Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics. The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations. With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern...

# Course

Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics. The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations. With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern...

Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics. The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations. With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern...

This course consists of an assessment that tests your knowledge on the course content from 14.310x - Data Analysis for Social Scientists, a statistics and data analysis course that will introduce you to the essential notions of probability and statistics. It will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. It will illustrate these concepts...

The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning. In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks....

Improvements in modern biology have led to a rapid increase in sensitivity and measurability in experiments and have reached the point where it is often impossible for a scientist alone to sort through the large volume of data that is collected from just one experiment. For example, individual data points collected from one gene expression study can easily number in the hundreds of thousands. These types of data sets are often referred to as ‘biological big data’ and require bioinformaticians to...

Statistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting. This course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors a...