Using Descriptive Statistics to Analyze Data in R from Coursera

- 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.

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

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

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

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Organize your notes, assignments, quizzes, and exams to improve your understanding of the core concepts and techniques covered in the course.

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Gather your course materials
Organize the materials into logical sections
Review the materials to reinforce your understanding

Practice data manipulation in R

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Complete coding exercises and drills to enhance your proficiency in data manipulation using R, which will be essential for your data analysis tasks.

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Find practice exercises and drills
Solve the exercises and drills
Review your solutions and identify areas for improvement

Practice descriptive statistical calculations

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Practice calculating descriptive statistical metrics to reinforce your understanding and familiarity with these concepts.

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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

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Explore the R Data Manipulation Cookbook

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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

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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

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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

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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

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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.

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Find or create a study group
Meet regularly to discuss course topics
Work together on practice problems and assignments

Create a data quality report

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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.

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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

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Attend a workshop focused on R programming to gain practical hands-on experience and learn from experts in the field.

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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 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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Quality Control Manager

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.

See salaries and explore the career path for Health Data Analyst

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Data Entry Clerk

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.

See salaries and explore the career path for Customer Service Representative

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.

See salaries and explore the career path for Administrative Assistant

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.

See salaries and explore the career path for Receptionist