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Yan Luo
The R language plays a critical role in data analysis and a common programming language when working in the field of data science & analytics. This course will introduce you to R language fundamentals like data types, techniques for manipulation, and how to implement fundamental programming tasks. We’ll also cover common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.
Read more
The R language plays a critical role in data analysis and a common programming language when working in the field of data science & analytics. This course will introduce you to R language fundamentals like data types, techniques for manipulation, and how to implement fundamental programming tasks. We’ll also cover common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.
Read more
The R language plays a critical role in data analysis and a common programming language when working in the field of data science & analytics. This course will introduce you to R language fundamentals like data types, techniques for manipulation, and how to implement fundamental programming tasks. We’ll also cover common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.
This course emphasizes hands-on and practical learning. You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to produce data-driven insights.
No prior knowledge of R, or programming is required.
What you'll learn
-
Manipulate numeric and textual data types using the R programming language and RStudio or Jupyter Notebooks.
-
Define and manipulate R data structures, including vectors, factors, lists, and data frames.
-
Control program flow, define functions, perform character string and date operations, define regular expressions, and handle errors.
-
Read, write, and save data files and scrape web pages using R.
Register for this course and see more details by visiting:
OpenCourser.com/course/sr4n2o/r
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Teaches foundational R programming skills critical to working in the field of data science
Provides hands-on and practical learning with RStudio and Jupyter notebooks
Develops foundational skills in data manipulation and programming fundamentals
Requires no prior knowledge of R or programming, which makes it accessible to beginners
Advises students to take other courses first as prerequisites
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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 R Programming Basics for Data Science with these
activities:
Organize your course materials
Show steps
Ensure you have all the necessary materials and keep them organized for easy access during the course.
Show steps
-
Create a folder on your computer to store all course materials.
-
Download and save all course materials, including lecture notes, assignments, and datasets.
-
Keep a notebook or use a digital note-taking app to record your notes and ideas.
Review R basics
Show steps
Refresh your memory on the basics of R programming before starting the course.
Browse courses on
Data Types
Show steps
-
Review the course materials on R basics.
-
Complete the practice exercises provided in the course materials.
-
Take an online quiz or assessment to test your understanding.
Review data analysis concepts
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Refresh your knowledge of data analysis concepts before starting the course.
Browse courses on
Data Analysis
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-
Review the course materials on data analysis concepts.
-
Complete the practice exercises provided in the course materials.
-
Take an online quiz or assessment to test your understanding.
Five other activities
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Read 'R for Data Science'
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Gain a deeper understanding of R fundamentals and data science concepts by reading this comprehensive book.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read the chapters that correspond to the topics covered in the course.
-
Work through the practice exercises in the book.
-
Apply the concepts you learn to your own projects.
Follow R tutorials
Show steps
Expand your R knowledge and skills by following tutorials that cover specific topics or techniques.
Show steps
-
Identify tutorials that align with your learning goals.
-
Follow the instructions in the tutorials carefully.
-
Experiment with the code and apply it to your own projects.
Practice R exercises
Show steps
Reinforce your understanding of R basics and data manipulation techniques by completing practice exercises.
Browse courses on
Data Types
Show steps
-
Complete the exercises provided in the course materials.
-
Find additional practice exercises online or in books.
-
Collaborate with classmates to solve problems.
Create a blog post or video tutorial
Show steps
Solidify your understanding of R and data science concepts by creating a blog post or video tutorial that shares your knowledge with others.
Browse courses on
R Programming
Show steps
-
Choose a topic that you are familiar with and that would be valuable to others.
-
Write or record your content in a clear and concise manner.
-
Share your content on social media or other platforms.
Build a data analysis project
Show steps
Apply your R skills to create a data analysis project that demonstrates your understanding of the concepts covered in the course.
Browse courses on
Data Analysis
Show steps
-
Define the problem you want to solve or the question you want to answer.
-
Collect and clean the necessary data.
-
Analyze the data using R.
-
Draw conclusions and create visualizations to present your findings.
Organize your course materials
Show steps
Ensure you have all the necessary materials and keep them organized for easy access during the course.
Show steps
Show steps
Show steps
- Create a folder on your computer to store all course materials.
- Download and save all course materials, including lecture notes, assignments, and datasets.
- Keep a notebook or use a digital note-taking app to record your notes and ideas.
Review R basics
Show steps
Refresh your memory on the basics of R programming before starting the course.
Browse courses on
Data Types
Show steps
Show steps
Show steps
- Review the course materials on R basics.
- Complete the practice exercises provided in the course materials.
- Take an online quiz or assessment to test your understanding.
