We may earn an affiliate commission when you visit our partners.
Dr. Nikunj Maheshwari
By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries.
Read more
By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries.
Read more
By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries.
- minimum value
- maximum value
- average value
- standard deviation
- total number of values
- missing values
- unique values
- data types
You will then learn how to record the statistical metrics for each column of a dataset using a custom function created by you in R. The output of the function will be a ready-to-use data quality report. Finally, you will learn how to export this report to an external file.
A data quality report can be used to identify outliers, missing values, data types, anomalies, etc. that are present in your dataset. This is the first step to understand your dataset and let you plan what pre-processing steps are required to make your dataset ready for analysis.
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/071oh3/using
Three deals to help you save
We found three 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.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
Using descriptive statistics to analyze data in R
By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries: minimum value, maximum value, average value, standard deviation, total number of values, missing values, unique values, and data types. You will then learn how to record the statistical metrics for each column of a dataset using a custom function created by you in R. The output of the function will be a ready-to-use data quality report. Finally, you will learn how to export this report to an external file.A data quality report can be used to identify outliers, missing values, data types, anomalies, etc. that are present in your dataset. This is the first step to understand your dataset and let you plan what pre-processing steps are required to make your dataset ready for analysis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores descriptive statistics, which is standard in industry data analysis
Introduces essential concepts in data quality, including outliers, missing values, and data types
Provides hands-on experience in calculating descriptive statistics using R functions
Develops a custom function for generating data quality reports, enhancing practical skills
Teaches industry-relevant data analysis methods for data preprocessing and understanding
Enhances learners' understanding of data and prepares them for further analysis
Save this course
Save Using Descriptive Statistics to Analyze Data in R to your list so you can find it easily later:
Save
Save
Reviews summary
Great for beginners in r
Learners say this course is a great introduction to descriptive statistics and a good review for students with a background in R programming. Students appreciate the concise lectures, engaging assignments, and real-world data examples. However, some learners say the material becomes challenging toward the end.
Uses real-world data examples.
"The real life data set examples in this course make it very engaging."
Good for students who are new to statistics.
"The material for this course is good for a beginner in statistics."
"It quickly enables you to progress from simple concepts like mean, median etc to complex topics like sampling distribution."
Some learners found the material challenging.
"It was a bit challenging to follow towards the end."
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 Using Descriptive Statistics to Analyze Data in R with these
activities:
Organize and review course materials
Show steps
Organize your notes, assignments, quizzes, and exams to improve your understanding of the core concepts and techniques covered in the course.
Browse courses on
Data Analysis
Show steps
-
Gather your course materials
-
Organize the materials into logical sections
-
Review the materials to reinforce your understanding
Practice data manipulation in R
Show steps
Complete coding exercises and drills to enhance your proficiency in data manipulation using R, which will be essential for your data analysis tasks.
Browse courses on
Data Manipulation
Show steps
-
Find practice exercises and drills
-
Solve the exercises and drills
-
Review your solutions and identify areas for improvement
Practice descriptive statistical calculations
Show steps
Practice calculating descriptive statistical metrics to reinforce your understanding and familiarity with these concepts.
Browse courses on
Descriptive Statistics
Show steps
-
Load a dataset into R
-
Calculate the minimum and maximum values
-
Calculate the average value
-
Calculate the standard deviation
-
Calculate the total number of values
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore the R Data Manipulation Cookbook
Show steps
Read and practice examples from the R Data Manipulation Cookbook to enhance your proficiency with essential data manipulation tasks in R.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Acquire a copy of the R Data Manipulation Cookbook
-
Read through the chapters and examples
-
Practice the examples provided in the book
Learn about data quality
Show steps
Explore tutorials on data quality to gain a deeper understanding of the importance of data quality and how to identify and address issues.
Browse courses on
Data Quality
Show steps
-
Find tutorials on data quality
-
Follow the tutorials to learn about data quality concepts
-
Apply the concepts to practice datasets
Participate in peer study groups
Show steps
Collaborate with your peers to discuss course concepts, share knowledge, and work on practice problems together, deepening your understanding and improving your problem-solving skills.
Browse courses on
Data Analysis
Show steps
-
Find or create a study group
-
Meet regularly to discuss course topics
-
Work together on practice problems and assignments
Create a data quality report
Show steps
Create a data quality report for a practice dataset to demonstrate your understanding of data quality and your ability to identify and document data issues.
Show steps
-
Load a dataset into R
-
Calculate descriptive statistical metrics
-
Identify and document data quality issues
-
Export the data quality report to CSV
Attend an R workshop
Show steps
Attend a workshop focused on R programming to gain practical hands-on experience and learn from experts in the field.
