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Arimoro Olayinka Imisioluwa
In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine the best one for making accurate predictions.
Read more
In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine the best one for making accurate predictions.
Read more
In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine the best one for making accurate predictions.
Upon completion, you’ll produce a well-validated prediction model that showcases your ability to perform a complete data analysis project involving feature engineering, handling missing data, model evaluation, model selection, and model deployment.
There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers.
ROLE: Data Analyst
SKILLS: R, Data Analysis, Predictive Modeling
PREREQUISITES:
Load, clean, explore, manipulate, and visualize data in R,
Use R to build a prediction model
Use R documentations and vignettes to write new codes
Register for this course and see more details by visiting:
OpenCourser.com/course/gbqntb/build
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What's inside
Syllabus
Project
In this 7-9-hour project, you'll build and deploy a stroke prediction model with R and upload your findings to your Coursera profile to showcase to potential employers.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the use of data science principles to build a machine learning model that predicts the likelihood of a patient suffering a stroke
Uses real-world data to build a predictive model
Develops valuable data analysis skills
Provides an opportunity to complete a data science project that can be showcased to potential employers
Utilizes the R programming language, which is widely used in data science
Requires basic knowledge of data analysis and predictive modeling in R, which may limit accessibility for complete beginners
Save this course
Save Build and deploy a stroke prediction model using R 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 Build and deploy a stroke prediction model using R with these
activities:
Seek mentorship from experienced data scientists or practitioners
Show steps
Accelerate learning by connecting with experts who can provide guidance and support.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors through networking events or online platforms
-
Reach out to mentors with a clear purpose and request for support
Review Linear Algebra and Statistics
Show steps
Review foundational linear algebra and statistics to ensure understanding before starting this course.
Browse courses on
Linear Algebra
Show steps
-
Review matrix operations and transformations
-
Review probability distributions and statistical inference
Complete RStudio tutorials on predictive modeling
Show steps
Follow guided tutorials to gain hands-on experience with RStudio and its tools for predictive modeling.
Browse courses on
Predictive Modeling
Show steps
-
Complete the 'Introduction to RStudio' tutorial.
-
Complete the 'Predictive Modeling with R' tutorial.
-
Work through the exercises and examples provided in the tutorials.
-
Explore additional tutorials or resources to expand your knowledge.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Practice R coding challenges
Show steps
Strengthen understanding of R programming through practice and repetition.
Browse courses on
R
Show steps
-
Solve coding challenges on platforms like LeetCode
-
Participate in online coding competitions
Read 'Introduction to Statistical Learning
Show steps
Review the foundational concepts of statistical learning, including supervised and unsupervised learning, model selection, and evaluation.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read chapters 1-5 to gain an understanding of supervised learning and model building.
-
Work through the exercises and examples to practice applying the concepts.
-
Discuss the concepts with peers or mentors to clarify understanding.
-
Apply the concepts to a small dataset to gain practical experience.
Participate in a study group to discuss stroke prediction
Show steps
Engage with peers to exchange knowledge, clarify concepts, and work through challenges related to stroke prediction.
Show steps
-
Join or form a study group with other learners.
-
Choose a specific topic or problem related to stroke prediction.
-
Meet regularly to discuss the topic, share insights, and ask questions.
-
Work collaboratively to develop solutions or explore alternative approaches.
Follow R tutorials on data analysis
Show steps
Expand expertise in data analysis techniques by exploring tutorials and examples.
Browse courses on
Data Analysis
Show steps
-
Enroll in online courses or workshops on data analysis with R
-
Follow tutorials and documentation provided by RStudio
Practice building R models
Show steps
Complete repetitive exercises to reinforce understanding of model building and algorithm selection.
Browse courses on
Model Building
Show steps
-
Load and clean a dataset related to healthcare or medical research.
-
Explore the data using visualization techniques.
-
Split the data into training and testing sets.
-
Build multiple models using different algorithms.
-
Evaluate the models based on performance metrics.
Start a personal data analysis project
Show steps
Gain practical experience by applying skills to a self-directed data analysis project.
