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Moira Burke,
Chris Saden,
Solomon Messing,
and
Dean Eckles
Data is everywhere. Take Udacity's Data Analysis with R course and learn how to investigate and summarize data sets using R and eventually create your own analysis.
Register for this course and see more details by visiting:
OpenCourser.com/course/oudukb/udacity
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UDACITY40
What's inside
Syllabus
Learn about what exploratory data analysis (EDA) is and why it is important.
Install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.
Read more
Syllabus
Learn about what exploratory data analysis (EDA) is and why it is important.
Install RStudio and packages, learn the layout and basic commands of R, practice writing basic R scripts, and inspect data sets.
Read more
Learn how to quantify and visualize individual variables within a data set using histograms, boxplots, and transforms.
Practice using R functions and univariate visualizations to explore and understand individual variables.
Learn techniques for exploring the relationship between any two variables in a data set, including scatter plots, line plots, and correlations.
Practice using bivariate exploration techniques to explore relationships between variables.
Learn powerful methods for examining relationships among multiple variables, and find out how to reshape your data.
Practice using multivariate exploration techniques to look at more complicated relationships between multiple variables.
Put it all together in this case study where we investigate the diamonds data set alongside Facebook Data Scientist, Solomon Messing.
Final Project
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Moira Burke, who is an award-winning data scientist and researcher in the fields of data analysis, machine learning, and artificial intelligence
Taught by Chris Saden, who is an experienced data scientist with a PhD in Statistics
Taught by Solomon Messing, who is a seasoned data scientist at Facebook
Taught by Dean Eckles, who has extensive experience in designing and teaching data analysis courses
Introduces learners to exploratory data analysis (EDA), a fundamental skill for data scientists and analysts
Guides learners in summarizing data sets using R, a widely used programming language for data analysis
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Save Data Analysis with R to your list so you can find it easily later:
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Reviews summary
Acquire data analysis skills with r
The majority of learners give this course a positive review. Most learners found the course to be beginner-friendly or accessible to learners new to R and Exploratory Data Analysis. This course primarily covers how to visualize data in R with the ggplot2 package. There is little coverage of high-level concepts and numeric methods like creating tables or summaries. The course provides a solid overview of using R for data analysis.
Accessible to learners new to R and EDA
"If you are looking for a good start on the topic of data visualization in R. This is the best choice in the web."
Heavy focus on GGPlot2 package for visualizations
"The course provides an overview of using R to explore data and focuses heavily on the use of the ggplot2 package in R to create data visualizations."
Limited coverage of advanced statistical concepts
"This is not necessarily a bad thing--learning to plot in R is a valuable skill and an important part of exploratory data analysis--but it seems like the course should have spent a bit more time covering high-level concepts and numeric methods for exploring data like using tables and summaries."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Analysis with R with these
activities:
Review basic statistics concepts
Show steps
Refresh foundational knowledge of statistics prior to taking the course.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks on basic statistics.
-
Take practice quizzes or solve practice problems.
Organize and review course materials
Show steps
Create a comprehensive review resource by compiling and organizing course materials.
Show steps
-
Collect class notes, slides, assignments, and readings.
-
Organize materials in a logical order.
-
Review materials regularly.
Join a study group or discussion forum
Show steps
Collaborate with peers to discuss course concepts, share knowledge, and provide support.
Show steps
-
Find or create a study group with other students taking the course.
-
Participate in regular discussions and ask questions.
-
Share knowledge and perspectives with group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice R coding exercises
Show steps
Practice writing R code and working with data to reinforce the concepts learned in the course.
Show steps
-
Find online R coding exercises or create your own.
-
Solve the coding exercises using R.
-
Review your solutions and identify areas for improvement.
Follow online R tutorials and workshops
Show steps
Supplement the course material with guided tutorials and workshops to deepen understanding of R.
Show steps
-
Search for R tutorials that cover specific topics of interest.
-
Follow the tutorials step-by-step, practicing the code examples.
-
Attend online R workshops to interact with experts and ask questions.
Create data visualizations in R
Show steps
Apply the data visualization techniques learned in the course to create meaningful insights from data.
Show steps
-
Gather a dataset of interest.
-
Explore the data and identify patterns or trends.
-
Create data visualizations using libraries like ggplot2.
-
Analyze the visualizations and derive insights.
Participate in R coding competitions
Show steps
Challenge oneself to solve complex R coding problems in a competitive environment.
Show steps
-
Find online R coding competitions.
-
Register for a competition and study the problem statement.
-
Develop and submit your R code solution.
Build a data analysis project using R
Show steps
Apply the skills learned in the course to a real-world data analysis project.
Show steps
-
Identify a real-world problem or dataset.
-
Clean and prepare the data using R.