Browse courses on
Data Analysis
Show steps
-
Find an R workshop that aligns with your interests
-
Register for the workshop
-
Attend the workshop and actively participate
Organize and review course materials
Show steps
Organize your notes, assignments, quizzes, and exams to improve your understanding of the core concepts and techniques covered in the course.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Gather your course materials
- Organize the materials into logical sections
- Review the materials to reinforce your understanding
Practice data manipulation in R
Show steps
Complete coding exercises and drills to enhance your proficiency in data manipulation using R, which will be essential for your data analysis tasks.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Find practice exercises and drills
- Solve the exercises and drills
- Review your solutions and identify areas for improvement
Practice descriptive statistical calculations
Show steps
Practice calculating descriptive statistical metrics to reinforce your understanding and familiarity with these concepts.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Load a dataset into R
- Calculate the minimum and maximum values
- Calculate the average value
- Calculate the standard deviation
- Calculate the total number of values
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore the R Data Manipulation Cookbook
Show steps
Read and practice examples from the R Data Manipulation Cookbook to enhance your proficiency with essential data manipulation tasks in R.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of the R Data Manipulation Cookbook
- Read through the chapters and examples
- Practice the examples provided in the book
Learn about data quality
Show steps
Explore tutorials on data quality to gain a deeper understanding of the importance of data quality and how to identify and address issues.
Browse courses on
Data Quality
Show steps
Show steps
Show steps
- Find tutorials on data quality
- Follow the tutorials to learn about data quality concepts
- Apply the concepts to practice datasets
Participate in peer study groups
Show steps
Collaborate with your peers to discuss course concepts, share knowledge, and work on practice problems together, deepening your understanding and improving your problem-solving skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find or create a study group
- Meet regularly to discuss course topics
- Work together on practice problems and assignments
Create a data quality report
Show steps
Create a data quality report for a practice dataset to demonstrate your understanding of data quality and your ability to identify and document data issues.
Show steps
Show steps
Show steps
- Load a dataset into R
- Calculate descriptive statistical metrics
- Identify and document data quality issues
- Export the data quality report to CSV
Attend an R workshop
Show steps
Attend a workshop focused on R programming to gain practical hands-on experience and learn from experts in the field.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find an R workshop that aligns with your interests
- Register for the workshop
- Attend the workshop and actively participate
Career center
Learners who complete Using Descriptive Statistics to Analyze Data in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data analysts focus on finding meaningful patterns and insights from raw data. With a solid understanding of descriptive statistics, they can effectively analyze and interpret large datasets. This course provides a strong foundation in descriptive statistics, making it an ideal choice for aspiring data analysts seeking to enhance their analytical skills.
Business Analyst
Business Analyst
Business analysts leverage data to understand business needs and improve operations. Descriptive statistics play a critical role in their ability to analyze and summarize complex data, identify trends, and make informed recommendations. This course equips business analysts with the statistical skills essential for success in their field.
Data Scientist
Data Scientist
Data scientists specialize in extracting knowledge and insights from data. Descriptive statistics provide a crucial foundation for data scientists to explore, analyze, and interpret large datasets. By taking this course, data scientists can enhance their understanding of descriptive statistics and its applications in real-world data analysis.
Market Researcher
Market Researcher
Market researchers analyze market trends and consumer behavior to inform business decisions. Descriptive statistics are essential for understanding customer demographics, preferences, and buying patterns. This course provides market researchers with the statistical skills necessary to conduct effective market research and gain valuable insights.
Financial Analyst
Financial Analyst
Financial analysts evaluate financial data to make investment recommendations and advise clients. Descriptive statistics play a key role in their ability to summarize financial data, identify trends, and assess risk. This course equips financial analysts with the statistical skills they need to succeed in their profession.
Statistician
Statistician
Statisticians design, conduct, and analyze statistical surveys and experiments. This course may be useful for statisticians who wish to enhance their understanding of descriptive statistics and its applications in various research contexts.
Operations Research Analyst
Operations Research Analyst
Operations research analysts solve complex business problems using mathematical and statistical techniques. This course may be useful for operations research analysts who want to strengthen their foundation in descriptive statistics and apply it to optimization and decision-making processes.
Quality Control Manager
Quality Control Manager
Quality control managers ensure the quality of products or services. Descriptive statistics are essential for monitoring and evaluating quality, identifying defects, and implementing quality improvement initiatives. This course can provide quality control managers with the statistical tools they need to effectively manage quality control processes.
Health Data Analyst
Health Data Analyst
Health data analysts analyze healthcare data to improve patient outcomes and optimize healthcare systems. Descriptive statistics are crucial for understanding health data, identifying risk factors, and evaluating interventions. This course provides health data analysts with the skills to effectively analyze healthcare data and contribute to improved patient care.
Epidemiologist
Epidemiologist
Epidemiologists study the distribution and determinants of health-related states or events in populations. This course may be useful for epidemiologists who wish to enhance their understanding of descriptive statistics and its applications in epidemiological research.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty in insurance, finance, and other areas. This course may be useful for actuaries who wish to strengthen their understanding of descriptive statistics and how it is applied in actuarial models.
