EdX

Data Science: R Basics (edX)

Data Science: R Basics (edX)

Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

We’ll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.
Rather than covering every R skill you might need, you’ll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.
The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.
This course is part of the Data Science Professional Certificate.

What you'll learn

  • Basic R syntax
  • Foundational R programming concepts such as data types, vectors arithmetic, and indexing
  • How to perform operations in R including sorting, data wrangling using dplyr, and making plots
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introduction to Linear Models and Matrix Algebra (edX) EdX
HarvardX,Harvard University

Introduction to Linear Models and Matrix Algebra (edX)

Learn to use R programming to apply linear models to analyze data in life sciences. Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory data analysis course, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language.

Self Paced
Self-Paced
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. 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.

Self Paced
Self-Paced
Data Science Tools (edX) EdX
IBM

Data Science Tools (edX)

Learn about the most popular data science tools, including how to use them and what their features are. In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.

Self Paced
Self-Paced
Introduction to Data Science (edX) EdX
IBM

Introduction to Data Science (edX)

Learn about the world of data science first-hand from real data scientists. The art of uncovering the insights and trends in data has been around for centuries. The ancient Egyptians applied census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year.

Self Paced
Self-Paced
MathTrackX: Probability (edX) EdX
University of Adelaide,AdelaideX

MathTrackX: Probability (edX)

Understand probability and how it manifests in the world around us. This course introduces probability and how it manifests in the world around us. Beginning with discrete random variables, together with their uses in modelling random processes involving chance and variation, you will start to uncover the framework for statistical inference.

Self Paced
Self-Paced
Big Data Capstone Project (edX) EdX
University of Adelaide,AdelaideX

Big Data Capstone Project (edX)

Further develop your knowledge of big data by applying the skills you have learned to a real-world data science project. This project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem. This project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.

Self Paced
Self-Paced
Data Science: Inference and Modeling (edX) EdX
HarvardX,Harvard University

Data Science: Inference and Modeling (edX)

Learn inference and modeling, two of the most widely used statistical tools in data analysis. 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.

Self Paced
Self-Paced
Data Processing and Analysis with Excel (edX) EdX
Rochester Institute of Technology,RITx

Data Processing and Analysis with Excel (edX)

Learn to use Excel to organize and clean data so it can be manipulated and analyzed. In this course, you will learn how to organize your data within the Microsoft Office Excel software tool. Once organized, we will discuss data cleaning. You will learn how to identify outliers and anomalies in the data, and how to identify and change data-types. Together we will develop a data analysis plan, after which we will apply analysis methods and tools, including exploratory analysis, evaluation of results, and comparison with other findings.

Self Paced
Self-Paced