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Programming with R

Data analysis is an incredibly valuable skill for everyone in every industry. Assuming no prior knowledge, this course will show you the basics of R programming language such as data types and data structures, conditional statements, and functions.

At the core of data analysis is a thorough knowledge of R programming. In this course,

, you will learn how to manipulate different objects.

First, you will learn the basic syntax for R coding.

Next, you will explore data types and data structures available in R.

At the core of data analysis is a thorough knowledge of R programming. In this course,

, you will learn how to manipulate different objects.

First, you will learn the basic syntax for R coding.

Next, you will explore data types and data structures available in R.

Finally, you will discover how to write your own functions by implementing control flow statements.

When you are finished with this course, you will have a foundational knowledge of R programming that will help you as you move forward to data science.

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What's inside

Syllabus

Course Overview

Getting Started with R

Exploring Vectors and Factors

Using Matrices, Arrays, and Lists

Working with Data Frames

Managing Control Statements

Building Your First Function

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers foundational principles of data analysis in a structured manner

Teaches practical implementation of data analysis techniques using R programming

Appropriate for beginners with no prior knowledge of R programming

Builds a strong foundation for further exploration in data science

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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 Programming with R with these activities:

Organize and Review Course Materials

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Consolidate your notes, assignments, quizzes, and readings to enhance your comprehension and prepare for assessments.

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Gather and organize your course materials into a central location.
Review your notes and assignments, identifying areas for further study.
Create summaries or flashcards to reinforce key concepts.

Read 'R for Data Science'

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Reinforce your understanding of R programming fundamentals and data analysis techniques by reading this comprehensive guide.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Obtain a copy of 'R for Data Science'.
Read through the book, taking notes and highlighting important concepts.
Complete the exercises and examples provided in the book.

Follow R Programming Tutorials

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Seek out and follow online or in-person tutorials to supplement your understanding of R programming concepts and techniques.

Browse courses on R Programming

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Identify topics you want to learn or improve in.
Search for reputable R programming tutorials that cover those topics.
Follow the tutorials step-by-step, taking notes and practicing the code examples.

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Practice R Coding Exercises

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Regularly work through coding exercises to reinforce your understanding of R syntax, data structures, and conditional statements.

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Find online R coding challenges or exercises.
Set aside dedicated time for practicing R coding.
Review the solutions to exercises and identify areas for improvement.

Attend R User Group Meetups

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Connect with fellow R enthusiasts, exchange knowledge, and learn about the latest developments in the R community.

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Locate R User Group meetups in your area.
Attend meetups regularly and participate in discussions.
Share your experiences and insights with the group.

Build a Small Data Analysis Project

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Apply your R programming skills to a practical project, such as analyzing a dataset or creating an interactive visualization, to solidify your understanding and gain hands-on experience.

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Define a small-scale data analysis project that aligns with your interests or learning goals.
Gather and clean the data for your project.
Analyze the data using R programming techniques.
Present your findings in a clear and concise manner.

Create a Function Library for R

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Develop a library of reusable R functions that address common data analysis tasks, enhancing your programming efficiency and fostering code maintainability.

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Identify repetitive or complex data analysis tasks that can be automated.
Design and implement R functions to perform these tasks.
Test and debug your functions to ensure their accuracy and efficiency.
Document your functions with clear and concise descriptions.

Career center

Learners who complete Programming with R will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts make sense of raw data by cleaning, organizing, and presenting information. R programming is a key tool for Data Analysts to manipulate, visualize, and analyze large datasets. This course can help you build a strong foundation in R, which is highly sought after by employers in the field of Data Analytics.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists use scientific methods, processes, algorithms, and systems to extract insights from data. This course can help you build a strong foundation in R, which is widely used by Data Scientists to analyze data, build models, and create data visualizations.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, interpret, and present data. R programming is a valuable tool for Statisticians to perform statistical analysis, create visualizations, and build models. This course can help you learn the basics of R, which is essential for success in the field of Statistics.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze and predict financial data. R programming is widely used by Quantitative Analysts to perform analysis, build models, and make trading decisions. This course can help you build a strong foundation in R, which is essential for success in the field of Quantitative Analysis.

See salaries and explore the career path for Quantitative Analyst

Software Engineer

Software Engineers design, develop, and maintain software applications. R programming is increasingly used by Software Engineers to analyze data, build models, and create data visualizations. This course can help you gain a competitive advantage by learning the basics of R, which is becoming a valuable skill for Software Engineers.

See salaries and explore the career path for Software Engineer

Data Journalist

Data Journalists use data to tell stories and inform the public. R programming is a powerful tool for Data Journalists to clean, analyze, and visualize data. This course can help you build a strong foundation in R, which is essential for success in the field of Data Journalism.

See salaries and explore the career path for Data Journalist

Market Researcher

Market Researchers collect, analyze, and interpret data to understand consumer behavior and trends. R programming is increasingly used by Market Researchers to conduct surveys, analyze data, and create visualizations. This course can help you develop the skills you need to succeed in the field of Market Research.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts use data to make investment recommendations. R programming is a powerful tool for Financial Analysts to analyze financial data, build models, and make trading decisions. This course can help you build a strong foundation in R, which is essential for success in the field of Financial Analysis.

See salaries and explore the career path for Financial Analyst

Business Analyst

Business Analysts use data to improve business processes and make informed decisions. R programming is increasingly used by Business Analysts to analyze data, build models, and create visualizations. This course can help you build a strong foundation in R, which is becoming a valuable skill for Business Analysts.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex business problems. R programming is widely used by Operations Research Analysts to analyze data, build models, and make recommendations. This course can help you build a strong foundation in R, which is essential for success in the field of Operations Research.

See salaries and explore the career path for Operations Research Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. R programming is increasingly used by Actuaries to analyze data, build models, and make predictions. This course can help you build a strong foundation in R, which is becoming a valuable skill for Actuaries.

See salaries and explore the career path for Actuary

Economist

Economists use data to analyze economic trends and make predictions. R programming is increasingly used by Economists to analyze data, build models, and create visualizations. This course can help you develop the skills you need to succeed in the field of Economics.

See salaries and explore the career path for Economist

Epidemiologist

Epidemiologists use data to study the distribution and determinants of health-related states or events in defined populations. R programming is increasingly used by Epidemiologists to analyze data, build models, and create visualizations. This course can help you develop the skills you need to succeed in the field of Epidemiology.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical methods to design and analyze studies in the health sciences. R programming is widely used by Biostatisticians to analyze data, build models, and make predictions. This course can help you build a strong foundation in R, which is essential for success in the field of Biostatistics.

See salaries and explore the career path for Biostatistician

Clinical Research Associate

Clinical Research Associates manage clinical trials and ensure that they are conducted in accordance with good clinical practice guidelines. R programming is increasingly used by Clinical Research Associates to analyze data, build models, and create visualizations. This course can help you develop the skills you need to succeed in the field of Clinical Research.

See salaries and explore the career path for Clinical Research Associate

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 Programming with R.

R for Data Science

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Comprehensive guide to using R for data analysis. It covers a wide range of topics, from the basics of R programming to more advanced techniques like data visualization and machine learning. It valuable resource for anyone who wants to learn how to use R for data science.

Programming with R

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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