Nanodegree Program
This Programming for Data Science with R Nanodegree program provides an overview of data analysis and programming. In the SQL course, students learn to use SQL to answer complex business problems, including basic commands, joins, and aggregations. In the R course, students learn programming fundamentals and topics such as data types, functions, loops, and conditional statements. They learn to read and manipulate data in R, create basic visualizations, and perform statistical analysis. The version control course teaches students to use Git and GitHub to manage versions of programs and collaborate with others. These courses include practical projects where students use their skills to analyze real data.
This Programming for Data Science with R Nanodegree program provides an overview of data analysis and programming. In the SQL course, students learn to use SQL to answer complex business problems, including basic commands, joins, and aggregations. In the R course, students learn programming fundamentals and topics such as data types, functions, loops, and conditional statements. They learn to read and manipulate data in R, create basic visualizations, and perform statistical analysis. The version control course teaches students to use Git and GitHub to manage versions of programs and collaborate with others. These courses include practical projects where students use their skills to analyze real data.
Beginner
2 months
Real-world Projects
Completion Certificate
Last Updated December 4, 2023
No experience required
Course 1 • 40 minutes
This is an overview of several courses on data analysis and programming. In the SQL course, students learn to use SQL to answer complex business problems, including basic commands, joins, and aggregations. In the Python course, students learn programming fundamentals and use libraries like NumPy and Pandas to work with data. The version control course teaches students to use Git and GitHub to manage versions of programs and collaborate with others. These courses include practical projects where students use their skills to analyze real data.
Course 2 • 4 weeks
Course 3 • 4 weeks
In this course, you’ll learn to represent and store data using R data types and variables, and use conditionals and loops to control the flow of your programs. You’ll harness the power of complex data structures like lists, sets, dictionaries, and tuples to store collections of related data. You’ll define and document your own custom functions, write scripts, and handle errors. You will also learn to use two powerful R libraries - Numpy, a scientific computing package, and Pandas, a data manipulation package.
Course 4 • 2 hours
The Unix shell is a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer.
Staff Data Scientist
Josh has been sharing his passion for data for over a decade. He's used data science for work ranging from cancer research to process automation. He recently has found a passion for solving data science problems within marketplace companies.
Command Line Instructor
Before joining Udacity, Karl was a Site Reliability Engineer (SRE) at Google for eight years, building automation and monitoring to keep the world's busiest web services online.
CEO at Mode
Derek is the CEO of Mode Analytics. He developed an analytical foundation at Facebook and Yammer and is passionate about sharing it with future analysts. He authored SQL School and is a mentor at Insight Data Science.
Instructor
Richard is a Course Developer with a passion for teaching. He has a degree in computer science, and first worked for a nonprofit doing everything from front end web development, to backend programming, to database and server management.
Curriculum Lead at Udacity
Juno is the curriculum lead for the School of Data Science. She has been sharing her passion for data and teaching, building several courses at Udacity. As a data scientist, she built recommendation engines, computer vision and NLP models, and tools to analyze user behavior.
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Programming for Data Science with R
