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Shalini Gopalkrishnan
In this 1-hour long project-based course, we will show you how to do basic descriptives using RCmdr .You will learn about measures of central tendency and dispersion. This project uses data about cereals that you eat and details about their sugar, fiber calorie content.
Read more
In this 1-hour long project-based course, we will show you how to do basic descriptives using RCmdr .You will learn about measures of central tendency and dispersion. This project uses data about cereals that you eat and details about their sugar, fiber calorie content.
Read more
In this 1-hour long project-based course, we will show you how to do basic descriptives using RCmdr .You will learn about measures of central tendency and dispersion. This project uses data about cereals that you eat and details about their sugar, fiber calorie content.
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/5shptu/basic
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What's inside
Syllabus
Basic Descriptive statistics using Rcmdr
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Increases learners' descriptive statistics knowledge, which is foundational for most applications of data science and machine learning
Taught by Shalini Gopalkrishnan, who is recognized for their work in data science and statistics
Builds a strong foundation for beginners in descriptive statistics and data analysis
Uses a well-established data analysis tool (Rcmdr) that is widely used in industry
Focuses on practical applications of descriptive statistics using a real-world dataset
May not cover advanced topics in descriptive statistics that are needed for specialized applications (e.g., ANOVA, multivariate analysis)
Save this course
Save Basic Descriptives using R Cmdr to your list so you can find it easily later:
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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 Basic Descriptives using R Cmdr with these
activities:
Review concepts of probability and data distribution
Show steps
Review concepts of probability and data distribution to strengthen foundational knowledge for further topics in the course.
Browse courses on
Probability
Show steps
-
Recall basic concepts of probability (e.g., events, outcomes).
-
Review different types of data distributions (e.g., normal distribution).
-
Practice solving problems involving probability and distributions.
Review basic descriptive statistics
Show steps
Review basic descriptive statistics measures such as mean, median, mode, range, and standard deviation to strengthen foundational knowledge for this course.
Browse courses on
Descriptive Statistics
Show steps
-
Review online tutorials on basic descriptive statistics.
-
Complete practice problems on calculating descriptive statistics.
Organize and review key concepts
Show steps
Organize and review course notes, assignments, and other materials to strengthen understanding of key concepts and reinforce learning.
Show steps
-
Review lecture notes and identify main concepts.
-
Organize notes into a logical structure.
-
Summarize key concepts in your own words.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore online resources for descriptive statistics
Show steps
Explore online resources, such as tutorials, articles, and videos, to supplement understanding of descriptive statistics concepts.
Show steps
-
Search for online resources on descriptive statistics.
-
Review and take notes on the content.
-
Apply the knowledge gained to practice exercises.
Calculate descriptive statistics using R
Show steps
Practice using Rcmdr to calculate descriptive statistics on different datasets, reinforcing the concepts learned in the course.
Browse courses on
RCmdr
Show steps
-
Load a dataset into Rcmdr.
-
Calculate measures of central tendency (mean, median, mode) using Rcmdr functions.
-
Calculate measures of dispersion (range, standard deviation, variance) using Rcmdr functions.
Solve practice problems on measures of dispersion
Show steps
Complete practice problems to improve understanding and proficiency in calculating and interpreting measures of dispersion.
Browse courses on
Measures of Dispersion
Show steps
-
Review the concepts of range, standard deviation, and variance.
-
Solve practice problems involving the calculation of these measures.
-
Interpret the results and draw meaningful conclusions.
Participate in a study group or discussion forum
Show steps
Engage with peers in discussions or study groups to clarify concepts, exchange perspectives, and reinforce learning.
Show steps
-
Find a study group or discussion forum relevant to the course.
-
Participate actively in discussions.
-
Ask questions, share insights, and engage with others.
Create a visual representation of descriptive statistics
Show steps
Create a visual representation (e.g., infographic, chart, graph) of descriptive statistics from a given dataset to enhance understanding and retention of the concepts.
Browse courses on
Descriptive Statistics
Show steps
-
Choose a relevant dataset.
-
Calculate descriptive statistics for the dataset.
-
Select an appropriate visual format (e.g., bar chart, scatter plot).
-
Create a visually appealing and informative representation of the data.
Develop a descriptive statistics project
Show steps
Design and execute a project that involves applying descriptive statistics techniques to analyze a dataset and draw meaningful conclusions.
Browse courses on
Descriptive Statistics
Show steps
-
Identify a relevant dataset and research question.
-
Apply descriptive statistics techniques to analyze the data.