Review data analysis concepts
Show steps
Refresh your knowledge of data analysis concepts before starting the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Review the course materials on data analysis concepts.
- Complete the practice exercises provided in the course materials.
- Take an online quiz or assessment to test your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read 'R for Data Science'
Show steps
Gain a deeper understanding of R fundamentals and data science concepts by reading this comprehensive book.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters that correspond to the topics covered in the course.
- Work through the practice exercises in the book.
- Apply the concepts you learn to your own projects.
Follow R tutorials
Show steps
Expand your R knowledge and skills by following tutorials that cover specific topics or techniques.
Show steps
Show steps
Show steps
- Identify tutorials that align with your learning goals.
- Follow the instructions in the tutorials carefully.
- Experiment with the code and apply it to your own projects.
Practice R exercises
Show steps
Reinforce your understanding of R basics and data manipulation techniques by completing practice exercises.
Browse courses on
Data Types
Show steps
Show steps
Show steps
- Complete the exercises provided in the course materials.
- Find additional practice exercises online or in books.
- Collaborate with classmates to solve problems.
Create a blog post or video tutorial
Show steps
Solidify your understanding of R and data science concepts by creating a blog post or video tutorial that shares your knowledge with others.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Choose a topic that you are familiar with and that would be valuable to others.
- Write or record your content in a clear and concise manner.
- Share your content on social media or other platforms.
Build a data analysis project
Show steps
Apply your R skills to create a data analysis project that demonstrates your understanding of the concepts covered in the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define the problem you want to solve or the question you want to answer.
- Collect and clean the necessary data.
- Analyze the data using R.
- Draw conclusions and create visualizations to present your findings.
Career center
Learners who complete R Programming Basics for Data Science will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is a vital professional who uses their skills in analytical reasoning, modeling, and data interpretation to identify, extract, and communicate meaningful insights from complex data. Data Scientists work with teams across the organization to build and deploy predictive models that optimize business outcomes. They also collaborate with Data Engineers to ensure that data is properly collected, stored, and processed. This course provides a foundational understanding of the fundamental concepts of R programming, including data manipulation, analysis, and visualization, which are critical skills for Data Scientists. By completing this course, you will be well-equipped to embark on a career in Data Science or enhance your current role.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and transforming raw data into a format that can be analyzed and interpreted. They use statistical techniques and programming languages like R to identify trends and patterns in data and communicate their findings to stakeholders. This course introduces learners to the basics of R programming, including data manipulation, visualization, and statistical analysis. By mastering these skills, learners can develop a strong foundation for a career as a Data Analyst and gain the ability to extract meaningful insights from data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts (Quants) use mathematical and statistical models to analyze financial data and make investment recommendations. They play a critical role in the financial industry, helping investment firms make informed decisions about portfolio management and risk assessment. This course provides a strong foundation in R programming, which is widely used in the finance industry for data analysis and modeling. By mastering the concepts taught in this course, learners can develop the skills necessary to succeed as a Quant and make meaningful contributions to the financial sector.
Data Science Manager
Data Science Manager
Data Science Managers are responsible for leading and managing teams of data scientists and data analysts. They set the strategic direction for data science initiatives, manage resources, and ensure that data science projects are aligned with business objectives. This course provides a foundation in R programming, which is widely used in data science for data analysis and modeling. By completing this course, learners can develop the skills necessary to lead and manage data science teams and drive successful data-driven initiatives.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work with data scientists and software engineers to build and maintain machine learning systems that can automate tasks, make predictions, and improve decision-making. This course provides a solid foundation in R programming, which is often used for data manipulation, analysis, and visualization in machine learning projects. By completing this course, learners will gain the necessary skills to contribute to the development and deployment of machine learning models.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, interpret, and present data. They work in various fields, including healthcare, finance, and market research, to provide insights and make informed decisions. This course provides a solid foundation in R programming, an essential tool for Statisticians. Through hands-on exercises and practical examples, learners will develop proficiency in data manipulation, analysis, and visualization, which are fundamental skills for success in this role.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists are responsible for creating visual representations of data to communicate insights and trends. They work with data scientists and business analysts to translate complex data into visually appealing and easy-to-understand formats. This course provides a foundational understanding of R programming, which is widely used for data visualization. By completing this course, learners will gain the skills necessary to create compelling data visualizations that effectively communicate insights and drive decision-making.
Financial Analyst
Financial Analyst
Financial Analysts use financial data and analysis to make recommendations on investments and financial decisions. They work in various sectors, including banking, investment management, and insurance, to provide insights and advice to clients. This course provides a foundation in R programming, which is increasingly used in the finance industry for data analysis and modeling. By completing this course, learners can develop the skills necessary to succeed as a Financial Analyst and make informed decisions about financial matters.
