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Jane Wall
This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming
Read more
This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming
Read more
This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:
• you want to do data analysis but don’t know programming
• you know programming but aren’t too familiar with R
• you know some R programming but want to learn more about the tidyverse verbs
It is best taken following the first course in the specialization or if you already are familiar with ggplot, RMarkdown, and basic function writing in R. You will use learn to use readr to read in your data, dplyr to analyze your data, and stringr and forcats to manipulate strings and factors.
Register for this course and see more details by visiting:
OpenCourser.com/course/a2ulbx/data
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What's inside
Syllabus
Projects, Tibbles and Importing Data
When analyzing data, you will often be required to import data from CSV or txt files. In this module, you will learn how to import and parse data in base R and the readr library, a package in the Tidyverse. You will also be introduced to R projects, which help store and organize data files associated with an analysis.
Read more
Syllabus
Projects, Tibbles and Importing Data
When analyzing data, you will often be required to import data from CSV or txt files. In this module, you will learn how to import and parse data in base R and the readr library, a package in the Tidyverse. You will also be introduced to R projects, which help store and organize data files associated with an analysis.
Read more
Tidying Data
Data are stored in tabular forms and are often organized differently depending on its use. In this module, you will learn how to reorganize data to produce a "tidy" data set, where every variable is stored in its own column, every observation is stored in its own row, and each value is stored in a table cell.
Relational Data
Data analysis rarely involves a single data table and you will be required to combine multiple related tables to answer questions you are interested in. In this module, you will learn and practice mutating variables and filtering observations from relational data.
String Manipulation and Regular Expressions
This module will introduce string manipulation in R. You will learn the basics of strings, including string creation, merging, and subsetting. Then, you will use regular expressions to describe and view patterns in strings.
Categorical Variables and Factors
In the last module of the course, you will use the forcats package in the tidyverse to work with categorical variables, variables that have discrete values. The forcats package introduces factors - data objects used to categorize the data in levels. You will practice creating and modifying factors.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Well-suited for those with little to no R programming experience who want to learn data analysis
Useful for students familiar with R programming that want to strengthen their tidyverse skills
Covers essential tidyverse packages and techniques for data analysis, including readr, dplyr, stringr, and forcats
Provides a solid foundation for beginners in data analysis using R and the tidyverse
Assumes students have some familiarity with ggplot, RMarkdown, and basic function writing in R
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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 Tidyverse with these
activities:
Review Probability and Statistics
Show steps
Refresh understanding of probability and statistics concepts
Browse courses on
Probability
Show steps
-
Review lecture notes or textbooks on probability and statistics
-
Complete practice problems
Reading: Introduction to Data Science in R
Show steps
Provide a comprehensive background on data science fundamentals
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read Chapters 1-3
-
Complete the exercises in the book
Review R Markdown
Show steps
Solidify knowledge of how to create dynamic documents in R Markdown
Browse courses on
R Markdown
Show steps
-
Review documentation for R Markdown
-
Create a new R Markdown document
-
Insert code chunks and text
-
Knit the document to HTML
Six other activities
Expand to see all activities and additional details
Show all nine activities
Interactive Tutorials on String Manipulation
Show steps
Enhance understanding of string manipulation techniques in R
Browse courses on
String Manipulation
Show steps
-
Complete the tutorials on the RStudio website
-
Practice string manipulation exercises
Data Manipulation Practice
Show steps
Practice data manipulation skills using the tidyverse verbs
Browse courses on
Data Manipulation
Show steps
-
Load a dataset into R
-
Use dplyr to filter the data
-
Use tidyr to reshape the data
-
Use stringr to manipulate strings
Study Group for Relational Data
Show steps
Engage with peers to discuss and reinforce concepts of relational data and data analysis
Browse courses on
Relational Data
Show steps
-
Form a study group with classmates
-
Meet regularly to discuss course materials and practice exercises
Data Cleaning and Tidying Project
Show steps
Develop skills in cleaning and tidying real-world datasets
Browse courses on
Data Cleaning
Show steps
-
Obtain a messy dataset
-
Use dplyr and tidyr to clean and tidy the data
-
Create a report documenting the cleaning and tidying process
Data Visualization Project
Show steps
Apply data visualization principles to create a visually appealing and informative plot
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and a research question
-
Explore the data and identify patterns
-
Create a ggplot2 plot
-
Refine the plot and add annotations
-
Write a brief report summarizing the findings
Volunteer at a Data Science Project
Show steps
Gain practical experience in data science by contributing to real-world projects
Browse courses on
Data Science
Show steps
-
Find a local data science organization or project
-
Contact the organization and inquire about volunteer opportunities
-
Commit to a regular volunteering schedule
-
Contribute to data analysis, visualization, or other tasks
