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Fredrik Hallgren
This course introduces the most important methods and concepts from statistics with applications in the R programming language. We cover the fitting of statistical models to data, statistical testing, and prediction.
Read more
This course introduces the most important methods and concepts from statistics with applications in the R programming language. We cover the fitting of statistical models to data, statistical testing, and prediction.
Read more
This course introduces the most important methods and concepts from statistics with applications in the R programming language. We cover the fitting of statistical models to data, statistical testing, and prediction.
We need principles, models, and theory to make sense of the vast amounts of data generated in today’s world. In this course, Interpreting Data Using Statistical Models in R, you will gain the ability to apply statistical and data science models to any task. First, you will learn how to fit statistical models to data. Next, you will discover how to test for relationships in data. Finally, you will explore how to create predictions with linear regression. When you are finished with this course, you will have the skills needed to turn data into knowledge.
Register for this course and see more details by visiting:
OpenCourser.com/course/0tbrrh/interpreting
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What's inside
Syllabus
Course Overview
Creating Statistical Models
Fitting Statistical Models
Implementing a Predictive Model: Single-variable Linear Regression
Read more
Syllabus
Course Overview
Creating Statistical Models
Fitting Statistical Models
Implementing a Predictive Model: Single-variable Linear Regression
Read more
Drawing Conclusions from Data with Statistical Testing
Using Multi-variable Linear Regression
Ensuring Predictive Accuracy
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Fredrik Hallgren, who is recognized for their work in R statistical programming
Suitable for learners who are interested in using R for statistical analysis
May be useful for students in the data science field
May be suitable for individuals who want to enhance their skills in fitting statistical models and drawing conclusions from data
Covers foundational concepts in statistics, making it suitable for beginners
Provides a comprehensive overview of statistical methods and their applications in R
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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 Interpreting Data Using Statistical Models in R with these
activities:
Read 'Introduction to Statistical Learning'
Show steps
This book provides a comprehensive overview of statistical learning techniques, complementing the concepts covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read the introductory chapters
-
Focus on chapters relevant to the course topics
-
Identify key concepts and techniques
Create a data visualization to illustrate statistical concepts
Show steps
Creating a visual representation of statistical concepts deepens understanding and aids in remembering key principles.
Browse courses on
Statistical Modeling
Show steps
-
Choose a relevant statistical concept
-
Select an appropriate data visualization technique
-
Create the visualization using tools like R or Python
-
Interpret the visualization and explain its implications
Practice conducting statistical tests with R
Show steps
Hands-on practice with statistical testing enhances understanding of hypothesis testing and interpreting data in the context of the course.
Browse courses on
Statistical Testing
Show steps
-
Formulate a hypothesis based on the data
-
Select the appropriate statistical test
-
Conduct the test using R code
-
Interpret the results and draw conclusions
Two other activities
Expand to see all activities and additional details
Show all five activities
Connect with professionals or experts in the field
Show steps
Having a mentor can provide guidance, support, and insights that enhance the learning process.
Show steps
-
Identify potential mentors who align with your interests
-
Reach out to them and express your interest in learning
-
Schedule regular meetings or discussions
Develop a predictive model using R
Show steps
Building a predictive model in R reinforces the practical applications of statistical concepts covered in the course.
Browse courses on
Predictive Modeling
Show steps
-
Identify a problem or question that can be solved through prediction
-
Gather and prepare the data
-
Build a predictive model using R
-
Evaluate the model's performance
-
Present the results and insights
Read 'Introduction to Statistical Learning'
Show steps
This book provides a comprehensive overview of statistical learning techniques, complementing the concepts covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read the introductory chapters
- Focus on chapters relevant to the course topics
- Identify key concepts and techniques
Create a data visualization to illustrate statistical concepts
Show steps
Creating a visual representation of statistical concepts deepens understanding and aids in remembering key principles.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Choose a relevant statistical concept
- Select an appropriate data visualization technique
- Create the visualization using tools like R or Python
- Interpret the visualization and explain its implications
Practice conducting statistical tests with R
Show steps
Hands-on practice with statistical testing enhances understanding of hypothesis testing and interpreting data in the context of the course.
Browse courses on
Statistical Testing
Show steps
Show steps
Show steps
- Formulate a hypothesis based on the data
- Select the appropriate statistical test
- Conduct the test using R code
- Interpret the results and draw conclusions
Two other activities
Expand to see all activities and additional details
Show all five activities
Connect with professionals or experts in the field
Show steps
Having a mentor can provide guidance, support, and insights that enhance the learning process.
