, marketing to political science, the influence of data and statistics is unavoidable. If you’re the kind of person who gets excited about making data-informed decisions to solve real-world problems, consider a minor in Applied Statistics at Dordt. You’ll learn to use statistical tools and reasoning to help people and organizations make smart choices through research and analysis. And you can apply what you learn to nearly any major you pursue.Request Info
As an applied statistics minor, you’ll develop tools to put what you learn in the classroom into action. At Dordt, our professors invest fully in each student’s success. We offer opportunities to attend conferences, assist in presentations, and grow through social events and clubs. And you’ll learn how your Christian faith can be woven into every aspect of your career.
What You'll Learn
As a statistics minor your coursework will lay a strong foundation for success. Not only will you develop a comprehensive view of statistics, but you’ll learn how to analyze data in your professional work. You'll be plugged into real problems, real data, and experiential courses. And you'll learn how to make data-driven arguments from a Christian perspective.
What You Can Do With An Applied Statistics Minor
Upon graduation, you’ll have the skills and tools you need to apply your statistics training to whichever career path you choose. Health care. Community development. Data science. With the skills you’ll gain from an applied statistics minor, you’ll be ready to make an impact in any field.
A Data Analyst assesses data to determine ways to solve problems within an organization or business.
Statistics Research Assistant
A Statistics Research Assistant provides support to statisticians and mathematicians in analyzing and examining research.
An Account Executive is responsible for overseeing client accounts as well as attempt to secure more revenue for their organization.
To earn a statistics minor, students will need to complete a series of series of statistics courses, as well as a programming or calculus course. Students have a lot of flexibility when it comes to the courses they take for the applied statistics minor, but all students who want this minor will complete an introductory statistics course.
- Introductory Statistics: An introductory course in statistical techniques and methods and their application to a variety of fields. Topics include data analysis, design of experiments, and statistical inference including confidence intervals and hypothesis testing. Exposure to statistical software and a substantive student project are also part of this course.
- Accelerated Introductory Statistics: This course covers the same content and learning objectives as Statistics 131 but in half the time. This course, along with Statistics 202 and Statistics 203, also serves as preparation for Actuarial Exam SRM. Additionally this course, along with Statistics 202, Statistics 203, Statistics 220 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Offered first half of spring semester.
- Applied Statistical Models: This course surveys multivariable design and statistical methods used across various disciplines and seen in peer-reviewed research. Topics include multiple and non-linear regression, general linear models, multivariable statistical models, and multifactor experimental design emphasis is on active-learning using group activities and projects, critiquing research, and statistical software. Offered second half of spring semester.
- Econometrics: This course covers all of the topics in Statistics 201 and topics commonly used in economic applications of statistics: time series and forecasting, linear time series models, moving average, autoregressive and ARIMA models, data analysis and forecasting with time series models and forecasting errors. Meets at the same times as Statistics 201 plus two additional hours per week. This course, along with Statistics 132 and Statistics 203, also serves as preparation for Actuarial Exam SRM. Additionally this course, along with Statistics 132, Statistics 203, Statistics 220 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Offered second half of spring semester.
- Data Analysis Internship: A semester-long research experience that involves a significant use of multivariable statistics in an applied research project. Students will identify and work with a primary faculty mentor to develop a project proposal prior to enrolling; students will also be supervised by a statistics professor. Part of the course will include an oral and written presentation of results. The course will be offered as needed and is run as an individual study. May be repeated for up to 12 credits.
- Programming I: An introduction to computer programming. Basic notions of abstraction, elementary composition principles, the fundamental data structures, and object-oriented programming technique are introduced. Topics include variables, control structures, arrays, and input/output.
- Calculus I: A study of the basic concepts and techniques of calculus for students in all disciplines. Topics include limits, differentiation, integration, and applications. This course is intended for students without any previous calculus credit.
- Generalized Linear Models: This course covers simple linear regression and associated special topics, multiple linear regression, indicator variables, influence diagnostics, assumption analysis, selection of ‘best subset’, nonstandard regression models, logistic regression, and nonlinear regression models. This course, along with Statistics 132 and Statistics 202, also serves as preparation for Actuarial Exam SRM. Additionally this course, along with Statistics 132, Statistics 202, Statistics 220 and Statistics 352, serves as preparation for Actuarial Exam MAS I.