Review Probability and Statistics
Show steps
Refresh understanding of probability and statistics concepts
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on probability and statistics
- Complete practice problems
Reading: Introduction to Data Science in R
Show steps
Provide a comprehensive background on data science fundamentals
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3
- Complete the exercises in the book
Review R Markdown
Show steps
Solidify knowledge of how to create dynamic documents in R Markdown
Browse courses on
R Markdown
Show steps
Show steps
Show steps
- Review documentation for R Markdown
- Create a new R Markdown document
- Insert code chunks and text
- Knit the document to HTML
Six other activities
Expand to see all activities and additional details
Show all nine activities
Interactive Tutorials on String Manipulation
Show steps
Enhance understanding of string manipulation techniques in R
Browse courses on
String Manipulation
Show steps
Show steps
Show steps
- Complete the tutorials on the RStudio website
- Practice string manipulation exercises
Data Manipulation Practice
Show steps
Practice data manipulation skills using the tidyverse verbs
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Load a dataset into R
- Use dplyr to filter the data
- Use tidyr to reshape the data
- Use stringr to manipulate strings
Study Group for Relational Data
Show steps
Engage with peers to discuss and reinforce concepts of relational data and data analysis
Browse courses on
Relational Data
Show steps
Show steps
Show steps
- Form a study group with classmates
- Meet regularly to discuss course materials and practice exercises
Data Cleaning and Tidying Project
Show steps
Develop skills in cleaning and tidying real-world datasets
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Obtain a messy dataset
- Use dplyr and tidyr to clean and tidy the data
- Create a report documenting the cleaning and tidying process
Data Visualization Project
Show steps
Apply data visualization principles to create a visually appealing and informative plot
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and a research question
- Explore the data and identify patterns
- Create a ggplot2 plot
- Refine the plot and add annotations
- Write a brief report summarizing the findings
Volunteer at a Data Science Project
Show steps
Gain practical experience in data science by contributing to real-world projects
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a local data science organization or project
- Contact the organization and inquire about volunteer opportunities
- Commit to a regular volunteering schedule
- Contribute to data analysis, visualization, or other tasks
Career center
Learners who complete Data Analysis with Tidyverse will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts help make sense of raw data. They use their skills to identify trends, patrones, and other useful information that companies can use to make better decisions. As a data analyst, you will apply your skills to interpret large amounts of data using the tidyverse, including data that have been imported from CSV and txt files. By learning how to reorganize data to be "tidy," you will prepare yourself for this role, where data is often stored in a variety of tabular forms and must be organized to be analyzed. This course will be most helpful to you in your role as a data analyst if you are new to the field or are looking to gain experience with a new skill such as tidyverse.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data, and then presenting that data in a way that is easy to understand. This course will help you build a foundation in the tidyverse, a collection of packages in R that helps data scientists analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and machine learning techniques.
Statistician
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. This course will help you build a foundation in data analysis using the tidyverse, a collection of packages in R that helps statisticians analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical techniques.
Financial Analyst
Financial Analyst
Financial analysts use data to make sound decisions about investments. As a financial analyst, you will be responsible for analyzing financial data and making recommendations to clients. This course will help you develop the skills you need to analyze financial data using the tidyverse, a collection of packages in R that helps financial analysts analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and financial techniques.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and make sound decisions about marketing campaigns. This course will help you build a foundation in data analysis using the tidyverse, a collection of packages in R that helps market researchers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and marketing techniques.
Business Analyst
Business Analyst
Business analysts use data to help businesses make better decisions. This course will help you build a foundation in data analysis using the tidyverse, a collection of packages in R that helps business analysts analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and business techniques.
Data Engineer
Data Engineer
Data engineers design, build, and maintain the data infrastructure that businesses use to store and process data. This course will help you build a foundation in data engineering using the tidyverse, a collection of packages in R that helps data engineers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and data engineering techniques.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. This course will help you build a foundation in software engineering using the tidyverse, a collection of packages in R that helps software engineers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and software engineering techniques.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use data to make investment decisions. This course will help you build a foundation in quantitative analysis using the tidyverse, a collection of packages in R that helps quantitative analysts analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and quantitative analysis techniques.
