Statistics for Data Science Applications Specialization

Colleagues, the Statistical Modeling for Data Science Applications Specialization will equip you to build your Statistical Skills for Data Science. Master the Statistics Necessary for Data Science. Learn to Correctly analyze and apply tools of regression analysis to model relationship between variables and make predictions given a set of input variables., Successfully conduct experiments based on best practices in experimental design.and Use advanced statistical modeling techniques, such as generalized linear and additive models, to model a wide range of real-world relationships. Gain high demand skills in Linear Models, R Programming, Statistical Models, Regresion, Calculus and probability theory, and Linear Algebra.. The three courses in this Specialization include: 1) Modern Regression Analysis in R - provides a set of foundational statistical modeling tools for data science. In particular, students will be introduced to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse., 2) ANOVA and Experimental Design - This second course in statistical modeling will introduce students to the study of the analysis of variance (ANOVA) - provides the mathematical basis for designing experiments for data science applications. Emphasis will be placed on important design-related concepts, such as randomization, blocking, factorial design, and causality. Some attention will also be given to ethical issues raised in experimentation, and 3) Generalized Linear Models and Nonparametric Regression - study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.

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Much career success, Lawrence E. Wilson - Online Learning Central