Please note: All students are required to complete a web-based placement assessment called ALEKS to determine readiness for entry-level mathematics and statistics courses unless they meet certain exceptions. These exceptions are based on various factors such as SAT or ACT scores or previously earned mathematics credit in college-level courses or developmental education courses at a community college. Please see the Exceptions page at the UNC Math Placement website for complete details. Entry-level mathematics and statistics courses that require the ALEKS assessment include MATH 120, MATH 124, MATH 125, MATH 127, MATH 131, MATH 171, and STAT 150. More information on ALEKS and a link to the assessment can be found at www.unco.edu/nhs/mathematical-sciences/placement/.
Introduction to elements of Data Science and elementary tools, programming languages, and techniques for data collection, visualization, computations, and inference. Includes ethical issues with data collection and analysis.
(ALEKS Test Score with a minimum score of 025 or Completion of LC2-Mathematics course or Completion of LAX1-Mathematics course)
In this supplementary course we will develop critical thinking, ethical decision-making, and effective communication. The course will provide supplemental academic support for students enrolled in Introduction to Statistical Analysis (STAT 150). This will include content review, study skills, and effective strategies for success in STAT 150. S/U graded.
Study techniques used in organizing data, including frequency distributions, histograms, measures of central tendency, measures of dispersion, probability distributions, point estimation, interval estimation and testing hypotheses.
(ALEKS Test Score with a minimum score of 030 or Concurrent Prerequisite
STAT 149 with a minimum grade of S)
Provides tools to prepare data, critique and improve visualizations of statistical data, learn visual encoding principles of quantitative information, and learn how these principles are applied to create effective visualizations.
Introduction of the tools, standards and practices for collecting, organizing, managing, exploring, and using data. Develop familiarity with computational tools and the preparation, analysis, and visualization of data and creating analysis tools for larger data sets.
This course is an introduction to statistical methods in biological sciences. Topics include study designs, data visualization and exploration, basic probability with applications, and statistical inference for comparing multiple groups.
This course provides direction on how to prepare real-world datasets for statistical analysis, as well as perform data cleansing, reformatting, and data wrangling. Students will perform exploratory data analysis using real-world datasets and develop data visualization to facilitate statistical inquiry.
This course addresses planning and organization of experiments, including ethical considerations. One-factor experiments, randomized blocks, Latin squares and related designs, factorial designs and fractional factorial designs, response surface methodology, nested and split-plot designs.
This course introduces basic regression techniques, focusing on the theoretical foundations of regression analysis and its application to real data sets. Emphasis is placed on specifying and interpreting regression models.
Introduces conceptions of statistics, data analysis, and concepts of probability. Focus is on understanding variability and probability, sampling and random variables, descriptive and inferential statistics.
Concurrent Prerequisite
MATH 132 with a minimum grade of C
This course is an introduction to the elements of data science. Topics include data visualization, data wrangling, statistical learning and predictive analytics, text mining and spatial data.
Individualized investigation under the direct supervision of a faculty member. (Minimum of 37.5 clock hours required per credit hour.)
Special Notes
Maximum concurrent enrollment is two times.
This course introduces three classes of multivariate statistical methods: factor analysis, multidimensional scaling (MDS), and cluster analysis emphasizing exploratory applications and learning about the structure of a data matrix.
A course on the application and practice of statistics in Natural and Health Sciences. Courses covers using modern computational tools: inferential statistics, multivariate regression, experimental design, ANOVA and non-parametric statistics.
This is a project course in data science and related fields. Interdisciplinary teams will analyze a new data science problem, develop a model, and control for error and overfitting.
Students gain experience applying a broad range of statistical techniques to real-world data, interpreting findings, and communicating results through the completion of a project.
Explores advanced topics in statistics not included in existing courses, reflecting specific interests of students and instructors.
A substantial independent project in the area of statistics, carried out by the student with the guidance of a faculty mentor.
Special Notes
Consent of instructor required.