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Arimoro Olayinka Imisioluwa
Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables. By extension, you will learn how to perform univariate and bivariate statistics for univariate and bivariate variables in R.
Read more
Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables. By extension, you will learn how to perform univariate and bivariate statistics for univariate and bivariate variables in R.
Read more
Welcome to this 2-hour long project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R. You will also learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables. By extension, you will learn how to perform univariate and bivariate statistics for univariate and bivariate variables in R.
Note: You do not need to be a Data Scientist to be successful in this guided project, just a familiarity with basic statistics and using R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided project titled “Getting Started with R”.
Register for this course and see more details by visiting:
OpenCourser.com/course/l6354u/calculating
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What's inside
Syllabus
Project Overview
Welcome to this project-based course Calculating Descriptive Statistics in R. In this project, you will learn how to perform extensive descriptive statistics on both quantitative and qualitative variables in R.By the end of this 2-hour long project, you will understand how to calculate basic descriptive statistics in R. Also, you will learn how to calculate the frequency and percentage of categorical variables and check the distribution of quantitative variables. By extension, you will learn how to perform univariate and bivariate statistics for univariate and bivariate variables in R. Note, you do not need to be a data scientist to be successful in this guided project, just a familiarity with basic statistics and using R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided project titled “Getting Started with R”.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Instructors are recognized for work in x
Suitable for beginners with basic statistics and R familiarity
Develops skills useful for personal growth and development
Covers quantitative and qualitative variables
Teaches univariate and bivariate statistics
Builds a strong foundation for beginners
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Reviews summary
Positive reviews for statistical analysis in r
learners say the Calculating Descriptive Statistics in R course is excellent for beginners and covers the basics of statistical analysis with simple explanations. They give high praise to the knowledgeable instructor and say that the guided course is very helpful for understanding R and performing statistical functions. The comprehensive course offers a lot to learn and can take longer than the listed 2 hours. Overall, learners highly recommend this engaging course that provides a solid foundation in statistical analysis using R and is well worth the effort.
Course provides a solid foundation in statistical analysis.
"The guided course on R is quite helpful for a beginner to grasp the working of R and performing different statistical functions on a dataset."
"Learning this course provided an basic insight of the use of R in data analysis."
Simple explanations make it easy to understand.
"covers the basics with simple explanations"
"The guide provides lucid instructions and explains each functions elaborately with examples."
Instructor is knowledgeable and engaging.
"Fantastic instructor"
"Instructor is quite knowledgeable and engaging."
"Great instructor."
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 Calculating Descriptive Statistics in R with these
activities:
Organize and review your course materials
Show steps
Stay organized and up-to-date with the course material by creating a comprehensive compilation.
Show steps
-
Create a dedicated folder or notebook for the course.
-
Organize your notes, assignments, quizzes, and exams into the folder or notebook.
-
Review your materials regularly to reinforce your understanding and identify areas for improvement.
Follow a guided tutorial on calculating descriptive statistics in R
Show steps
Get hands-on experience with R by following a guided tutorial on how to calculate descriptive statistics.
Show steps
-
Find a reputable online tutorial or video on calculating descriptive statistics in R.
-
Follow the steps in the tutorial to calculate descriptive statistics on a dataset.
-
Experiment with different functions and options to customize your analysis.
Complete practice exercises on calculating descriptive statistics in R
Show steps
Solidify your understanding of descriptive statistics in R by completing practice exercises.
Show steps
-
Find practice exercises online or in a textbook.
-
Solve the exercises to test your skills.
-
Review your answers and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Join a study group and discuss descriptive statistics concepts
Show steps
Enhance your understanding of descriptive statistics by discussing the concepts with peers.
Show steps
-
Form or join a study group with other students taking this course.
-
Meet regularly to discuss course material, share insights, and work on assignments together.
-
Prepare for each session by reviewing the relevant material and identifying specific questions or topics to discuss.
Read a book on descriptive statistics in R
Show steps
Expand your knowledge of descriptive statistics in R by reading a comprehensive book on the subject.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Purchase or borrow a copy of the book.
-
Read the chapters on descriptive statistics.
-
Work through the examples and exercises to reinforce your understanding.
Start a project that involves analyzing and interpreting descriptive statistics
Show steps
Apply your knowledge of descriptive statistics to a real-world project.
Show steps
-
Identify a topic or question that you are interested in exploring.
-
Gather data from reliable sources.
-
Calculate descriptive statistics on the data and analyze the results.
-
Interpret the statistics and draw conclusions based on your analysis.
-
Write a report or presentation to share your findings.
Organize and review your course materials
Show steps
Stay organized and up-to-date with the course material by creating a comprehensive compilation.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for the course.
