We may earn an affiliate commission when you visit our partners.
Emmanuel Segui
In this 1-hour long project-based course, you will learn how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R
Read more
In this 1-hour long project-based course, you will learn how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R
Read more
In this 1-hour long project-based course, you will learn how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/1tt0vw/descriptive
Two deals to help you save
We found two deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
What's inside
Syllabus
Project Overview
In this project, you'll learn how to perform descriptive and inferential statistics in R, including how to summarize descriptive statistics, calculate correlations and perform hypothesis testing in R
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces foundational concepts in descriptive and hypothesis testing
Ideal for learners seeking foundational skills in statistics using R
Includes hands-on project-based learning
Save this course
Save Descriptive and Inferential Statistics in R to your list so you can find it easily later:
Save
Save
Reviews summary
Highly rated statistics course
Learners say Descriptive and Inferential Statistics in R is well-structured with an amazing instructor. However, the task platform could be improved.
Instructor is amazing.
"amazing instructor"
"I will attend all his courses"
Task platform could be improved.
"Like the idea of the course, but the task platform could be improved."
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 Descriptive and Inferential Statistics in R with these
activities:
Review introductory statistics
Show steps
Brush up on your foundational knowledge in statistics to strengthen your understanding of the concepts covered in this course.
Browse courses on
Statistics
Show steps
-
Read through your notes or textbook from a previous statistics course.
-
Work through practice problems and review solved examples.
Compile a list of R resources
Show steps
Expand your knowledge of R by compiling a comprehensive list of resources, including tutorials, documentation, and community forums.
Show steps
-
Search and gather online resources, tutorials, and documentation related to R.
-
Organize and categorize the resources into a central location, such as a spreadsheet or document.
Explore online tutorials on R
Show steps
Supplement your understanding of R by following online tutorials to practice and refine your skills.
Show steps
-
Search for reputable online resources and tutorials for learning R.
-
Follow along with the tutorials, completing exercises and examples.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or discussion forum
Show steps
Engage with other learners through study groups or discussion forums to exchange ideas, ask questions, and deepen your understanding of the course material.
Show steps
-
Identify online or offline study groups or discussion forums related to the course.
-
Participate actively in discussions, sharing your insights and seeking clarification from others.
Complete practice exercises on descriptive statistics
Show steps
Reinforce your understanding of descriptive statistics by completing practice exercises and reviewing solutions.
Show steps
-
Find online resources or textbooks with practice exercises on descriptive statistics.
-
Work through the exercises, calculating measures of central tendency and variability.
-
Review the solutions to check your understanding and identify areas for improvement.
Create a cheat sheet on correlation and hypothesis testing
Show steps
Solidify your understanding of correlation and hypothesis testing by creating a comprehensive cheat sheet that summarizes key concepts and formulas.
Show steps
-
Gather your notes and resources on correlation and hypothesis testing.
-
Organize and synthesize the information into a concise and visually appealing cheat sheet.
Begin a project using R to analyze real-world data
Show steps
Apply your skills in R to a practical project, solidifying your understanding and gaining hands-on experience with data analysis.
Show steps
-
Identify a real-world dataset that aligns with your interests.
-
Develop a project plan outlining your research question, analysis methods, and expected outcomes.
-
Start working on the project, exploring the data, performing analysis, and interpreting the results.
Review introductory statistics
Show steps
Brush up on your foundational knowledge in statistics to strengthen your understanding of the concepts covered in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Read through your notes or textbook from a previous statistics course.
- Work through practice problems and review solved examples.
Compile a list of R resources
Show steps
Expand your knowledge of R by compiling a comprehensive list of resources, including tutorials, documentation, and community forums.
Show steps
Show steps
Show steps
- Search and gather online resources, tutorials, and documentation related to R.
- Organize and categorize the resources into a central location, such as a spreadsheet or document.
Explore online tutorials on R
Show steps
Supplement your understanding of R by following online tutorials to practice and refine your skills.
Show steps
Show steps
Show steps
- Search for reputable online resources and tutorials for learning R.
- Follow along with the tutorials, completing exercises and examples.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a study group or discussion forum
Show steps
Engage with other learners through study groups or discussion forums to exchange ideas, ask questions, and deepen your understanding of the course material.
Show steps
Show steps
Show steps
- Identify online or offline study groups or discussion forums related to the course.
- Participate actively in discussions, sharing your insights and seeking clarification from others.
Complete practice exercises on descriptive statistics
Show steps
Reinforce your understanding of descriptive statistics by completing practice exercises and reviewing solutions.