-
Interpret the results and draw conclusions.
-
Present the findings in a clear and concise manner.
Review concepts of probability and data distribution
Show steps
Review concepts of probability and data distribution to strengthen foundational knowledge for further topics in the course.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Recall basic concepts of probability (e.g., events, outcomes).
- Review different types of data distributions (e.g., normal distribution).
- Practice solving problems involving probability and distributions.
Review basic descriptive statistics
Show steps
Review basic descriptive statistics measures such as mean, median, mode, range, and standard deviation to strengthen foundational knowledge for this course.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Review online tutorials on basic descriptive statistics.
- Complete practice problems on calculating descriptive statistics.
Organize and review key concepts
Show steps
Organize and review course notes, assignments, and other materials to strengthen understanding of key concepts and reinforce learning.
Show steps
Show steps
Show steps
- Review lecture notes and identify main concepts.
- Organize notes into a logical structure.
- Summarize key concepts in your own words.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Explore online resources for descriptive statistics
Show steps
Explore online resources, such as tutorials, articles, and videos, to supplement understanding of descriptive statistics concepts.
Show steps
Show steps
Show steps
- Search for online resources on descriptive statistics.
- Review and take notes on the content.
- Apply the knowledge gained to practice exercises.
Calculate descriptive statistics using R
Show steps
Practice using Rcmdr to calculate descriptive statistics on different datasets, reinforcing the concepts learned in the course.
Browse courses on
RCmdr
Show steps
Show steps
Show steps
- Load a dataset into Rcmdr.
- Calculate measures of central tendency (mean, median, mode) using Rcmdr functions.
- Calculate measures of dispersion (range, standard deviation, variance) using Rcmdr functions.
Solve practice problems on measures of dispersion
Show steps
Complete practice problems to improve understanding and proficiency in calculating and interpreting measures of dispersion.
Browse courses on
Measures of Dispersion
Show steps
Show steps
Show steps
- Review the concepts of range, standard deviation, and variance.
- Solve practice problems involving the calculation of these measures.
- Interpret the results and draw meaningful conclusions.
Participate in a study group or discussion forum
Show steps
Engage with peers in discussions or study groups to clarify concepts, exchange perspectives, and reinforce learning.
Show steps
Show steps
Show steps
- Find a study group or discussion forum relevant to the course.
- Participate actively in discussions.
- Ask questions, share insights, and engage with others.
Create a visual representation of descriptive statistics
Show steps
Create a visual representation (e.g., infographic, chart, graph) of descriptive statistics from a given dataset to enhance understanding and retention of the concepts.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Choose a relevant dataset.
- Calculate descriptive statistics for the dataset.
- Select an appropriate visual format (e.g., bar chart, scatter plot).
- Create a visually appealing and informative representation of the data.
Develop a descriptive statistics project
Show steps
Design and execute a project that involves applying descriptive statistics techniques to analyze a dataset and draw meaningful conclusions.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Identify a relevant dataset and research question.
- Apply descriptive statistics techniques to analyze the data.
- Interpret the results and draw conclusions.
- Present the findings in a clear and concise manner.
Career center
Learners who complete Basic Descriptives using R Cmdr will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use statistical software to analyze trends, create models, and make predictions. Their goal is to help organizations solve complex problems and make better decisions. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Data Analyst
Data Analyst
Data Analysts use statistical software to analyze trends, collect data, and present information. Their goal is to provide useful insights to business stakeholders so that they can make data-driven decisions. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Research Analyst
Research Analyst
Research Analysts use statistical software to collect, analyze, and interpret data. Their goal is to provide insights on market trends, customer behavior, and product performance to help businesses make informed decisions. This course will help you develop the skills necessary for this role by teaching you how to use RCmdr to perform basic descriptive analyses.
Statistician
Statistician
Statisticians use statistical software to collect, analyze, and interpret data. Their goal is to provide insights on trends, patterns, and relationships. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Marketing Analyst
Marketing Analyst
Marketing Analysts use statistical software to collect, analyze, and interpret data. Their goal is to help businesses understand their customers and develop effective marketing campaigns. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Business Analyst
Business Analyst
Business Analysts use statistical software to analyze trends, collect data, and present information. Their goal is to help businesses make informed decisions about their operations and strategies. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts use statistical software to collect, analyze, and interpret data. Their goal is to help ensure that products and services meet quality standards. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Risk Manager
Risk Manager
Risk Managers use statistical software to assess risk and uncertainty. Their goal is to help organizations identify and mitigate risks. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Database Administrator
Database Administrator
Database Administrators use statistical software to manage and maintain databases. Their goal is to help ensure that data is accurate, secure, and accessible. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Financial Analyst
Financial Analyst
Financial Analysts use statistical software to analyze trends, create models, and make predictions. Their goal is to help organizations make informed decisions about their investments and financial strategies. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical software to analyze trends, identify problems, and develop solutions. Their goal is to help organizations improve their efficiency and effectiveness. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Data Engineer
Data Engineer
Data Engineers use statistical software to design and develop data systems. Their goal is to help organizations collect, store, and analyze data. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Actuary
Actuary
Actuaries use statistical software to assess risk and uncertainty. Their goal is to help organizations make informed decisions about their insurance and financial strategies. This course will help prepare you for this role by providing you with a foundation in basic descriptive statistics using the RCmdr software.
Software Engineer
Software Engineer
Software Engineers use statistical software to develop and test software applications. Their goal is to help organizations create reliable and efficient software. This course may provide a useful foundation in basic descriptive statistics using the RCmdr software for this role.
Web Developer
Web Developer
Web Developers use statistical software to design and develop websites. Their goal is to help organizations create user-friendly and effective websites. This course may provide a useful foundation in basic descriptive statistics using the RCmdr software for this role.
For more career information including salaries, visit:
OpenCourser.com/course/5shptu/basic
Reading list
We've selected 15 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
Basic Descriptives using R Cmdr.
Provides a comprehensive introduction to probability and statistics, with a focus on using R for data analysis. It covers the basics of probability, statistical inference, and regression analysis, and includes many examples and exercises.
Save
Comprehensive guide to the R programming language. It covers all aspects of the language, from basic data manipulation to advanced statistical modeling. It valuable resource for both new and experienced R users.
Save
Practical guide to using R for data science. It covers all aspects of the data science process, from data acquisition and cleaning to data analysis and visualization. It valuable resource for both new and experienced data scientists.
Provides a modern introduction to statistical inference using R and the tidyverse. It covers the basics of probability, statistical modeling, and data visualization. It valuable resource for both new and experienced data scientists.
Comprehensive guide to data manipulation with R. It covers all aspects of data manipulation, from basic data cleaning to advanced data wrangling. It valuable resource for both new and experienced R users.
Save
Collection of recipes for creating different types of graphs in R. It covers a wide range of graph types, from basic line graphs to complex interactive graphs. It valuable resource for both new and experienced R users.
Save
Modern introduction to statistics using R. It covers the basics of probability, statistical modeling, and data visualization. It valuable resource for both new and experienced data scientists.
Comprehensive introduction to statistical methods for psychology. It covers all aspects of statistical analysis, from data collection to data interpretation. It valuable resource for both new and experienced researchers.
Classic introduction to statistical learning. It covers the basics of machine learning, including supervised learning, unsupervised learning, and model selection. It valuable resource for both new and experienced data scientists.
Practical guide to machine learning with R. It covers all aspects of the machine learning process, from data preparation to model evaluation. It valuable resource for both new and experienced data scientists.
Practical guide to deep learning with R. It covers all aspects of the deep learning process, from model building to model deployment. It valuable resource for both new and experienced data scientists.
Save
Comprehensive introduction to time series analysis with R. It covers all aspects of time series analysis, from data collection to model forecasting. It valuable resource for both new and experienced data scientists.
Comprehensive introduction to spatial data analysis with R. It covers all aspects of spatial data analysis, from data preprocessing to model fitting. It valuable resource for both new and experienced data scientists.
Practical guide to big data analytics with R. It covers all aspects of big data analytics, from data collection to data visualization. It valuable resource for both new and experienced data scientists.
Save
Comprehensive guide to R packages. It covers all aspects of R package development, from package creation to package distribution. It valuable resource for both new and experienced R package developers.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5shptu/basic
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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Shalini Gopalkrishnan
Language
English
Good to know
Increases learners' descriptive statistics knowledge, which is foundational for most applications of data science and machine learning
Taught by Shalini Gopalkrishnan, who is recognized for their work in data science and statistics
Builds a strong foundation for beginners in descriptive statistics and data analysis
Uses a well-established data analysis tool (Rcmdr) that is widely used in industry
Focuses on practical applications of descriptive statistics using a real-world dataset
May not cover advanced topics in descriptive statistics that are needed for specialized applications (e.g., ANOVA, multivariate analysis)
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