Overview

This Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s “new great complex world” that we now inhabit. This specialization is intended for individuals that work in the general field of data science and analytics. Learners may have job titles related to data science specialist, data science managers, statisticians, and engineers. This specialization would also appeal to market researchers, individuals involved in product development and those that work in the area of online testing. Learners pursuing this course should have excitement for solving problems with data and applying what they learn on a project.

Syllabus

Course 1: Probability, Statistical Inference and Regression Analysis
Course 2: Modern Statistical Computing and Regression Modeling in R
Course 3: Bayesian Statistical Concepts and Methods
Course 4: Classification and Planned Experiments

Courses

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5 hours 30 minutes
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Welcome to Bayesian Statistical Concepts and Methods. In this course, you will use Bayesian methods in data analysis and modeling; work with posterior distributions, distributions without closed form, directed acyclic graphs, Markov Chain Monte Carlo algorithms; and employ R and the Stan platform for statistical modeling. You will also be introduced to Bayesian hierarchical models, which are useful for the interpretation of multi-level data (sub-group versus group).
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4 hours 47 minutes
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Welcome to Classification and Planned Experiments. This course will first contrast regression models with classification models, which have broad application in machine learning. It will then introduce basic classification techniques, focusing on K-nearest neighbor, and logistic regression. You will examine data visualizations and see how setting hyperparameters or estimating parameters supports interpretation and effective classification. The course will then address another powerful field of applied statistics called experimental design, which is concerned with running controlled tests (experiments) to try to understand causal relationships between factors of interest. Several types of designs will be introduced, including ones that use computer modeling. You will learn the principles of experimental design and work through several examples to help you understand how to actually set up, run and analyze these experiments leveraging data.
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11 hours 6 minutes
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Welcome to Modern Statistical Computing and Regression Modeling in R. In this course, you will become familiar with computer applications for working with data, including Excel, R, Tableau, and Jupyter Notebooks; and will learn concepts and applications of Monte Carlo methods and regression analysis. You will learn how R, an interpreted language for analyzing and visualizing data, can be used to accomplish regression analysis, and will have an opportunity to practice with given data sets and code.
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14 hours 41 minutes
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Welcome to Probability, Statistical Inference and Regression Analysis. This course is an introduction to statistical methods and thinking, focusing on modern applications. Some of the concepts will be familiar to those who have taken an elementary statistics course. However, some of the topics presented here extend those ideas into new and emerging applications. These contemporary applications include graphics and data visualization, big data, and newer analytical methods, such as bootstrapping. Acquiring a strong foundation in Regression Analysis is an objective of this course. There is a companion book available that was written by our instructors and would be an excellent companion guide for learners who'd like to further deepen their knowledge of these topics. Proceed to the first module for further details, and to begin learning about Descriptive Statistics.