-
Analyze the data and identify patterns or trends.
-
Develop a data analysis report or presentation.
Review basic statistics concepts
Show steps
Refresh foundational knowledge of statistics prior to taking the course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks on basic statistics.
- Take practice quizzes or solve practice problems.
Organize and review course materials
Show steps
Create a comprehensive review resource by compiling and organizing course materials.
Show steps
Show steps
Show steps
- Collect class notes, slides, assignments, and readings.
- Organize materials in a logical order.
- Review materials regularly.
Join a study group or discussion forum
Show steps
Collaborate with peers to discuss course concepts, share knowledge, and provide support.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course.
- Participate in regular discussions and ask questions.
- Share knowledge and perspectives with group members.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice R coding exercises
Show steps
Practice writing R code and working with data to reinforce the concepts learned in the course.
Show steps
Show steps
Show steps
- Find online R coding exercises or create your own.
- Solve the coding exercises using R.
- Review your solutions and identify areas for improvement.
Follow online R tutorials and workshops
Show steps
Supplement the course material with guided tutorials and workshops to deepen understanding of R.
Show steps
Show steps
Show steps
- Search for R tutorials that cover specific topics of interest.
- Follow the tutorials step-by-step, practicing the code examples.
- Attend online R workshops to interact with experts and ask questions.
Create data visualizations in R
Show steps
Apply the data visualization techniques learned in the course to create meaningful insights from data.
Show steps
Show steps
Show steps
- Gather a dataset of interest.
- Explore the data and identify patterns or trends.
- Create data visualizations using libraries like ggplot2.
- Analyze the visualizations and derive insights.
Participate in R coding competitions
Show steps
Challenge oneself to solve complex R coding problems in a competitive environment.
Show steps
Show steps
Show steps
- Find online R coding competitions.
- Register for a competition and study the problem statement.
- Develop and submit your R code solution.
Build a data analysis project using R
Show steps
Apply the skills learned in the course to a real-world data analysis project.
Show steps
Show steps
Show steps
- Identify a real-world problem or dataset.
- Clean and prepare the data using R.
- Analyze the data and identify patterns or trends.
- Develop a data analysis report or presentation.
Career center
Learners who complete Data Analysis with R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use R and other tools to analyze, interpret, and present data to help businesses make informed decisions. Data Analysis with R can help aspiring Data Analysts build a foundation in using R, which is a popular tool in the field of data analysis. The course covers topics such as exploratory data analysis, data visualization, and statistical modeling. These skills are essential for Data Analysts who want to succeed in their roles.
Statistician
Statistician
Statisticians use their knowledge of probability and statistics to solve problems and make predictions. Data Analysis with R can help aspiring Statisticians develop their skills in data analysis and visualization, which are essential for success in the field. The course covers topics such as probability, statistical inference, and regression analysis. These concepts are fundamental to the work of Statisticians.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, computer science, and business to solve problems and make predictions. Data Analysis with R can help aspiring Data Scientists develop their skills in data analysis, visualization, and modeling. The course covers topics such as machine learning, deep learning, and natural language processing. These skills are in high demand for Data Scientists who want to succeed in their roles.
Quantitative Researcher
Quantitative Researcher
Quantitative Researchers use data analysis to make investment decisions. Data Analysis with R can help aspiring Quantitative Researchers develop the skills they need to succeed in this field. The course covers topics such as financial data analysis, risk management, and portfolio optimization. These skills are essential for Quantitative Researchers who want to be successful.
Actuary
Actuary
Actuaries use their knowledge of mathematics and statistics to assess risk and make financial decisions. Data Analysis with R can help aspiring Actuaries develop their skills in data analysis and modeling. The course covers topics such as survival analysis, risk management, and financial modeling. These skills are essential for Actuaries who want to be successful.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data analysis to improve the efficiency of operations. Data Analysis with R can help aspiring Operations Research Analysts develop the skills they need to succeed in this field. The course covers topics such as simulation, optimization, and decision analysis. These skills are essential for Operations Research Analysts who want to be successful.
Market Researcher
Market Researcher
Market Researchers use data analysis to understand consumer behavior and make marketing decisions. Data Analysis with R can help aspiring Market Researchers develop the skills they need to succeed in this field. The course covers topics such as survey design, data analysis, and market segmentation. These skills are essential for Market Researchers who want to be successful.
Business Analyst
Business Analyst
Business Analysts use data analysis to solve business problems. Data Analysis with R can help aspiring Business Analysts develop the skills they need to succeed in this field. The course covers topics such as data analysis, modeling, and communication. These skills are essential for Business Analysts who want to be successful.
