Minor in Data Science

This course is a hands-on, beginner-friendly course designed for students eager to dive into the world of data science. No prior experience with programming or data science is required. By the end of the semester, students will have the foundational skills to analyze real-world data using the R programming language. This course introduces students to the application of data science across various fields—such as healthcare, finance, social media, and sports—through engaging, real-world examples. Students will develop the skills necessary to extract insights and make informed decisions using data. The course starts with an overview of data science and its workflow before delving into topics like data visualization, data transformation, and result interpretation. Students will also learn to work with different data types and how to manipulate and clean data to simplify analysis.

This is an introductory course in statistics. It covers basic concepts of statistics, including simple graphical displays and numerical summaries of categorical and quantitative data, measures of center and spread, z-scores, the Normal distribution, and correlation. Applications to different fields are included throughout. Additional topics in statistics may be included.

This course provides students with the necessary skills to study calculus and various other mathematics, science, and computer related courses. Students will learn the properties of various types of functions, graph them, and solve equations involving these functions. Topics covered include: polynomial, rational, exponential, and logarithmic functions, trigonometric functions and identities, and sequences and series. Applications are included throughout. Passing both MAT 125 College Algebra and MAT 126 Trigonometry is equivalent to passing MAT 130.

This course builds on the foundation established in DAS 105, focusing on advanced techniques for data wrangling, visualization, and analysis, along with an introduction to regression analysis. Students will work with real-world data through case studies and hands-on labs, gaining practical experience in cleaning, structuring, and visualizing data. Additionally, students will explore the theory and application of various regression techniques, including simple linear regression, multiple linear regression, and logistic regression. The course provides essential experience in using R as a powerful tool for data science.

This course introduces fundamental concepts of computer programming. Students learn program logic, flow charting, and problem solving through analysis, development, basic debugging and testing procedures. Topics include variables, expressions, data types, functions, decisions, loops, and arrays. Students will use the knowledge and skills gained throughout this course to develop a variety of simple programs.

This course introduces students to the world of computer simulations of natural systems (from biology, chemistry, or physics) with and emphasis on data collection, analysis, visualization and interpretation. This is done through independent research where students work in groups to design and pursue computational modeling projects accompanied by a technical group report and presentation.

This course introduces students to probability and simulation. Students will learn how probability and simulation are applied in real-world decision-making scenarios. Topics covered include probability distributions (normal, binomial, Bernoulli, and Poisson), Markov chains and random walks, bootstrapping, and randomization tests. Applications will be emphasized throughout. A programming language will be used.

This course covers the fundamentals of machine learning for data scientists. Students will learn about training data, and how to use a set of data to discover potentially predictive relationships. Topics covered include supervised and unsupervised machine learning, generalized linear models including multivariate linear regression and binary logistic regression, automatic feature selection, bootstrapping, simple reinforcement learning, and decision trees. Applications will be emphasized throughout. A programming language will be used.

Ever wondered how companies like Amazon know more about you? Ever wondered how weather data is represented in the news? Using interdisciplinary concepts, we will learn how to tackle big data. Complex data sets are being generated continuously. Many questions arise as to what these data are telling us. Are we missing something? How do we look for signals in these large datasets? Using computer programs like Excel and R programming we will learn how to manage, sort and represent these data. Students will be encouraged to identify a data set related to a real world problem and use the tools learned in class to tell their stories.

Modern day biology has generated massive amounts of data but very few experts to analyze this data. A course in Genomics and Bioinformatics will teach students how to use computer algorithms to analyze the data. Students learn applications of genomics to biomedical and biological research by performing computational exercises using databases. Topics include genome sequencing gene prediction, genetic variation, sequence database searching, multiple sequence alignment, evolutionary tree construction, protein structure prediction, proteomic analysis, interaction networks and use of genome browsers among other topics.

This is an applied statistics course designed for undergraduate data science students. Topics include exploratory data analysis for categorical and numerical data with data summaries and visualization; simple, multiple, and nonlinear regression modeling; hypothesis testing and confidence intervals for means and proportions. Students will apply these statistical tools to real-world data using R and will develop skills in interpreting and communicating data analysis results.

Advanced level course on special topics, current issues, or areas of interest not included in other courses offered by the (department/program). May be repeated with different content areas.

The course introduces students to ArcGIS Online, an online Geographic Information System (GIS) application from Esri. With GIS, the student can explore, visualize, and analyze data; create 2D maps and 3D scenes with several layers of data to visualize multiple data sets at once; and share work to an online portal. GIS analytics tools are used in many disciplines and fields of practice including public health, history, sociology, political science, business, biology, international development, and information technology. In the end of the course, students will have the opportunity to take additional training on GIS applications in their specific field of interest.

The course introduces students to ArcGIS Pro mapping software, a stand-alone Geographic Information System (GIS) application from ESRI. With GIS, the student can explore, visualize, and analyze data; create 2D maps and 3D scenes with several layers of data to visualize multiple data sets at once; and share work to an online portal. GIS analytics tools are used in many disciplines and fields of practice including public health, history, sociology, political science, business, biology, international development, and information technology. In the end of the course, students will have the opportunity to take additional training on GIS applications in their field of interest.  DAS 532 is recommended but not required.

This is an introductory course in statistics. It covers basic concepts of statistics, including simple graphical displays and numerical summaries of categorical and quantitative data, measures of center and spread, z-scores, the Normal distribution, and correlation. Applications to different fields are included throughout. Additional topics in statistics may be included.

This is an introductory course in probability and statistics for science and information technology students. It covers basic concepts of probability and statistics, frequency distributions, graphical methods, measures of central tendency and variability, counting principles, Bayes' theorem, discrete and normal probability distributions, linear regression models, correlation, central limit theorem, sampling variability, confidence intervals, and hypothesis testing. Applications to different fields are included throughout.

This course will introduce the concepts, theories, and applications of biostatistics to biological, medical, and public health research. It will cover descriptive statistics, concepts of probabilities and distributions, graphical methods, comparisons of two variables, central limit theorem, sampling variability, confidence intervals, and hypothesis testing.

This course covers an introduction to statistical procedures for psychological research. Topics include distributions and graphs, measures of central tendency and variation, z-scores, probability, hypothesis testing, t-tests, Anova, correlation and regression, and Chi square. Students are introduced to the use of SPSS (or a similar program) for analysis and interpretation of data.

An introduction to descriptive statistics and methods of organizing, presenting, and interpreting data. Covers measures of central tendency, measures of association for two variables, and some multivariate analyses. Includes computer analysis of real data.