Data Entry Clerk
Data Entry Clerk
Data entry clerks are responsible for entering and maintaining data in computer systems. While descriptive statistics are not a core requirement for data entry clerks, an understanding of data types and data quality can enhance their ability to handle data effectively.
Customer Service Representative
Customer Service Representative
Customer service representatives provide support and assistance to customers. While descriptive statistics are not directly related to customer service, this course may be useful for customer service representatives who wish to gain a better understanding of data analysis and its applications in understanding customer needs and improving customer service.
Administrative Assistant
Administrative Assistant
Administrative assistants provide support to executives and other professionals. While descriptive statistics are not essential for administrative assistants, this course may be useful for those who wish to develop a better understanding of data analysis and its applications in supporting decision-making and improving administrative processes.
Receptionist
Receptionist
Receptionists greet visitors and provide general administrative support in offices. While descriptive statistics are not a requirement for receptionists, this course may be useful for those who wish to gain a foundation in data analysis and understand how data can be used to improve office operations and customer interactions.
For more career information including salaries, visit:
OpenCourser.com/course/071oh3/using
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
Using Descriptive Statistics to Analyze Data in R.
Save
Provides a comprehensive introduction to the R programming language, with a focus on data science. It covers topics such as data manipulation, visualization, and statistical modeling.
Save
Provides a practical guide to using R for data analysis. It covers topics such as data import, cleaning, and transformation, as well as statistical modeling and visualization.
Provides an introduction to statistics, with a focus on using R. It covers topics such as descriptive statistics, probability, distributions, and hypothesis testing.
Provides a comprehensive introduction to statistical methods for psychology. It covers topics such as descriptive statistics, probability, distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to statistical learning. It covers topics such as supervised learning, unsupervised learning, and model selection.
Provides a practical introduction to machine learning with R. It covers topics such as supervised learning, unsupervised learning, and model evaluation.
Save
Provides a practical guide to predictive modeling. It covers topics such as data preparation, model selection, and model evaluation.
Provides a comprehensive introduction to statistical inference. It covers topics such as probability, distributions, hypothesis testing, and confidence intervals.
Provides a comprehensive introduction to Bayesian data analysis. It covers topics such as Bayesian probability, Bayesian inference, and Bayesian modeling.
Provides a comprehensive introduction to causal inference. It covers topics such as causal graphs, counterfactuals, and causal models.
Save
Provides a comprehensive introduction to missing data. It covers topics such as missing data mechanisms, missing data imputation, and missing data analysis.
Save
Provides a comprehensive introduction to data visualization. It covers topics such as visual encodings, data transformations, and interactive visualizations.
Provides a comprehensive introduction to ggplot2, a popular R package for data visualization. It covers topics such as ggplot2 grammar, geoms, and themes.
Save
Provides a collection of recipes for common R tasks. It covers topics such as data manipulation, visualization, and statistical modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/071oh3/using
Share
Similar courses
Here are nine courses similar to
Using Descriptive Statistics to Analyze Data in R.
Coping with Missing, Invalid, and Duplicate Data in R
OpenCourser.com/course/txqm5b/coping
Coping with Missing, Invalid, and Duplicate Data in R
Most relevant
Handling Missing Values in R using tidyr
OpenCourser.com/course/jsfzz2/handling
Handling Missing Values in R using tidyr
Most relevant
Inferenzstatistik
OpenCourser.com/course/jq6z30/inferenzstatisti
Inferenzstatistik
Inferential Statistics
OpenCourser.com/course/1eb7on/inferential
Inferential Statistics
R Programming and Tidyverse Capstone Project
OpenCourser.com/course/5l64ku/r
R Programming and Tidyverse Capstone Project
Implementing Policy for Missing Values in Python
OpenCourser.com/course/4mwgdk/implementing
Implementing Policy for Missing Values in Python
Building and analyzing linear regression model in R
OpenCourser.com/course/21owyx/building
Building and analyzing linear regression model in R
Importing Data from Relational Databases in R 3
OpenCourser.com/course/fpokjx/importing
Importing Data from Relational Databases in R 3
Exploring Data with Quantitative Techniques Using R
OpenCourser.com/course/2dxi3z/exploring
Exploring Data with Quantitative Techniques Using R
Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Dr. Nikunj Maheshwari
Language
English
Good to know
Explores descriptive statistics, which is standard in industry data analysis
Introduces essential concepts in data quality, including outliers, missing values, and data types
Provides hands-on experience in calculating descriptive statistics using R functions
Develops a custom function for generating data quality reports, enhancing practical skills
Teaches industry-relevant data analysis methods for data preprocessing and understanding
Enhances learners' understanding of data and prepares them for further analysis
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