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)
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.
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
Topics in multiple linear regression, estimation of model parameters, inferences, diagnostics, model assumptions, ANOVA formulation.
Introduction to elementary sampling concepts. Includes random sampling, stratified sampling, cluster sampling and systematic sampling. Inferences and assumptions are presented for all sampling methods.
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 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.