An introduction to data science with an emphasis on conceptual understanding and interpretation. Surveys various statistical techniques for gaining insights about data. Topics include data visualization, using models to understand data, classification, and other machine learning techniques. Explores ethical considerations in data science. Students will learn to use basic computational tools for data exploration.

Course will help students develop skills necessary for success in introduction to quantitative modeling.

Introductory statistics, including the collection of data, descriptive statistics, probability, and inference. Topics include sampling methods, experiments, numerical and graphical descriptive methods, correlation and regression, contingency tables, probability concepts and distributions, confidence intervals, and hypothesis testing for means and proportions.

Introductory statistics, including the collection of data, descriptive statistics, probability, and inference. Topics include sampling methods, experiments, numerical and graphical descriptive methods, correlation and regression, contingency tables, probability concepts and distributions, confidence intervals, and hypothesis testing for means and proportions.

In-depth introductory statistical methods, focusing on inference, alignment between study design and conclusions, and real-world decision making. Includes parametric and non-parametric approaches to one- and two-sample inference for means and proportions, Type I and II errors, power; chi-squared tests and simple regression. Course will be implemented in R.

Introduction to statistics that includes collection of data using observational studies and experiments; descriptive statistics; parametric and non-parametric approaches to one- and two-sample inference for means and proportions; errors and power; chi-squared tests and simple linear regression. Emphasizes precise communication and implementation using statistical software (R).

Elementary statistical analysis and data manipulation in R. Topics include algorithms, programs, and computing in R. Fundamental techniques of program development in R. Programming projects and applications. Hands-on experience of data input/output and formatting, brief introduction to object-oriented programming, introduction to statistical computing and very elementary data analysis and graphics.

Applied approach to statistical investigation, focusing on real- world decision making in the face of uncertainty. Introduction to central limit theorem and sampling distributions from probabilistic and simulations frameworks for inference, including one- and two-sample inference for means, proportions, simple regression, and categorical data. Consequences of Type I, Type II errors.

Applied approach to statistical investigation, focusing on real- world decision making in the face of uncertainty. Introduction to central limit theorem and sampling distributions from probabilistic and simulations frameworks for inference, including one- and two-sample inference for means, proportions, simple regression, and categorical data. Consequences of Type I, Type II errors.

An understanding of probability and uncertainty in real-world situations. Case studies of the role of uncertainty in the life sciences. Analyzing chance phenomena to identify the underlying probabilistic principles and translating them into probability distributions or simulations. Introduction to random variables, expected values, and variance in applied settings to understand decision making in the face of variability. Introduction to common discrete and continuous random variables and their applications to the life sciences. Extensive use of computer simulations to aid in conceptual understanding.

Univariate analysis for measurement data using graphs and numerical summaries; bivariate analysis for measurement data using scatterplots, correlation, and fitting lines; describing categorical data; sampling methods; observational and experimental studies; describing random behavior; binomial and normal distributions; sampling distributions; confidence intervals and significance testing; pedagogy methods for instruction and integrating technology.

Sampling and statistical studies; basic probability; random variables and their distributions; exploring data using graphical techniques and numerical summaries; exploring relationships between two variables: chi-sq. test of independence; correlation, linear regression; confidence intervals and hypothesis testing for means and proportions. Group projects and activities illustrating concepts will be utilized.