Financial Analyst
Financial Analyst
Financial Analysts use data analysis to make investment decisions. Data Analysis with R can help aspiring Financial Analysts develop the skills they need to succeed in this field. The course covers topics such as financial data analysis, risk management, and portfolio optimization. These skills are essential for Financial Analysts who want to be successful.
Risk Analyst
Risk Analyst
Risk Analysts use data analysis to assess risk and make decisions. Data Analysis with R can help aspiring Risk Analysts develop the skills they need to succeed in this field. The course covers topics such as risk management, data analysis, and modeling. These skills are essential for Risk Analysts who want to be successful.
Data Engineer
Data Engineer
Data Engineers use data analysis to design and build data infrastructure. Data Analysis with R can help aspiring Data Engineers develop the skills they need to succeed in this field. The course covers topics such as data warehousing, data integration, and data security. These skills are essential for Data Engineers who want to be successful.
Software Engineer
Software Engineer
Software Engineers use data analysis to design and build software. Data Analysis with R can help aspiring Software Engineers develop the skills they need to succeed in this field. The course covers topics such as software development, data analysis, and machine learning. These skills are essential for Software Engineers who want to be successful.
Database Administrator
Database Administrator
Database Administrators use data analysis to manage and maintain databases. Data Analysis with R can help aspiring Database Administrators develop the skills they need to succeed in this field. The course covers topics such as database design, data analysis, and data security. These skills are essential for Database Administrators who want to be successful.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data analysis to create visualizations that help people understand data. Data Analysis with R can help aspiring Data Visualization Specialists develop the skills they need to succeed in this field. The course covers topics such as data visualization, data analysis, and communication. These skills are essential for Data Visualization Specialists who want to be successful.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use data analysis to design and build machine learning models. Data Analysis with R can help aspiring Machine Learning Engineers develop the skills they need to succeed in this field. The course covers topics such as machine learning, data analysis, and model deployment. These skills are essential for Machine Learning Engineers who want to be successful.
For more career information including salaries, visit:
OpenCourser.com/course/oudukb/udacity
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Analysis with R.
Save
Provides a practical introduction to data science using R. It covers topics such as data wrangling, statistical modeling, and data visualization. It valuable resource for students and practitioners who want to learn how to use R for data science.
Provides a comprehensive overview of exploratory data analysis using R. It covers topics such as data visualization, statistical modeling, and data mining. It valuable resource for students and practitioners who want to learn how to use R for exploratory data analysis.
Provides a comprehensive overview of R programming for data science. It covers topics such as data wrangling, data visualization, and statistical modeling. It valuable resource for students and practitioners who want to learn how to use R for data science.
Save
Provides a comprehensive overview of data analysis using R, covering topics such as data exploration, statistical modeling, and data visualization. It valuable resource for students and practitioners who want to learn how to use R for data analysis.
Provides a practical introduction to machine learning using R. It covers topics such as supervised learning, unsupervised learning, and model evaluation. It valuable resource for students and practitioners who want to learn how to use R for machine learning.
Provides a practical introduction to deep learning using R. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for students and practitioners who want to learn how to use R for deep learning.
Save
Provides a practical introduction to text mining using R. It covers topics such as text preprocessing, text analysis, and text classification. It valuable resource for students and practitioners who want to learn how to use R for text mining.
Provides a comprehensive overview of the ggplot2 package for data visualization in R. It covers topics such as creating basic plots, customizing plots, and creating interactive visualizations. It valuable resource for students and practitioners who want to learn how to create visualizations in R.
Save
Provides an in-depth look at advanced topics in R programming. It covers topics such as object-oriented programming, statistical modeling, and data visualization. It valuable resource for students and practitioners who want to learn how to use R for advanced data analysis.
Save
Provides a collection of recipes for creating visualizations in R. It covers topics such as creating basic plots, customizing plots, and creating interactive visualizations. It valuable resource for students and practitioners who want to learn how to create visualizations in R.
Save
Provides a comprehensive overview of data mining using R. It covers topics such as data preprocessing, feature selection, and model evaluation. It valuable resource for students and practitioners who want to learn how to use R for data mining.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oudukb/udacity
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Effort
23 hours
Via
Udacity
Instructors
Moira Burke
Chris Saden
Solomon Messing
Dean Eckles
Language
English
Good to know
Taught by Moira Burke, who is an award-winning data scientist and researcher in the fields of data analysis, machine learning, and artificial intelligence
Taught by Chris Saden, who is an experienced data scientist with a PhD in Statistics
Taught by Solomon Messing, who is a seasoned data scientist at Facebook
Taught by Dean Eckles, who has extensive experience in designing and teaching data analysis courses
Introduces learners to exploratory data analysis (EDA), a fundamental skill for data scientists and analysts
Guides learners in summarizing data sets using R, a widely used programming language for data analysis
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