Business Analyst
Business Analyst
Business Analysts use data analysis techniques to identify opportunities for improvement and optimize business processes. They work with stakeholders across the organization to gather requirements, analyze data, and develop recommendations for改进. This course provides a strong foundation in R programming, which is increasingly used in business analysis for data manipulation, visualization, and statistical analysis. By mastering these skills, learners can enhance their ability to extract meaningful insights from data and drive business decisions.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines and infrastructure. They work with data scientists and data analysts to ensure that data is properly collected, stored, and processed for analysis. This course provides a foundational understanding of R programming, including data manipulation, transformation, and integration. By completing this course, learners will gain the skills necessary to contribute to the development and management of data pipelines and support data-driven decision-making.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and distribution of health-related events and diseases in populations. They use statistical methods and data analysis to identify risk factors, develop prevention strategies, and evaluate the effectiveness of public health interventions. This course provides a foundation in R programming, which is increasingly used in epidemiology for data analysis and visualization. By completing this course, learners can enhance their ability to analyze health data and contribute to the development of evidence-based public health policies.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to design and analyze research studies in the biomedical and health sciences. They work with researchers to develop study protocols, collect and analyze data, and interpret the results to answer scientific questions. This course provides a foundation in R programming, which is increasingly used in biostatistics for data analysis and visualization. By completing this course, learners can enhance their ability to analyze biomedical data and contribute to the advancement of scientific knowledge.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumer behavior and market trends to help businesses make informed decisions. They use various methods, including surveys, interviews, and data analysis, to gain insights into target markets and develop effective marketing strategies. This course provides a foundation in R programming, which is increasingly used in market research for data analysis and visualization. By completing this course, learners can enhance their ability to analyze market data and contribute to the development of successful marketing campaigns.
Chief Data Officer
Chief Data Officer
Chief Data Officers (CDOs) are responsible for overseeing all aspects of data management and analytics within an organization. They set the strategic vision for data-driven decision-making, manage data governance and compliance, and ensure that data is used ethically and responsibly. This course provides a foundation in R programming, which is increasingly used in data management and analytics. By completing this course, learners can enhance their ability to contribute to the development and implementation of data-driven strategies and become more effective CDOs.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work in various industries, including technology, finance, and healthcare, to create software solutions that meet specific business needs. This course provides a foundational understanding of R programming, which is increasingly used in software development for data analysis, visualization, and statistical modeling. By completing this course, learners can enhance their software development skills and become more competitive in the job market.
For more career information including salaries, visit:
OpenCourser.com/course/sr4n2o/r
Reading list
We've selected ten 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
R Programming Basics for Data Science.
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers a wide range of topics, including data manipulation, visualization, and statistical modeling.
Save
Comprehensive reference guide to the R programming language. It covers a wide range of topics, including data types, data structures, and programming techniques.
Save
Provides a comprehensive introduction to the advanced features of the R programming language. It covers a wide range of topics, including object-oriented programming, data visualization, and statistical modeling.
Provides a comprehensive introduction to machine learning algorithms using the R programming language. It covers a wide range of topics, including supervised learning, unsupervised learning, and ensemble methods.
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers a wide range of topics, including data manipulation, visualization, and statistical modeling.
Save
Provides a collection of recipes for solving common problems in R. It covers a wide range of topics, including data manipulation, visualization, and statistical modeling.
Provides a comprehensive introduction to marketing data analysis using the R programming language. It covers a wide range of topics, including data management, customer segmentation, and campaign analysis.
Save
Provides a comprehensive introduction to data mining algorithms using the R programming language. It covers a wide range of topics, including supervised learning, unsupervised learning, and ensemble methods.
Provides a comprehensive introduction to big data analysis using the R programming language. It covers a wide range of topics, including data management, parallelization, and machine learning.
Save
Provides a collection of recipes for creating visualizations in R. It covers a wide range of topics, including ggplot2, plotly, and dplyr.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sr4n2o/r
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Effort
1 to 2 hours per week for 5 weeks
Level
Elementary
Via
edX
Institution
IBM
Instructor
Yan Luo
Language
English
Transcript
English
Good to know
Teaches foundational R programming skills critical to working in the field of data science
Provides hands-on and practical learning with RStudio and Jupyter notebooks
Develops foundational skills in data manipulation and programming fundamentals
Requires no prior knowledge of R or programming, which makes it accessible to beginners
Advises students to take other courses first as prerequisites
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