Beginning fall 2021, four SDS course names are changing! See below for more details about each course.
SDS 302 and SDS 306 are now SDS 302F, Foundations of Data Analysis!
SDS 302F Foundations of Data Analysis (replacing SDS 302 Data Analysis for the Health Sciences) Introduction to data analysis and statistical methods. Topics may include: random sampling; principles of observational study and experimental design; data summaries and graphics; and statistical models and inference, including the simple linear regression model and one-way analysis of variance.
Credit for SDS 302F may not be earned after a student has received credit for SDS 320E, 322E (or SDS 328M or 332). Only one of the following may be counted: Statistics and Data Sciences 302, 306, 302F.
Flags: Ethics, Quantitative Reasoning
SDS 328M is now SDS 320E, Elements of Statistics!
SDS 320E Elements of Statistics (replacing SDS 328M Biostatistics) Introduction to statistics. Topics may include: probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced.
Only one of the following may be counted: Statistics and Data Sciences 320E and 328M.
Flags: Ethics, Quantitative Reasoning, Independent Inquiry
SDS 348 is now SDS 322E, Elements of Data Science!
SDS 322E Elements of Data Science (replacing SDS 348 Computational Biology & Bioinformatics) Introduction to data science. Topics may include: data wrangling; exploratory data analysis, including data visualization; markdown and data workflow; simulation-based inference; and classification methods. R programming is emphasized and Python programming is introduced.
Prerequisite: Credit for an introductory statistics course.
Only one of the following may be counted: Statistics and Data Sciences 322E and 348.
SDS 332 is now SDS 324E, Elements of Regression Analysis!
SDS 324E Elements of Regression Analysis (replacing SDS 332 Statistical Models for Health/Behavior Sciences) A follow-up to an introductory statistics course, with an emphasis on the use of regression analysis in applied research. Topics may include: multiple linear regression; ANOVA; logistic regression; random and mixed effects models; and models for dependent data. Emphasis is placed on identification of appropriate statistical methods and interpretation of software output. R programming is introduced.
Prerequisite: SDS 302F or SDS 320E (or SDS 302, 304, 306, 328M)
Only one of the following may be counted: Statistics and Data Sciences 324E or 332.
Flag: Quantitative Reasoning
Even though course names have changed, you can look at the past syllabi for course information!
SDS 302F and SDS 320E: Look at the syllabus for SDS 302 (302F) or SDS 328M (320E), these are identical.
SDS 324E will be similar but not identical to SDS 332. Look at a the SDS 332 syllabus for a general example.
SDS 322E will be similar but not identical to SDS 348. Look at a SDS 348 syllabus for a general example.
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