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.
Prerequisite: Two years of high school algebra with a grade of C or better. 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.
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.
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.
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.
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.