Show steps
Show steps
Show steps
- Find online resources or textbooks with practice exercises on descriptive statistics.
- Work through the exercises, calculating measures of central tendency and variability.
- Review the solutions to check your understanding and identify areas for improvement.
Create a cheat sheet on correlation and hypothesis testing
Show steps
Solidify your understanding of correlation and hypothesis testing by creating a comprehensive cheat sheet that summarizes key concepts and formulas.
Show steps
Show steps
Show steps
- Gather your notes and resources on correlation and hypothesis testing.
- Organize and synthesize the information into a concise and visually appealing cheat sheet.
Begin a project using R to analyze real-world data
Show steps
Apply your skills in R to a practical project, solidifying your understanding and gaining hands-on experience with data analysis.
Show steps
Show steps
Show steps
- Identify a real-world dataset that aligns with your interests.
- Develop a project plan outlining your research question, analysis methods, and expected outcomes.
- Start working on the project, exploring the data, performing analysis, and interpreting the results.
Career center
Learners who complete Descriptive and Inferential Statistics in R will develop knowledge and skills
that may be useful to these careers:
Market Researcher
Market Researcher
A Market Researcher conducts research to understand consumer behavior and market trends. This course provides a solid foundation for this role by teaching you how to summarize descriptive statistics, calculate correlations, and perform hypothesis testing. These skills will enable you to effectively analyze market data, identify trends, and draw meaningful conclusions that can inform marketing strategies and drive business decisions.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical modeling to assess risk and make investment decisions. The concepts covered in this course, such as descriptive and inferential statistics, hypothesis testing, and correlation analysis, are fundamental to success in this role. By gaining a strong foundation in these areas, you will be able to develop and implement quantitative models, evaluate data, and make informed recommendations that can drive investment decisions.
Data Analyst
Data Analyst
A Data Analyst interprets and communicates data to help organizations make informed decisions. The skills you will gain in this course, including summarizing descriptive statistics, calculating correlations, and performing hypothesis testing, are essential for success in this role. By developing these skills, you will be able to effectively analyze data, identify trends, and draw meaningful conclusions that can drive decision-making.
Statistician
Statistician
A Statistician collects and analyzes data to make informed decisions. This course helps build a foundation for success in this role by providing a strong understanding of descriptive and inferential statistics. With this knowledge, you will be able to effectively analyze data, draw conclusions, and make recommendations based on evidence. Additionally, the course covers hypothesis testing, which is essential for evaluating the validity of claims and making informed decisions.
Actuary
Actuary
An Actuary assesses risk and uncertainty in the insurance and finance industries. The concepts covered in this course, such as descriptive and inferential statistics, hypothesis testing, and correlation analysis, are fundamental to success in this role. By gaining a strong foundation in these areas, you will be able to develop and implement actuarial models, evaluate risk, and make informed decisions that can help manage risk and ensure financial stability.
Financial Analyst
Financial Analyst
A Financial Analyst evaluates financial data to make investment recommendations. The knowledge gained in this course, including descriptive and inferential statistics, hypothesis testing, and correlation analysis, is highly relevant to this role. By developing these skills, you will be able to effectively analyze financial data, identify trends, and draw meaningful conclusions that can help you make informed investment decisions.
Epidemiologist
Epidemiologist
An Epidemiologist investigates the causes and distribution of diseases in populations. This course provides a solid foundation for this role by teaching you how to summarize descriptive statistics, calculate correlations, and perform hypothesis testing. These skills will enable you to effectively analyze epidemiological data, identify risk factors, and draw meaningful conclusions that can inform public health policies and interventions.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to medical research. The knowledge gained in this course, including descriptive and inferential statistics, hypothesis testing, and correlation analysis, is highly relevant to this role. By developing these skills, you will be able to effectively analyze medical data, identify patterns, and draw meaningful conclusions that can contribute to medical research and advancements.
Survey Researcher
Survey Researcher
A Survey Researcher designs and conducts surveys to collect data on various topics. The skills you will gain in this course, including summarizing descriptive statistics, calculating correlations, and performing hypothesis testing, are essential for success in this role. By developing these skills, you will be able to effectively design and implement surveys, analyze data, and draw meaningful conclusions that can inform decision-making in various fields.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses analytical techniques to solve complex problems in business and industry. This course provides a solid foundation for this role by teaching you how to summarize descriptive statistics, calculate correlations, and perform hypothesis testing. These skills will enable you to effectively analyze data, identify inefficiencies, and develop solutions that can improve operational efficiency and decision-making.