Browse courses on
Data Analysis
Show steps
-
Define a project scope and research question
-
Collect or acquire data related to the research question
-
Analyze and interpret the data
-
Communicate findings through a report or presentation
Develop a predictive model for a healthcare dataset
Show steps
Showcase skills by building a predictive model that addresses a real-world healthcare problem.
Browse courses on
Predictive Modeling
Show steps
-
Identify a healthcare dataset and define a research question
-
Explore and preprocess the data
-
Train and evaluate multiple predictive models
-
Interpret model results and communicate findings
Develop a stroke risk prediction dashboard
Show steps
Create an interactive dashboard that visualizes the results of the stroke prediction model and allows users to explore the data and interact with the model.
Browse courses on
Dashboard Development
Show steps
-
Design the dashboard layout and functionality.
-
Select and implement appropriate visualization components.
-
Integrate the stroke prediction model into the dashboard.
-
Test and refine the dashboard for usability and accuracy.
-
Document the dashboard's functionality and use cases
Contribute to open-source R packages for data science
Show steps
Deepen understanding of the R ecosystem and support community development by contributing to open source projects.
Browse courses on
Open Source
Show steps
-
Identify R packages that align with interests or skillset
-
Review documentation and contribute bug fixes or enhancements
-
Collaborate with maintainers and participate in community discussions
Contribute to an open-source project related to stroke prediction
Show steps
Participate in an open-source project to gain practical experience in stroke prediction and contribute to the community.
Show steps
-
Identify an open-source project related to stroke prediction.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute, such as feature engineering or model evaluation.
-
Submit a pull request with your proposed changes.
Develop a stroke prediction app
Show steps
Design and build a mobile app that allows users to assess their risk of stroke and receive personalized recommendations.
Browse courses on
Mobile App Development
Show steps
-
Define the app's functionality and user interface.
-
Design the app's architecture and data flow.
-
Integrate the stroke prediction model into the app.
-
Develop the app's user interface and user experience.
-
Test and deploy the app on a mobile platform
Seek mentorship from experienced data scientists or practitioners
Show steps
Accelerate learning by connecting with experts who can provide guidance and support.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors through networking events or online platforms
- Reach out to mentors with a clear purpose and request for support
Review Linear Algebra and Statistics
Show steps
Review foundational linear algebra and statistics to ensure understanding before starting this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review matrix operations and transformations
- Review probability distributions and statistical inference
Complete RStudio tutorials on predictive modeling
Show steps
Follow guided tutorials to gain hands-on experience with RStudio and its tools for predictive modeling.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Complete the 'Introduction to RStudio' tutorial.
- Complete the 'Predictive Modeling with R' tutorial.
- Work through the exercises and examples provided in the tutorials.
- Explore additional tutorials or resources to expand your knowledge.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Practice R coding challenges
Show steps
Strengthen understanding of R programming through practice and repetition.
Browse courses on
R
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode
- Participate in online coding competitions
Read 'Introduction to Statistical Learning
Show steps
Review the foundational concepts of statistical learning, including supervised and unsupervised learning, model selection, and evaluation.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-5 to gain an understanding of supervised learning and model building.
- Work through the exercises and examples to practice applying the concepts.
- Discuss the concepts with peers or mentors to clarify understanding.
- Apply the concepts to a small dataset to gain practical experience.
Participate in a study group to discuss stroke prediction
Show steps
Engage with peers to exchange knowledge, clarify concepts, and work through challenges related to stroke prediction.
Show steps
Show steps
Show steps
- Join or form a study group with other learners.
- Choose a specific topic or problem related to stroke prediction.
- Meet regularly to discuss the topic, share insights, and ask questions.
- Work collaboratively to develop solutions or explore alternative approaches.
Follow R tutorials on data analysis
Show steps
Expand expertise in data analysis techniques by exploring tutorials and examples.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Enroll in online courses or workshops on data analysis with R
- Follow tutorials and documentation provided by RStudio
Practice building R models
Show steps
Complete repetitive exercises to reinforce understanding of model building and algorithm selection.
Browse courses on
Model Building
Show steps
Show steps
Show steps
- Load and clean a dataset related to healthcare or medical research.
- Explore the data using visualization techniques.
- Split the data into training and testing sets.
- Build multiple models using different algorithms.