- Organize your notes, assignments, quizzes, and exams into the folder or notebook.
- Review your materials regularly to reinforce your understanding and identify areas for improvement.
Follow a guided tutorial on calculating descriptive statistics in R
Show steps
Get hands-on experience with R by following a guided tutorial on how to calculate descriptive statistics.
Show steps
Show steps
Show steps
- Find a reputable online tutorial or video on calculating descriptive statistics in R.
- Follow the steps in the tutorial to calculate descriptive statistics on a dataset.
- Experiment with different functions and options to customize your analysis.
Complete practice exercises on calculating descriptive statistics in R
Show steps
Solidify your understanding of descriptive statistics in R by completing practice exercises.
Show steps
Show steps
Show steps
- Find practice exercises online or in a textbook.
- Solve the exercises to test your skills.
- Review your answers and identify areas for improvement.
Three other activities
Expand to see all activities and additional details
Show all six activities
Join a study group and discuss descriptive statistics concepts
Show steps
Enhance your understanding of descriptive statistics by discussing the concepts with peers.
Show steps
Show steps
Show steps
- Form or join a study group with other students taking this course.
- Meet regularly to discuss course material, share insights, and work on assignments together.
- Prepare for each session by reviewing the relevant material and identifying specific questions or topics to discuss.
Read a book on descriptive statistics in R
Show steps
Expand your knowledge of descriptive statistics in R by reading a comprehensive book on the subject.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of the book.
- Read the chapters on descriptive statistics.
- Work through the examples and exercises to reinforce your understanding.
Start a project that involves analyzing and interpreting descriptive statistics
Show steps
Apply your knowledge of descriptive statistics to a real-world project.
Show steps
Show steps
Show steps
- Identify a topic or question that you are interested in exploring.
- Gather data from reliable sources.
- Calculate descriptive statistics on the data and analyze the results.
- Interpret the statistics and draw conclusions based on your analysis.
- Write a report or presentation to share your findings.
Career center
Learners who complete Calculating Descriptive Statistics in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a crucial role in analyzing and interpreting data to gain insights and inform decision-making. The Calculating Descriptive Statistics in R course provides a solid foundation for Data Analysts by equipping them with the skills to perform extensive descriptive statistics on both quantitative and qualitative variables. This course teaches the calculation of frequency and percentage of categorical variables, checking the distribution of quantitative variables, and performing univariate and bivariate statistics. These skills are essential for Data Analysts to effectively summarize and analyze data, identify patterns and trends, and draw meaningful conclusions.
Data Scientist
Data Scientist
Data Scientists leverage advanced statistical techniques to extract knowledge from data. The Calculating Descriptive Statistics in R course provides a valuable foundation for Data Scientists by introducing them to the basics of descriptive statistics in R. By mastering these concepts, Data Scientists can effectively explore, analyze, and summarize data, which is crucial for building predictive models, identifying patterns and insights, and making data-driven decisions.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to understand market trends, consumer behavior, and industry dynamics. The Calculating Descriptive Statistics in R course can enhance the skills of Market Researchers by providing them with the ability to perform comprehensive descriptive statistics on both quantitative and qualitative data. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Market Researchers to effectively analyze survey data, customer feedback, and market trends, enabling them to provide valuable insights and inform marketing strategies.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to assess financial risks and make investment decisions. The Calculating Descriptive Statistics in R course provides a strong foundation for Quantitative Analysts by introducing them to the basics of descriptive statistics in R. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Quantitative Analysts to effectively analyze financial data, identify patterns and trends, and develop quantitative models for risk assessment and investment决策.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present statistical data. The Calculating Descriptive Statistics in R course provides a strong foundation for Statisticians by introducing them to the basics of descriptive statistics in R. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Statisticians to effectively analyze data, draw meaningful conclusions, and communicate statistical findings in various fields.
