When you think about what data analysts and data scientists do on a day-to-day basis, you might have a general understanding of types of conclusions they make, but how do they arrive at those conclusions? The statistical programming language R is widely used in data science; understanding the basics of how it works can help you manipulate and visualize data in a quick, flexible manner, and it may improve your communication with data scientists on your team.

In this course, you will explore the basics of statistical programming and develop R skills. As you hone your ability to use commands in R, you will combine those basic skills to complete more complex tasks, such as data manipulation and visualization. Finally, you will examine how to repeat tasks in R, which makes it easier to manipulate large data sets. This course involves many hands-on coding exercises to help you gain confidence in your newfound programming skills.

System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.

The real world is extremely complex, and revealing the patterns that underlie these complexities can be challenging. However, unlocking the power of a data set can provide you with remarkable insights and help guide decision-making. This course will prepare you to use summarization and visualization techniques to reveal patterns in real-world data, using examples from a variety of disciplines, including business and medicine.

In this course, Professor Basu will guide you as you begin to understand key data collection principles and how to make conclusions from data. Choosing which analyses to use depends on your question, so you will use a framework to help you choose which methods to use with your data. Then, you will use R to perform exploratory data analyses, which will allow you to identify key patterns and trends in a ready-to-analyze data set. You will also learn the importance of quantifying the uncertainty associated with your results, and how to measure variability in your data. This course involves many hands-on coding exercises in R to help you gain confidence in your programming skills.

System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.

“Exploring Data Sets With R” must be completed prior to starting this course.

Data scientists make decisions by inferring the characteristics of a large population based on the characteristics of samples from that population. Basing a decision on samples is necessary since it would not be possible to measure every individual or unit in a population. However, it also means that data scientists need to consider the potential variability among samples before using those samples to make conclusions about the population. The variability across samples leads to uncertainty in decision-making, and understanding and quantifying that uncertainty is a key aspect of data science.

Throughout this course, Professor Basu will guide you through the nuances of understanding and quantifying the uncertainty around your results, and through making decisions in the face of that uncertainty. In data science, simulations offer a powerful framework with which to understand the uncertainty around your data, so you will learn to perform simulations in R and use a simulation-based framework to quantify uncertainty when studying the relationship between categorical variables. You will also use resampling techniques to understand numerical variables and compare their summary statistics across different levels of a categorical variable. Often, data scientists search for relationships between numerical variables and use one numerical variable to predict another numerical variable, and you will do this by building a prediction rule with linear regression while keeping the uncertainty of your results in mind. Finally, you will use the errors from linear regression to compare prediction rules and determine which prediction rules fit your data best. This course involves many hands-on coding exercises in R which will help you gain confidence in your programming skills.

System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.

“Exploring Data Sets With R” and “Summarizing and Visualizing Data” must be completed prior to starting this course.

Data scientists use data collected from the real world to answer questions and solve problems that would otherwise be intractable. But since the world is complex, data collected to describe the world can also be complex, which makes it messy and difficult to work with. To successfully analyze data, data scientists need to spend time cleaning — or organizing and manipulating — their data to put it into a form that is easier to work with and understand.

In this course, you will delve into the world of data cleaning by presenting and manipulating your data with the Tidyverse in R. You will organize data by selecting only the variables you're interested in, creating new groups of data, and summarizing data in a way that makes sense for the questions you're trying to ask. You will also create high-quality plots to quickly summarize complex data. You will become familiar with the concept of tidy data and organize data sets in a way that allows for the most efficient analysis. Finally, you will work with data types of more complexity so that you can answer increasingly difficult questions as you take your new skills into your workplace. You will practice all these skills by working with four real-world, complex data sets. This course involves many hands-on coding exercises that will help you take your programming skills to the next level.

System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.

“Exploring Data Sets With R” and “Measuring Relationships and Uncertainty” must be completed prior to starting this course.

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