- Experimental Design: Principles, construction and analysis of experimental designs. Completely randomized, randomized complete block, Latin squares, Graeco Latin squares, factorial, and nested designs. Fixed and random effects, expected mean squares, multiple comparisons, and analysis of covariance.
- Complex Data and Hierarchical Models: A course which illustrates statistical modelling techniques for the class of datasets which have correlation between the observations including time series, hierarchical samples, complex survey samples, clusters, family structures, etc. The general linear model is expanded to the general estimating equations approach.
- Statistical Programming in R: Odd Data acquisition, cleaning, and management in R; use of regular expressions; functional and object-oriented programming; graphical, descriptive, and inferential statistical methods; random number generation; Monte Carlo methods including resampling, randomization, and simulation.
- Machine Learning/Modern Data Analysis Methods: An introductory survey of modern machine learning. Machine learning is an active and growing field that would require many courses to cover completely. This course aims at the middle of the theoretical versus practical spectrum. We will learn the concepts behind several machine learning algorithms without going deeply into the mathematics and gain practical experience applying them. We will consider both pattern recognition and artificial intelligence perspectives.
- Introduction to Univariate Probability: An introduction to the theory and techniques of general probability and common univariate probability distributions. Topics include but are not limited to basic set theory, introductory probability rules (independence, combinatorials, conditionals, Bayes theorem, etc.), common univariate distributions (e.g., binomial and normal) and expected value/variance. This course, along with Statistics 216, also serves as preparation for Actuarial Exam P/1. Offered first half of the semester.
- Introduction to Multivariate Probability: An introduction to multivariate probability distributions. Topics include but are not limited to joint probability density functions, conditional and marginal probability distributions, moment generating functions, covariance and correlations, transformations and linear combinations of independent random variables. This course, along with Statistics 215, also serves as preparation for Actuarial Exam P/1. Offered second half of the semester.
- Mathematical Statistics: The theory of hypothesis testing and its applications. Power and uniformly most powerful tests. Categorical data and nonparametric methods. Bayesian vs. Frequentist methods. Other selected topics. This course, along with Statistics 132, Statistics 202, Statistics 203 and Statistics 352, serves as preparation for Actuarial Exam MAS I. Additionally this course, along with Statistics 290 and Statistics 353, serves as preparation for Actuarial Exam MAS II.
- Research Methods: This course introduces students to the research process, including formulation of hypotheses, design, interpretation, and communication of results. The course will include a review of statistical procedures with an emphasis on selection and interpretation of analyses and an introduction to computer data analysis with R. Methods of research are discussed from a reformed, Christian perspective. Students complete research proposals.
Methods of Social Science Research: An introduction to the research process as applied to the study of problems/issues in social science. Problem selection, research design, measurement, methods of observation and data collection, data analysis and interpretation, and report writing will be emphasized. A module on microcomputer utilization and the application of descriptive statistics is presented for application in student projects.
- Introduction to Data Science: Introduction to the field of data science and the workflow of a data scientist. Types of data (tabular, textual, sparse, structured, temporal, geospatial), basic data management and manipulation, simple summaries, and visualization. This course also serves as preparation for Actuarial Exam PA. Additionally this course, along with Statistics 220 and Statistics 353, serves as preparation for Actuarial Exam MAS II.
- Data Analysis Internship: Summer 374 A semester-long research experience that involves a significant use of multivariable statistics in an applied research project. Students will identify and work with a primary faculty mentor to develop a project proposal prior to enrolling; students will also be supervised by a statistics professor. Part of the course will include an oral and written presentation of results. The course will be offered as needed and is run as an individual study. May be repeated for up to 12 credits.
- Business and Technical Writing: Students will study the process, application, and characteristics of business and technical writing, and the way in which writing style, strategies, content, and clarity will relate practically to one’s profession. Concentrates on developing competence in a variety of writing tasks commonly performed in business, law, industry, social work, engineering, agriculture, and medicine. Satisfies Core Program writing-intensive requirement.
Ready to take the next step?
With all the resources and relationships that Dordt had to provide, Caleb was able to grow in his faith, sharpen his academic skills, harness his athletic ability, and prepare for everything that lies ahead of him.
Caleb PollemaRead More
Still looking for the right fit? Here are some additional program options that we think might interest you or are often paired with this program. You can also view the programs page to keep exploring your options.