Business Analyst
Business Analyst
Business Analysts use data analysis techniques to solve business problems and improve decision-making. The Calculating Descriptive Statistics in R course can enhance the skills of Business Analysts by providing them with the ability to perform descriptive statistics on both quantitative and qualitative data. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills are essential for Business Analysts to effectively analyze business data, identify trends and patterns, and develop data-driven solutions to improve business outcomes.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data architectures and systems. The Calculating Descriptive Statistics in R course may be helpful for Data Engineers who want to enhance their understanding of descriptive statistics and its applications in data management. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Data Engineers in designing data pipelines, optimizing data storage and retrieval, and ensuring data quality.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. The Calculating Descriptive Statistics in R course may be helpful for Machine Learning Engineers who want to strengthen their understanding of descriptive statistics and its role in machine learning. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Machine Learning Engineers in data preprocessing, feature engineering, and model evaluation.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The Calculating Descriptive Statistics in R course may be helpful for Software Engineers who want to enhance their understanding of descriptive statistics and its applications in software development. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Software Engineers in data analysis, debugging, and performance optimization.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments and financial products. The Calculating Descriptive Statistics in R course may be helpful for Financial Analysts who want to strengthen their understanding of descriptive statistics and its applications in financial analysis. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Financial Analysts in analyzing financial data, assessing risk and return, and making investment decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess and manage risk in insurance and finance. The Calculating Descriptive Statistics in R course may be helpful for Actuaries who want to enhance their understanding of descriptive statistics and its applications in actuarial science. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Actuaries in risk assessment, premium pricing, and financial modeling.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to improve the efficiency and effectiveness of operations and processes. The Calculating Descriptive Statistics in R course may be helpful for Operations Research Analysts who want to strengthen their understanding of descriptive statistics and its applications in operations research. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Operations Research Analysts in data analysis, optimization modeling, and decision-making.
Epidemiologist
Epidemiologist
Epidemiologists investigate the distribution and causes of diseases and health conditions in populations. The Calculating Descriptive Statistics in R course may be helpful for Epidemiologists who want to enhance their understanding of descriptive statistics and its applications in epidemiology. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Epidemiologists in data analysis, outbreak investigation, and disease surveillance.
Biostatistician
Biostatistician
Biostatisticians apply statistical principles and methods to the design, analysis, and interpretation of biomedical research studies. The Calculating Descriptive Statistics in R course may be helpful for Biostatisticians who want to strengthen their understanding of descriptive statistics and its applications in biostatistics. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Biostatisticians in clinical trial design, data analysis, and medical research.
Survey Researcher
Survey Researcher
Survey Researchers design, conduct, and analyze surveys to collect data on a wide range of topics. The Calculating Descriptive Statistics in R course may be helpful for Survey Researchers who want to enhance their understanding of descriptive statistics and its applications in survey research. This course covers techniques to calculate the frequency and percentage of categorical variables, check the distribution of quantitative variables, and conduct univariate and bivariate statistics. These skills can be useful for Survey Researchers in data analysis, report writing, and survey design.
For more career information including salaries, visit:
OpenCourser.com/course/l6354u/calculating
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
Calculating Descriptive Statistics in R.
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers topics such as data manipulation, visualization, and statistical modeling, making it an excellent reference for those who want to learn more about R and its capabilities.
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science applications. It covers topics such as data manipulation, visualization, and statistical modeling, making it an excellent reference for those who want to learn more about R and its capabilities.
Introduces statistical inference through the lens of data science, using R and the tidyverse. It covers topics such as probability, hypothesis testing, regression, and Bayesian analysis, making it a great resource for those who want to learn more about statistical inference in a modern context.
Provides a comprehensive overview of the R programming language, covering topics such as data manipulation, visualization, and statistical modeling. It valuable resource for those who want to learn more about the advanced features of R and how to use them effectively.
Save
Comprehensive reference for the R programming language, covering topics such as data manipulation, visualization, and statistical modeling. It valuable resource for those who want to learn more about the advanced features of R and how to use them effectively.
Provides a modern introduction to statistics, using R for data analysis. It covers topics such as probability, hypothesis testing, regression, and Bayesian analysis, making it a great resource for those who want to learn more about statistics in a modern context.
Provides a modern introduction to statistics, using R for data analysis. It covers topics such as probability, hypothesis testing, regression, and Bayesian analysis, making it a great resource for those who want to learn more about statistics in a modern context.
Provides a comprehensive introduction to the ggplot2 package for creating graphics in R. It covers topics such as data visualization, aesthetics, and themes. It valuable resource for those who want to learn more about how to create beautiful and informative graphics in R.
Provides a comprehensive overview of data manipulation in R, covering topics such as data import, cleaning, and transformation. It valuable resource for those who want to learn more about how to work with data in R.
Save
Provides a comprehensive introduction to the R Markdown format for creating dynamic reports and documents. It covers topics such as syntax, output formats, and extensions. It valuable resource for those who want to learn more about how to create beautiful and informative reports in R.
Save
Provides a collection of recipes for creating various types of graphs in R, covering topics such as bar charts, line charts, and scatterplots. It valuable resource for those who want to learn more about how to create beautiful and informative graphics in R.
Save
Provides a gentle introduction to the R programming language, with a focus on data analysis and visualization. It great resource for those who are new to R and want to learn the basics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l6354u/calculating
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Good to know
Instructors are recognized for work in x
Suitable for beginners with basic statistics and R familiarity
Develops skills useful for personal growth and development
Covers quantitative and qualitative variables
Teaches univariate and bivariate statistics
Builds a strong foundation for beginners
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