- Evaluate the models based on performance metrics.
Start a personal data analysis project
Show steps
Gain practical experience by applying skills to a self-directed data analysis project.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Define a project scope and research question
- Collect or acquire data related to the research question
- Analyze and interpret the data
- Communicate findings through a report or presentation
Develop a predictive model for a healthcare dataset
Show steps
Showcase skills by building a predictive model that addresses a real-world healthcare problem.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Identify a healthcare dataset and define a research question
- Explore and preprocess the data
- Train and evaluate multiple predictive models
- Interpret model results and communicate findings
Develop a stroke risk prediction dashboard
Show steps
Create an interactive dashboard that visualizes the results of the stroke prediction model and allows users to explore the data and interact with the model.
Browse courses on
Dashboard Development
Show steps
Show steps
Show steps
- Design the dashboard layout and functionality.
- Select and implement appropriate visualization components.
- Integrate the stroke prediction model into the dashboard.
- Test and refine the dashboard for usability and accuracy.
- Document the dashboard's functionality and use cases
Contribute to open-source R packages for data science
Show steps
Deepen understanding of the R ecosystem and support community development by contributing to open source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify R packages that align with interests or skillset
- Review documentation and contribute bug fixes or enhancements
- Collaborate with maintainers and participate in community discussions
Contribute to an open-source project related to stroke prediction
Show steps
Participate in an open-source project to gain practical experience in stroke prediction and contribute to the community.
Show steps
Show steps
Show steps
- Identify an open-source project related to stroke prediction.
- Review the project's documentation and codebase.
- Identify an area where you can contribute, such as feature engineering or model evaluation.
- Submit a pull request with your proposed changes.
Develop a stroke prediction app
Show steps
Design and build a mobile app that allows users to assess their risk of stroke and receive personalized recommendations.
Browse courses on
Mobile App Development
Show steps
Show steps
Show steps
- Define the app's functionality and user interface.
- Design the app's architecture and data flow.
- Integrate the stroke prediction model into the app.
- Develop the app's user interface and user experience.
- Test and deploy the app on a mobile platform
Career center
Learners who complete Build and deploy a stroke prediction model using R will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help strengthen knowledge in statistical modeling.
Epidemiologist
Epidemiologist
Epidemiologists are responsible for studying the distribution and determinants of health-related states or events in specified populations. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis and epidemiology.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers are responsible for designing, developing, and deploying machine-learning models. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling, predictive modeling, and deploying models using R. The course can also help build a foundation in machine learning and strengthen knowledge in statistical modeling.
Healthcare Analyst
Healthcare Analyst
Healthcare analysts are responsible for collecting, analyzing, and interpreting data to improve the efficiency and effectiveness of healthcare delivery. This course can be useful for those interested in this career path because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis.
Research Analyst
Research Analyst
Research analysts are responsible for conducting research and providing insights on a variety of topics. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis and strengthen knowledge in statistical modeling.
Data Scientist
Data Scientist
Data scientists apply statistical and machine learning techniques to help businesses make data-driven decisions. Those interested in this career path should take this course because it will provide them with the skills necessary to effectively perform these tasks. More specifically, this course will help in developing a strong foundation in data analysis and modeling using R, which is essential for building models that can predict future outcomes.
Data Science Manager
Data Science Manager
Data science managers are responsible for leading and managing data science teams. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis, project management, and leadership.
Biostatistician
Biostatistician
In order to be successful in this role, one must be able to design, conduct, and analyze statistical studies on medical data that are used to assess the safety and efficacy of new treatments. Those interested in this career path should take this course because it will provide them with the skills necessary to effectively perform these tasks. More specifically, this course will help in building foundational knowledge in data analysis and statistical modeling, which is essential for developing and conducting research studies.
Actuary
Actuary
Actuaries are responsible for assessing risk and uncertainty in insurance, finance, and other fields. If you are interested in this career path, taking this course may be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help strengthen knowledge in statistical modeling.
Risk Manager
Risk Manager
Risk managers are responsible for identifying, assessing, and managing risks. If you are interested in this career path, taking this course may be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis and risk management.
Data Analyst
Data Analyst
Data analysts are leaders in building dashboards, reports, and visualizations that translate raw data into actionable insights. They partner with business leaders to solve the most pressing business problems. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis and strengthen knowledge in statistical modeling.
Business Analyst
Business Analyst
Business analysts are responsible for analyzing business needs and developing solutions to improve business outcomes. If you are interested in this career path, taking this course may be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts are responsible for using mathematical and statistical models to analyze financial data. If you are interested in this career path, taking this course can be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help strengthen knowledge in statistical modeling.
Data Engineer
Data Engineer
Data engineers are responsible for designing, building, and maintaining data pipelines. If you are interested in this career path, taking this course may be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis.
Software Engineer
Software Engineer
Software engineers are responsible for designing, developing, and maintaining software systems. If you are interested in this career path, taking this course may be useful because it provides a structured learning environment to get started with data handling and predictive modeling using R. The course can also help build a foundation in data analysis.
For more career information including salaries, visit:
OpenCourser.com/course/gbqntb/build
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
Build and deploy a stroke prediction model using R.
Provides a comprehensive overview of statistical learning methods, including both supervised and unsupervised learning. It valuable resource for anyone interested in learning more about the theory and practice of statistical learning.
Classic in the field of statistical learning. It provides a comprehensive overview of the theory and practice of statistical learning, with a focus on supervised learning.
Renowned textbook in statistics that provides a comprehensive overview of statistical techniques. It offers a solid foundation in statistical concepts and methods, making it a valuable resource for understanding the statistical principles used in building prediction models.
Save
Provides a practical guide to predictive modeling. It covers various modeling techniques, including linear and logistic regression, decision trees, and ensemble methods. The book also includes case studies and exercises, making it a valuable resource for gaining hands-on experience in building prediction models.
Save
Comprehensive guide to using R for data analysis and visualization. It covers the fundamentals of R programming, data manipulation, and statistical analysis. The book is an essential resource for anyone who wants to use R for building prediction models.
Provides a practical guide to machine learning with R. It covers various machine learning algorithms and techniques, including supervised and unsupervised learning, model evaluation, and data visualization. The book valuable resource for those who want to use R for building prediction models.
Comprehensive guide to using R for machine learning. It covers various machine learning algorithms and techniques, including supervised and unsupervised learning, model evaluation, and data visualization. The book valuable resource for those who want to use R for building prediction models.
Provides a comprehensive overview of the foundations of data science. It covers topics such as data mining, machine learning, and optimization. The book valuable resource for understanding the underlying principles of data analysis and prediction modeling.
Provides a practical guide to building interpretable machine learning models. It covers techniques for understanding and explaining the predictions of machine learning models. This book is valuable for those interested in making their prediction models more transparent and interpretable.
Save
Comprehensive guide to deep learning, a subfield of machine learning that has gained popularity in recent years. It provides an overview of deep learning architectures, learning algorithms, and applications. While deep learning is not the focus of the course, this book can provide additional depth for those interested in exploring advanced modeling techniques.
Provides a Bayesian approach to statistical modeling. It covers Bayesian inference, model checking, and applications in various fields. This book can be valuable for those interested in exploring alternative approaches to statistical modeling and prediction.
Provides a theoretical foundation for prediction and learning algorithms. It covers topics such as statistical learning theory, online learning, and game theory. The book valuable resource for understanding the theoretical underpinnings of prediction modeling.
Comprehensive guide to causal inference, a branch of statistics that focuses on understanding the causal relationships between variables. It provides a theoretical foundation for causal modeling and discusses various causal inference methods.
Save
Provides a thought-provoking exploration of the future of machine learning. It discusses the potential of machine learning to solve complex problems and transform various industries. The book offers a broader perspective on the role of prediction modeling in the context of technological advancements.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gbqntb/build
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Effort
7-9 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Good to know
Teaches the use of data science principles to build a machine learning model that predicts the likelihood of a patient suffering a stroke
Uses real-world data to build a predictive model
Develops valuable data analysis skills
Provides an opportunity to complete a data science project that can be showcased to potential employers
Utilizes the R programming language, which is widely used in data science
Requires basic knowledge of data analysis and predictive modeling in R, which may limit accessibility for complete beginners
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