Show steps
Show steps
Show steps
- Identify potential mentors who align with your interests
- Reach out to them and express your interest in learning
- Schedule regular meetings or discussions
Develop a predictive model using R
Show steps
Building a predictive model in R reinforces the practical applications of statistical concepts covered in the course.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Identify a problem or question that can be solved through prediction
- Gather and prepare the data
- Build a predictive model using R
- Evaluate the model's performance
- Present the results and insights
Career center
Learners who complete Interpreting Data Using Statistical Models in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use statistical models and other mathematical approaches to analyze data and interpret their findings. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in a variety of industries, including finance, healthcare, and marketing.
Statistician
Statistician
Statisticians use statistical models to collect, analyze, interpret, and present data. This course provides a comprehensive overview of statistical modeling, including how to fit statistical models to data, test for relationships in data, and make predictions. These skills are essential for success in this role, which is in high demand in a variety of industries, including government, academia, and research.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in a variety of industries, including technology, finance, and healthcare.
Data Scientist
Data Scientist
Data Scientists use statistical models and other mathematical approaches to analyze data and make predictions. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in a variety of industries, including technology, finance, and healthcare.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical models and other mathematical approaches to analyze financial data and make predictions. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in the financial industry.
Actuary
Actuary
Actuaries use statistical models to assess risk and make financial decisions. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in the insurance industry.
Biostatistician
Biostatistician
Biostatisticians use statistical models to analyze biological data and make predictions. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in the healthcare and pharmaceutical industries.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use statistical models to optimize business processes. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in a variety of industries, including manufacturing, logistics, and healthcare.
Market Research Analyst
Market Research Analyst
Market Research Analysts use statistical models to analyze market data and make predictions. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in a variety of industries, including marketing, advertising, and public relations.
Survey Researcher
Survey Researcher
Survey Researchers use statistical models to design and analyze surveys. This course provides a strong foundation in statistical modeling, which is essential for success in this role. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills are in high demand in a variety of industries, including government, academia, and market research.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. This course may be useful for Data Engineers who want to learn more about statistical modeling. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills can be helpful for building and maintaining data pipelines that are used to train machine learning models.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course may be useful for Database Administrators who want to learn more about statistical modeling. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills can be helpful for optimizing database performance and ensuring data integrity.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers who want to learn more about statistical modeling. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills can be helpful for developing software applications that use statistical models.
Business Analyst
Business Analyst
Business Analysts use data to make recommendations for businesses. This course may be useful for Business Analysts who want to learn more about statistical modeling. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills can be helpful for making data-driven recommendations that can improve business outcomes.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. This course may be useful for Product Managers who want to learn more about statistical modeling. You will learn how to fit statistical models to data, test for relationships in data, and make predictions. These skills can be helpful for understanding customer needs and developing products that meet those needs.
For more career information including salaries, visit:
OpenCourser.com/course/0tbrrh/interpreting
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
Interpreting Data Using Statistical Models in R.
The classic reference book for statistical learning, covering a wide range of topics in great depth. It is an excellent choice for those who want to learn more about the theoretical foundations of statistical learning.
A comprehensive guide to Bayesian data analysis, covering a wide range of topics including Bayesian inference, model checking, and predictive modelling.
A comprehensive guide to machine learning from a probabilistic perspective, covering a wide range of topics including supervised learning, unsupervised learning, and Bayesian modelling.
Save
A comprehensive guide to pattern recognition and machine learning, covering a wide range of topics including supervised learning, unsupervised learning, and kernel methods.
An excellent resource for learning the fundamentals of statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and model assessment.
A classic reference book on generalized linear models, covering a wide range of topics including logistic regression, Poisson regression, and negative binomial regression.
This classic and widely-used textbook introduces students to the core concepts of statistics. It covers a wide range of topics, including descriptive statistics, probability, hypothesis testing, regression, and analysis of variance.
A comprehensive guide to regression modelling, covering a wide range of topics including linear regression, logistic regression, and survival analysis.
An excellent resource for learning about regression and multilevel/hierarchical models. It covers a wide range of topics, including Bayesian modelling.
A classic introduction to Bayesian statistics, covering a wide range of topics including probability theory, Bayesian inference, and model selection.
A comprehensive guide to statistical methods, with a focus on practical applications. It good choice for those who want to apply statistics in their own work, rather than just learning the theory.
A comprehensive guide to statistical methods in psychology, covering a wide range of topics including descriptive statistics, hypothesis testing, and regression.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0tbrrh/interpreting
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Time
105 hours
Level
Introductory
Via
Pluralsight
Instructor
Fredrik Hallgren
Language
English
Good to know
Taught by Fredrik Hallgren, who is recognized for their work in R statistical programming
Suitable for learners who are interested in using R for statistical analysis
May be useful for students in the data science field
May be suitable for individuals who want to enhance their skills in fitting statistical models and drawing conclusions from data
Covers foundational concepts in statistics, making it suitable for beginners
Provides a comprehensive overview of statistical methods and their applications in R
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