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Emmanuel Segui

In this 1-hour long project-based course, you will learn how to read all sorts of data and import them into R, including CSV files, Excel files, data from other statistical software, the web and from relational databases.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

Project Overview
In this 1-hour long project-based course, you will learn how to read all sorts of data and import them into R, including CSV files, Excel files, data from other statistical software, the web and from relational databases.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Addresses the need to import data of all sorts into R, which is a key skill for data science
Part of a specialization on data science in R, so it also assumes you have strong foundation 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 Importing Data into R with these activities:
Review basic statistics
Ensure that you have a strong foundation in statistics before starting this course
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  • Review your notes from a previous statistics course
  • Take a practice quiz or exam to test your understanding
Follow a tutorial on a specific data import method
Enhance your understanding of a specific data import method through a guided tutorial
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  • Identify a specific data import method you want to learn
  • Find a reputable tutorial on the method
  • Follow the tutorial step-by-step
Solve practice problems on data import
Practice importing data into R to improve your proficiency
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  • Find a set of practice problems online or in a textbook
  • Solve the problems step-by-step
  • Check your answers against the provided solutions
Five other activities
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Show all eight activities
Participate in a study group on data import
Collaborate with peers to discuss and solve data import challenges
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  • Find a study group or create your own
  • Meet with your group regularly to discuss data import topics
  • Work together to solve problems and answer questions
Write a blog post on data import techniques
Reinforce your understanding of data import by explaining it to others
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  • Choose a specific data import technique to focus on
  • Research the technique thoroughly
  • Write a clear and concise blog post explaining the technique
Attend a workshop on advanced data import techniques
Gain exposure to cutting-edge data import techniques through a workshop
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  • Find a reputable workshop on advanced data import techniques
  • Attend the workshop and actively participate
  • Apply the techniques learned in your own work
Develop a data import pipeline for a real-world dataset
Build a practical data import pipeline to solidify your understanding and skills
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  • Identify a real-world dataset that you want to import into R
  • Design and implement a data import pipeline to bring the dataset into R
  • Validate the imported data to ensure accuracy and completeness
Contribute to an open-source data import library
Deepen your understanding by contributing to a real-world data import project
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  • Identify an open-source data import library to contribute to
  • Read the documentation and get familiar with the codebase
  • Identify an area where you can make a contribution