Actuary
Actuary
Actuaries use data to assess risk and make sound decisions about insurance and financial products. This course will help you build a foundation in actuarial science using the tidyverse, a collection of packages in R that helps actuaries analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and actuarial science techniques.
Epidemiologist
Epidemiologist
Epidemiologists use data to investigate the causes of disease and promote public health. This course will help you build a foundation in epidemiology using the tidyverse, a collection of packages in R that helps epidemiologists analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and epidemiological techniques.
Biostatistician
Biostatistician
Biostatisticians use data to design and analyze clinical trials and other health-related research studies. This course will help you build a foundation in biostatistics using the tidyverse, a collection of packages in R that helps biostatisticians analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and biostatistical techniques.
Data Journalist
Data Journalist
Data journalists use data to tell stories and inform the public. This course will help you build a foundation in data journalism using the tidyverse, a collection of packages in R that helps data journalists analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and data journalism techniques.
Librarian
Librarian
Librarians help people find and access information. This course will help you build a foundation in library science using the tidyverse, a collection of packages in R that helps librarians analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and library science techniques.
Teacher
Teacher
Teachers help students learn and grow. This course will help you build a foundation in education using the tidyverse, a collection of packages in R that helps teachers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and educational techniques.
For more career information including salaries, visit:
OpenCourser.com/course/a2ulbx/data
Reading list
We've selected 13 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 Tidyverse.
Save
Provides a comprehensive introduction to R for data science, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about R and its applications in data science.
Provides a comprehensive guide to data manipulation in R, covering topics such as data import, cleaning, transformation, and analysis. It valuable resource for anyone who wants to learn how to manipulate data effectively in R.
Provides a comprehensive guide to ggplot2, a powerful package for data visualization in R. It valuable resource for anyone who wants to learn how to create beautiful and informative data visualizations.
Provides a comprehensive guide to statistical learning, covering topics such as linear models, regularization, and ensemble methods. It valuable resource for anyone who wants to learn more about the theory and practice of statistical learning.
Save
Provides a comprehensive guide to predictive modeling, covering topics such as data preparation, model selection, and model evaluation. It valuable resource for anyone who wants to learn how to build predictive models that can be used to make informed decisions.
Provides a comprehensive guide to data science for business, covering topics such as data collection, data analysis, and data visualization. It valuable resource for anyone who wants to learn how to use data science to improve business outcomes.
Save
Provides a comprehensive guide to machine learning for business, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn how to use machine learning to solve business problems.
Save
Provides a comprehensive guide to deep learning for business, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn how to use deep learning to solve business problems.
Provides a comprehensive guide to reinforcement learning for business, covering topics such as Markov decision processes, value iteration, and policy iteration. It valuable resource for anyone who wants to learn how to use reinforcement learning to solve business problems.
Save
Covers advanced topics in R, such as functional programming, object-oriented programming, and data visualization. It good resource for anyone who wants to learn more about the inner workings of R and how to use it to solve complex problems.
Save
Provides a comprehensive guide to R Markdown, a powerful tool for creating dynamic and reproducible reports, presentations, and dashboards. It valuable resource for anyone who wants to learn how to use R Markdown to communicate their data analysis results effectively.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for anyone who wants to learn more about the theory and practice of statistical learning.
Provides a comprehensive guide to R programming, covering topics such as data types, control flow, functions, and graphics. It valuable resource for anyone who wants to learn how to program in R effectively.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/a2ulbx/data
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Effort
3 to 4 hours per week for five weeks.
Level
Introductory
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Jane Wall
Language
English
Transcripts
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This course belongs to a
collection
called
Expressway to Data Science: R Programming and Tidyverse. It's comprised of three courses:
- Introduction to R Programming and Tidyverse
- Data Analysis with Tidyverse (viewing)
- R Programming and Tidyverse Capstone Project
Good to know
Well-suited for those with little to no R programming experience who want to learn data analysis
Useful for students familiar with R programming that want to strengthen their tidyverse skills
Covers essential tidyverse packages and techniques for data analysis, including readr, dplyr, stringr, and forcats
Provides a solid foundation for beginners in data analysis using R and the tidyverse
Assumes students have some familiarity with ggplot, RMarkdown, and basic function writing in R
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