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Laura K. Wiley, PhD
and
Michael G. Kahn, MD, PhD
This course prepares you to deal with advanced clinical data science topics and techniques including temporal and research quality analysis.
Register for this course and see more details by visiting:
OpenCourser.com/course/azx4eh/advanced
What's inside
Syllabus
Introduction: Advanced Clinical Data Science
Learn how to perform high quality and replicable clinical analyses.
Tools and Techniques: Temporality
Read more
Syllabus
Introduction: Advanced Clinical Data Science
Learn how to perform high quality and replicable clinical analyses.
Tools and Techniques: Temporality
Read more
Learn how to handle the impact of time on clinical data science analyses.
Tools and Techniques: Missing Data
Learn how to handle missing data in clinical data science.
Practical Application: Careers in Clinical Data Science
Prepare for the next step in your clinical data science journey by exploring potential career options!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for existing clinical data scientists seeking further expertise
Provides advanced techniques for handling time, missing data, and research quality analysis in clinical data
Taught by instructors with extensive experience in clinical data science
Save this course
Save Advanced Clinical Data Science to your list so you can find it easily later:
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Reviews summary
Excellent advanced clinical data science course
Learners say this course is very well developed and easy to understand. Students appreciate the hands-on practical experiences they receive, which have allowed them to learn important aspects about the science of clinical data.
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 Advanced Clinical Data Science with these
activities:
Review foundational data science concepts
Show steps
Reviewing foundational data science concepts will strengthen your understanding of the advanced topics covered in this course.
Browse courses on
Data Science Concepts
Show steps
-
Revisit introductory data science books or online resources.
-
Complete online tutorials or refreshers on basic data science skills.
-
Practice basic data manipulation and analysis tasks in a coding environment.
Follow online tutorials on advanced clinical data science techniques
Show steps
Supplement your course studies with guided tutorials to enhance your understanding of advanced clinical data science techniques.
Browse courses on
Clinical Data Science
Show steps
-
Search for tutorials that cover the topics you want to learn more about.
-
Follow the tutorials step-by-step.
-
Complete the exercises and quizzes to test your understanding.
Attend Clinical Data Science Conferences and Meetups
Show steps
Networking events provide opportunities to connect with professionals in the field, learn about the latest trends, and expand your knowledge.
Show steps
-
Identify relevant conferences and meetups
-
Attend the events and participate in discussions
-
Connect with other attendees and exchange ideas
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Practice Working with Temporal Data
Show steps
Get hands-on experience working with different types of temporal data to get better acquainted with the complexities of time in clinical data. Doing so will prepare you for deeper exploration later in the course.
Show steps
-
Find a dataset that contains temporal data.
-
Create a data visualization that shows how the data changes over time.
-
Write a report that summarizes your findings.
Attend a Workshop on Clinical Data Science
Show steps
Supplemental workshops can help provide face-to-face engagement with content and concepts covered in the course. Seek out workshops on specific topics where you may be struggling.
Browse courses on
Clinical Data Science
Show steps
-
Find a workshop that is relevant to your interests.
-
Register for the workshop.
-
Attend the workshop.
Join study groups and collaborate with peers
Show steps
Collaborating with classmates can reinforce concepts, provide diverse perspectives, and enhance motivation.
Show steps
-
Identify classmates or online forums for peer collaboration.
-
Organize regular study sessions to discuss course material and work on assignments.
Infographic on Missing Data Handling Techniques
Show steps
Creating an infographic will force you to synthesize information you've learned about missing data handling techniques and find a way to communicate them in a visually appealing way.
Browse courses on
Missing Data
Show steps
-
Gather information about missing data handling techniques.
-
Create a visual representation of the information you gathered.
-
Write a brief summary of your findings.
Practice manipulating clinical data
Show steps
Practicing data manipulation specific to clinical datasets will help you develop the skills necessary to perform advanced clinical data science analysis.
Browse courses on
Missing Data
Show steps
-
Find practice datasets that contain clinical data.
-
Manipulate the data using appropriate techniques, such as filtering, sorting, and aggregating.
-
Apply data visualization techniques to explore the data and identify patterns.
Practice analyzing clinical data with real-world datasets
Show steps
Practice your data science skills in clinical data analysis by working with datasets in the real world.