Risk Analyst
Risk Analyst
A Risk Analyst identifies and assesses risks in various fields, including finance, insurance, and healthcare. The knowledge gained in this course, including descriptive and inferential statistics, hypothesis testing, and correlation analysis, is highly relevant to this role. By developing these skills, you will be able to effectively analyze data, identify risks, and develop mitigation strategies that can help organizations manage risk and make informed decisions.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and systems to improve efficiency and effectiveness. This course may be useful for this role by providing a foundation in descriptive and inferential statistics. These skills can help you analyze data, identify trends, and make recommendations for process improvements.
Economist
Economist
An Economist studies the production, distribution, and consumption of goods and services. This course may be useful for this role by providing a foundation in descriptive and inferential statistics. These skills can help you analyze economic data, identify trends, and make predictions about economic behavior.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful for this role by providing a foundation in descriptive and inferential statistics. These skills can help you analyze data, identify trends, and make informed decisions about software design and development.
Data Scientist
Data Scientist
A Data Scientist uses statistical and computational methods to extract insights from data. This course may be useful for this role by providing a foundation in descriptive and inferential statistics. These skills can help you analyze data, identify patterns, and make informed decisions.
For more career information including salaries, visit:
OpenCourser.com/course/1tt0vw/descriptive
Reading list
We've selected 12 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
Descriptive and Inferential Statistics in R.
Provides a comprehensive introduction to statistical learning, with a focus on applications in R. It covers a wide range of topics, including descriptive statistics, hypothesis testing, regression, and classification.
Classic in the field of statistical learning, and it provides a more advanced treatment of the topics covered in the first book. It valuable resource for anyone who wants to learn more about the theory and practice of statistical learning.
Provides a comprehensive introduction to statistical inference, with a focus on the theory and methods used in hypothesis testing, confidence intervals, and regression analysis.
Provides a comprehensive overview of the field of statistics, covering a wide range of topics from descriptive statistics to Bayesian inference. It valuable resource for anyone who wants to learn more about the foundations of statistics.
Provides a clear and concise introduction to Bayesian data analysis, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of Bayesian methods in data analysis.
Provides a comprehensive introduction to Bayesian statistical modeling, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of Bayesian methods in data analysis.
Provides a comprehensive introduction to the mathematical foundations of machine learning, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of mathematics in machine learning.
Provides a comprehensive introduction to the field of machine learning, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of machine learning in data analysis.
Save
Provides a comprehensive introduction to the field of deep learning, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of deep learning in data analysis.
Provides a comprehensive introduction to the field of natural language processing, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of natural language processing in data analysis.
Provides a comprehensive introduction to the field of speech and language processing, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of speech and language processing in data analysis.
Provides a comprehensive introduction to the field of reinforcement learning, with a focus on the practical aspects of data analysis. It valuable resource for anyone who wants to learn more about the use of reinforcement learning in data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1tt0vw/descriptive
Share
Similar courses
Here are nine courses similar to
Descriptive and Inferential Statistics in R.
Serverless Event-based Application Architecture
OpenCourser.com/course/i6i8sg/serverless
Serverless Event-based Application Architecture
The Skills Economy: Moving Towards Skills based Organization
OpenCourser.com/course/n5qkqr/the
The Skills Economy: Moving Towards Skills based...
Utilizing Amazon Eventbridge in Serverless Applications
OpenCourser.com/course/2gfuu8/utilizing
Utilizing Amazon Eventbridge in Serverless Applications
Classroom Strategies for Inquiry-Based Learning
OpenCourser.com/course/go571v/classroom
Classroom Strategies for Inquiry-Based Learning
Introduction to Values-Based Leadership
OpenCourser.com/course/ketmbu/introduction
Introduction to Values-Based Leadership
Create a text adventure game with Ink
OpenCourser.com/course/bxu700/create
Create a text adventure game with Ink
Building an Ecosystem model with Insight Maker
OpenCourser.com/course/3hshuo/building
Building an Ecosystem model with Insight Maker
Create a Supermarket app using Java OOP
OpenCourser.com/course/0nnzyd/create
Create a Supermarket app using Java OOP
Building Similarity Based Recommendation System
OpenCourser.com/course/kmnwkz/building
Building Similarity Based Recommendation System
Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Emmanuel Segui
Language
English
Good to know
Introduces foundational concepts in descriptive and hypothesis testing
Ideal for learners seeking foundational skills in statistics using R
Includes hands-on project-based learning
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser