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Dr. Nikunj Maheshwari
By the end of this project, you will learn how to apply probability distributions to solve real world problems in R, a free, open-source program that you can download. You will learn how to answer real world problems using the following probability distributions – Binomial, Poisson, Normal, Exponential and Chi-square. You will also learn the various ways of visualizing these distributions of real world problems. By the end of this project, you will become confident in understanding commonly used probability distributions through solving practical problems and you will strengthen your core concepts of data distributions using R programming language.
Read more
By the end of this project, you will learn how to apply probability distributions to solve real world problems in R, a free, open-source program that you can download. You will learn how to answer real world problems using the following probability distributions – Binomial, Poisson, Normal, Exponential and Chi-square. You will also learn the various ways of visualizing these distributions of real world problems. By the end of this project, you will become confident in understanding commonly used probability distributions through solving practical problems and you will strengthen your core concepts of data distributions using R programming language.
Read more
By the end of this project, you will learn how to apply probability distributions to solve real world problems in R, a free, open-source program that you can download. You will learn how to answer real world problems using the following probability distributions – Binomial, Poisson, Normal, Exponential and Chi-square. You will also learn the various ways of visualizing these distributions of real world problems. By the end of this project, you will become confident in understanding commonly used probability distributions through solving practical problems and you will strengthen your core concepts of data distributions using R programming language.
These distributions are widely used in day-to-day life of statisticians for hypothesis testing and drawing conclusions on a population from a small sample. Additionally, in the field of data science, statistical inferences use probability distribution of data to analyze or predict trend from data.
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/gl490z/using
What's inside
Syllabus
Using probability distributions for real world problems in R
By the end of this project, you will learn how to apply probability distributions to solve real world problems in R, a free, open-source program that you can download. You will learn how to answer real world problems using the following probability distributions – Binomial, Poisson, Normal, Exponential and Chi-square. You will also learn the various ways of visualizing these distributions of real world problems. By the end of this project, you will become confident in understanding commonly used probability distributions through solving practical problems and you will strengthen your core concepts of data distributions using R programming language. These distributions are widely used in day-to-day life of statisticians for hypothesis testing and drawing conclusions on a population from a small sample. Additionally, in the field of data science, statistical inferences use probability distribution of data to analyze or predict trend from data.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners at the advanced beginner level
Imparts foundational knowledge of probability distributions
Provides practical examples to reinforce learning
Taught by Dr. Nikunj Maheshwari, an expert in probability distributions
Leverages R, a widely used open-source programming language for statistical analysis
May not be suitable for learners with no prior exposure to statistics or programming
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Reviews summary
Project-based probability in r
According to students, this course is well received and enriches student learning with excellent assignments that demonstrate probability distributions in R for real world problems.
Excellent project based assignments
"Excellent coursework project!"
"It was enriching, you learn in this project"
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 probability distributions for real world problems in R with these
activities:
Review your notes from previous math courses
Show steps
Reviewing your notes from previous math courses can help you refresh your knowledge of the mathematical concepts that are necessary for understanding probability distributions.
Browse courses on
Algebra
Show steps
-
Gather your notes from previous math courses.
-
Review the sections on algebra and calculus.
-
Focus on the concepts that are most relevant to probability distributions.
Watch video tutorials on probability distributions
Show steps
Watching video tutorials can help you learn the basics of probability distributions and how to apply them to real-world problems.
Show steps
-
Find a video tutorial on probability distributions.
-
Watch the tutorial.
-
Take notes on the key concepts.
Review the textbook for the course
Show steps
Reviewing the textbook for the course can help you learn the basics of probability distributions and how to apply them to real-world problems.
View
Probability & Statistics for Engineers &...
on Amazon
Show steps
-
Read the assigned chapters from the textbook.
-
Take notes on the key concepts.
-
Work through the practice problems at the end of the chapters.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Organize your notes, assignments, and other course materials
Show steps
Organizing your notes, assignments, and other course materials can help you stay on top of the material and improve your retention.
Show steps
-
Gather all of your notes, assignments, and other course materials.
-
Organize the materials into a logical order.
-
Create a system for keeping track of your materials.
Practice solving probability problems
Show steps
Solving practice problems will help you develop the skills you need to apply probability distributions to real-world problems.
Show steps
-
Find a set of practice problems online or in a textbook.
-
Work through the problems, step-by-step.
-
Check your answers against the solutions provided.
Create a cheat sheet on probability distributions
Show steps
Creating a cheat sheet will help you organize and remember the key concepts of probability distributions.
Show steps
-
Gather your notes and other resources on probability distributions.
-
Organize the information into a logical order.
-
Create a cheat sheet that includes the key concepts, formulas, and examples.
Join a study group to discuss probability distributions
Show steps
Discussing probability distributions with other students can help you understand the concepts more deeply and identify areas where you need additional support.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the course material.
-
Work together on practice problems and projects.
Contribute to an open-source project related to probability distributions
Show steps
Contributing to an open-source project related to probability distributions can help you apply your knowledge and skills to a real-world project.
Show steps
-
Find an open-source project related to probability distributions.
-
Read the project documentation.
-
Identify a way to contribute to the project.
-
Make your contribution to the project.
Review your notes from previous math courses
Show steps
Reviewing your notes from previous math courses can help you refresh your knowledge of the mathematical concepts that are necessary for understanding probability distributions.
Browse courses on
Algebra
Show steps
Show steps
Show steps
- Gather your notes from previous math courses.
- Review the sections on algebra and calculus.
- Focus on the concepts that are most relevant to probability distributions.
Watch video tutorials on probability distributions
Show steps
Watching video tutorials can help you learn the basics of probability distributions and how to apply them to real-world problems.
Show steps
Show steps
Show steps
- Find a video tutorial on probability distributions.
- Watch the tutorial.
- Take notes on the key concepts.
Review the textbook for the course
Show steps
Reviewing the textbook for the course can help you learn the basics of probability distributions and how to apply them to real-world problems.
View
Probability & Statistics for Engineers &...
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters from the textbook.
- Take notes on the key concepts.
- Work through the practice problems at the end of the chapters.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Organize your notes, assignments, and other course materials
Show steps
Organizing your notes, assignments, and other course materials can help you stay on top of the material and improve your retention.
Show steps
Show steps
Show steps
- Gather all of your notes, assignments, and other course materials.
- Organize the materials into a logical order.
- Create a system for keeping track of your materials.
Practice solving probability problems
Show steps
Solving practice problems will help you develop the skills you need to apply probability distributions to real-world problems.
Show steps
Show steps
Show steps
- Find a set of practice problems online or in a textbook.
- Work through the problems, step-by-step.
- Check your answers against the solutions provided.
Create a cheat sheet on probability distributions
Show steps
Creating a cheat sheet will help you organize and remember the key concepts of probability distributions.
Show steps
Show steps
Show steps
- Gather your notes and other resources on probability distributions.
- Organize the information into a logical order.
- Create a cheat sheet that includes the key concepts, formulas, and examples.
Join a study group to discuss probability distributions
Show steps
Discussing probability distributions with other students can help you understand the concepts more deeply and identify areas where you need additional support.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the course material.
- Work together on practice problems and projects.
Contribute to an open-source project related to probability distributions
Show steps
Contributing to an open-source project related to probability distributions can help you apply your knowledge and skills to a real-world project.
Show steps
Show steps
Show steps
- Find an open-source project related to probability distributions.
- Read the project documentation.
- Identify a way to contribute to the project.
- Make your contribution to the project.
Career center
Learners who complete Using probability distributions for real world problems in R will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists collect, store, and analyze data to extract meaningful insights and make predictions. Proficiency in advanced statistical tests is often required for this role. The **Using probability distributions for real world problems in R** course can help hone your knowledge of the Binomial, Poisson, Normal, Exponential and Chi-square distributions, all of which are commonly used by Data Scientists.
Research Analyst
Research Analyst
Research Analysts collect, analyze, and interpret data to provide insights for businesses. They often specialize in a particular industry or function, such as market research, financial analysis, or operations research. The **Using probability distributions for real world problems in R** course can help build foundational knowledge of probability and statistics that is necessary for this role.
Statistician
Statistician
Statisticians plan and conduct statistical experiments, analyze and interpret data, and develop new methodologies to understand phenomena. A deep understanding of probability distribution is necessary. The **Using probability distributions for real world problems in R** course can help build a foundation for the statistical methods used by Statisticians.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. More advanced roles may use statistical modeling to understand data. The **Using probability distributions for real world problems in R** course can provide a solid foundation in probability and statistics for working with data.
Market Researcher
Market Researcher
Market Researchers conduct surveys, focus groups, and other research to understand consumer behavior. They use this information to help businesses develop new products and marketing campaigns. A foundational knowledge of probability and statistics is helpful for survey design and data analysis. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in market research.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts develop and implement mathematical models to analyze financial data. They use these models to make investment decisions or to assess risk. This role may require an advanced degree and strong math and programming skills. Knowledge of probability is required. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions relevant to finance.
Actuary
Actuary
Actuaries analyze and assess risk in the insurance and finance industries. They use mathematical and statistical models to determine the probability of future events. This role requires passing a series of exams and typically requires an advanced degree. The **Using probability distributions for real world problems in R** course may be useful for strengthening foundational knowledge of probability distributions.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks to an organization. They use a variety of tools and techniques to analyze risk, including probability distributions. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in risk management.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. They use statistical methods to analyze data and identify risk factors. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in epidemiology.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. They design and analyze studies, and interpret data to draw conclusions. A strong foundation in probability and statistics is required. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in biostatistics.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts develop and apply mathematical models to solve problems in business and industry. They use these models to improve efficiency and productivity. Knowledge of probability and statistics is helpful for building mathematical models. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in operations research.
Business Analyst
Business Analyst
Business Analysts identify and solve problems within an organization. They use a variety of tools and techniques to analyze data, including probability distributions. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in business analysis.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and recommend investments. They use a variety of tools and techniques to analyze financial data, including probability distributions. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in financial analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. While probability and statistics is not a core requirement, some roles may involve data analysis or modeling. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in software development.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. While probability and statistics is not a core requirement, some roles may involve data analysis or modeling. The **Using probability distributions for real world problems in R** course may be useful for learning about probability distributions used in data engineering.
For more career information including salaries, visit:
OpenCourser.com/course/gl490z/using
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
Using probability distributions for real world problems in R.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in engineering and science. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a rigorous introduction to mathematical statistics, with a focus on applications in various fields. It covers a wide range of topics, including probability theory, statistical inference, and regression analysis.
Save
Provides a concise introduction to probability and statistics, with a focus on applications in engineering and science. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to probability, statistics, and random processes, with a focus on applications in engineering. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a concise introduction to probability and statistics, with a focus on applications in engineering. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in computer science. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a gentle introduction to probability and statistics, with a focus on applications in everyday life. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to statistics, with a focus on applications in research. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to probability and mathematical statistics, with a focus on applications in various fields. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to probability and stochastics, with a focus on applications in various fields. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to probability and random variables, with a focus on applications in various fields. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
Provides a comprehensive introduction to probability and statistics, with a focus on applications in applied science. It covers a wide range of topics, including probability distributions, hypothesis testing, and regression analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gl490z/using
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Effort
1.5 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Dr. Nikunj Maheshwari
Language
English
Good to know
Suitable for learners at the advanced beginner level
Imparts foundational knowledge of probability distributions
Provides practical examples to reinforce learning
Taught by Dr. Nikunj Maheshwari, an expert in probability distributions
Leverages R, a widely used open-source programming language for statistical analysis
May not be suitable for learners with no prior exposure to statistics or programming
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