Career center

Learners who complete Importing Data into R will develop knowledge and skills that may be useful to these careers:
Database Administrator
Database Administrators are responsible for the design, implementation, and maintenance of databases. They use their skills in data management, database design, and SQL to help businesses manage and analyze their data. This course can help you develop the skills needed to be a successful Database Administrator, including how to import data from a variety of sources, how to design and build databases, and how to manage and maintain databases.
Data Engineer
Data Engineers design, build, and maintain data systems. They use their skills in data management, data architecture, and software engineering to help businesses manage and analyze their data. This course can help you develop the skills needed to be a successful Data Engineer, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to design and build data systems.
Statistician
Statisticians collect, analyze, interpret, and present data. They use their skills in probability, statistics, and data analysis to help businesses and organizations make better decisions. This course can help you develop the skills needed to be a successful Statistician, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to use statistical software to analyze data.
Data Scientist
Data Scientists are responsible for developing and implementing data-driven solutions to business problems. They use their skills in data analysis, machine learning, and artificial intelligence to help businesses make better decisions. This course can help you develop the skills needed to be a successful Data Scientist, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to use statistical software and machine learning algorithms to analyze data.
Business Analyst
Business Analysts help businesses identify and solve problems using data. They use their skills in data analysis, process improvement, and business intelligence to help businesses make better decisions. This course can help you develop the skills needed to be a successful Business Analyst, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to use statistical software and business intelligence tools to analyze data.
Data Analyst
Data Analysts help businesses solve problems and make better decisions using data. They collect, clean, and analyze data to identify trends and patterns. This course can help you develop the skills needed to be a successful Data Analyst, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to use statistical software to analyze data.
Market Researcher
Market Researchers collect and analyze data about consumers and markets. They use their skills in data analysis, consumer behavior, and marketing to help businesses make better decisions about their products and services. This course can help you develop the skills needed to be a successful Market Researcher, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to use statistical software and market research tools to analyze data.
Information Technology Specialist
Information Technology Specialists provide technical support to businesses and organizations. They use their skills in computer hardware, software, and networking to help businesses solve technical problems and maintain their IT systems. This course can help you develop the skills needed to be a successful Information Technology Specialist, including how to import data from a variety of sources, how to clean and prepare data for analysis, and how to use statistical software and IT tools to analyze data.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help businesses solve problems and improve their operations. They use their skills in operations research, data analysis, and optimization to help businesses make better decisions. This course may help you develop the skills needed to be a successful Operations Research Analyst, including how to import data from a variety of sources and how to use statistical software to analyze data.
Computer Programmer
Computer Programmers write and maintain computer code. They use their skills in programming, software design, and computer science to help businesses solve problems and improve their operations. This course may help you develop the skills needed to be a successful Computer Programmer, including how to import data from a variety of sources and how to use statistical software to analyze data.
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their skills in programming, software design, and computer science to help businesses solve problems and improve their operations. This course may help you develop the skills needed to be a successful Software Engineer, including how to import data from a variety of sources and how to use statistical software to analyze data.
Web Developer
Web Developers design, develop, and maintain websites. They use their skills in HTML, CSS, JavaScript, and web design to help businesses create and maintain their online presence. This course may help you develop the skills needed to be a successful Web Developer, including how to import data from a variety of sources and how to use statistical software to analyze data.
Financial Analyst
Financial Analysts use financial data to help businesses make investment decisions. They use their skills in financial analysis, modeling, and forecasting to help businesses make better decisions about their investments. This course may help you develop the skills needed to be a successful Financial Analyst, including how to import data from a variety of sources and how to use statistical software to analyze data.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They use their skills in actuarial science, risk management, and financial analysis to help businesses and individuals make better decisions about their financial future. This course may help you develop the skills needed to be a successful Actuary, including how to import data from a variety of sources and how to use statistical software to analyze data.
Systems Analyst
Systems Analysts design, develop, and implement computer systems. They use their skills in systems analysis, software design, and business process improvement to help businesses solve problems and improve their operations. This course may help you develop the skills needed to be a successful Systems Analyst, including how to import data from a variety of sources and how to use statistical software to analyze data.

Reading list

We've selected 14 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 Importing Data into R.
Comprehensive guide to the R programming language. It covers all aspects of the language, from basic data types and operators to advanced topics such as object-oriented programming and statistical modeling. It valuable reference for anyone who wants to learn R.
Is an advanced introduction to statistical learning. It covers topics such as supervised learning, unsupervised learning, and statistical modeling. It valuable resource for anyone who wants to learn about statistical learning at a more advanced level.
Practical guide to using R for data science. It covers topics such as data import and export, data cleaning, data visualization, and statistical modeling. It valuable resource for anyone who wants to learn how to use R for data science.
Provides a comprehensive introduction to data manipulation in R, covering topics such as data cleaning, transformation, and aggregation. It valuable reference for anyone who wants to learn how to work with data in R.
Comprehensive guide to advanced R programming techniques. It covers topics such as object-oriented programming, functional programming, and statistical modeling. It valuable reference for anyone who wants to learn how to use R for advanced data analysis.
Is an introduction to statistical learning. It covers topics such as supervised learning, unsupervised learning, and statistical modeling. It valuable resource for anyone who wants to learn about statistical learning.
Practical guide to using R for data analysis. It covers topics such as data import and export, data cleaning, data visualization, and statistical modeling. It valuable resource for anyone who wants to learn how to use R for data analysis.
Is an introduction to data mining using R. It covers topics such as data preprocessing, data mining algorithms, and data visualization. It valuable resource for anyone who wants to learn how to use R for data mining.
Is an introduction to time series analysis using R. It covers topics such as time series data, time series models, and time series forecasting. It valuable resource for anyone who wants to learn how to use R for time series analysis.
Is an introduction to spatial data analysis using R. It covers topics such as spatial data, spatial data analysis methods, and spatial data visualization. It valuable resource for anyone who wants to learn how to use R for spatial data analysis.
Is an introduction to R for beginners. It covers topics such as basic data types and operators, data import and export, and data visualization. It valuable resource for anyone who wants to learn how to use R for basic data analysis.
Is an introduction to Python for data analysis. It covers topics such as data import and export, data cleaning, data visualization, and statistical modeling. It valuable resource for anyone who wants to learn how to use Python for data analysis.
Is an introduction to deep learning using R. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn how to use R for deep learning.

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