Statistics & Data Analysis

This course covers advanced topics in R programming that are necessary for developing powerful, robust, and reusable data science tools. Topics covered include functional programming in R, robust error handling, object oriented programming, profiling and benchmarking, debugging, and proper design of functions. [...]

This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you [...]

Learner Outcomes: After taking this course, you will be able to: utilize various Application Programming Interface (API) services to collect data from different social media sources such as YouTube, Twitter, and Flickr; process the collected data - primarily structured - using methods involving correlation, regression, and classification to derive [...]

Welcome to Data-driven Decision Making. In this course, you'll get an introduction to Data Analytics and its role in business decisions. You'll learn why data is important and how it has evolved. You'll be introduced to “Big Data” and how it is used. You'll also be introduced to a [...]

At the end of the course, you will be able to: Retrieve data from example database and big data management systems; Describe the connections between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications; Identify when a big data problem needs [...]

This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values [...]

In this first course of the specialization, you will discover just what data visualization is, and how we can use it to better see and understand data. Using Tableau, we’ll examine the fundamental concepts of data visualization and explore the Tableau interface, identifying and applying the various tools Tableau [...]

This course describes Bayesian statistics, in which one's inferences about parameters or hypotheses are updated as evidence accumulates. You will learn to use Bayes’ rule to transform prior probabilities into posterior probabilities, and be introduced to the underlying theory and perspective of the Bayesian paradigm. [...]

Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: a basic understanding of linear algebra and multivariate calculus; a [...]

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct [...]