Browse courses on
Clinical Data Science
Show steps
-
Find a dataset that is relevant to your interests.
-
Clean and prepare the data for analysis.
-
Perform exploratory data analysis.
-
Develop and test a model.
Practice Handling Missing Data
Show steps
Engaging in practice drills will strengthen your ability to effectively handle missing data, a common challenge in clinical data science.
Browse courses on
Missing Data Imputation
Show steps
-
Identify the different types of missing data
-
Learn how to impute missing data using different methods
-
Practice using statistical methods to analyze data with missing values
Write a Blog Post on a Clinical Data Science Topic
Show steps
Engage with the content and articulate your understanding of the material in a way that is helpful to other students. This type of knowledge translation can help reinforce your learning.
Browse courses on
Clinical Data Science
Show steps
-
Choose a topic that you are interested in.
-
Research the topic.
-
Write a blog post that explains the topic in a clear and concise way.
-
Publish your blog post.
Develop a Clinical Data Science Application
Show steps
Synthesize what you are learning in the course with practical application. This course is largely grounded in technical application, so endeavor to build a project based on what you are learning.
Browse courses on
Clinical Data Science
Show steps
-
Identify a clinical problem that you want to solve.
-
Gather data that is relevant to the problem.
-
Develop a model that can solve the problem.
-
Deploy the model in a clinical setting.
-
Evaluate the performance of the model.
Develop a case study of a clinical data science project
Show steps
Creating a case study will provide you with hands-on experience applying advanced clinical data science techniques to a real-world problem.
Browse courses on
Missing Data
Show steps
-
Select a clinical data science problem or question to explore.
-
Gather and prepare the necessary data.
-
Apply advanced clinical data science techniques to analyze the data.
-
Develop insights and conclusions based on the analysis.
-
Present your findings in a clear and concise manner.
Design and implement a hypothetical clinical data science project
Show steps
Create a project to put into practice the skills acquired in this course and expand your knowledge in different areas of clinical data science.
Browse courses on
Clinical Data Science
Show steps
-
Define the scope and objectives of your project.
-
Gather and preprocess the necessary data.
-
Develop and implement your analysis plan.
-
Interpret and present your results.
Develop a Clinical Data Science Research Proposal
Show steps
Creating a research proposal will allow you to apply the principles of clinical data science to a specific research question, enhancing your understanding and practical skills.
Browse courses on
Research Proposal Writing
Show steps
-
Identify a research question and develop a hypothesis
-
Design a study to test your hypothesis
-
Write a research proposal that outlines your study design and methods
Create a Blog or Article on a Clinical Data Science Topic
Show steps
Writing and sharing your knowledge helps solidify your understanding, fosters critical thinking, and contributes to the broader community.
Show steps
-
Choose a topic and conduct research
-
Write a well-structured and informative article or blog post
-
Publish your work and share it with others
Mentor Junior Clinical Data Scientists
Show steps
Mentoring others allows you to reinforce your knowledge and improve your communication skills while contributing to the development of junior professionals in the field.
Show steps
-
Identify opportunities to mentor junior data scientists
-
Provide guidance and support to your mentees
-
Share your knowledge and experience
Review foundational data science concepts
Show steps
Reviewing foundational data science concepts will strengthen your understanding of the advanced topics covered in this course.
Browse courses on
Data Science Concepts
Show steps
Show steps
Show steps
- Revisit introductory data science books or online resources.
- Complete online tutorials or refreshers on basic data science skills.
- Practice basic data manipulation and analysis tasks in a coding environment.
Follow online tutorials on advanced clinical data science techniques
Show steps
Supplement your course studies with guided tutorials to enhance your understanding of advanced clinical data science techniques.
Browse courses on
Clinical Data Science
Show steps
Show steps
Show steps
- Search for tutorials that cover the topics you want to learn more about.
- Follow the tutorials step-by-step.
- Complete the exercises and quizzes to test your understanding.
Attend Clinical Data Science Conferences and Meetups
Show steps
Networking events provide opportunities to connect with professionals in the field, learn about the latest trends, and expand your knowledge.
Show steps
Show steps
Show steps
- Identify relevant conferences and meetups
- Attend the events and participate in discussions
- Connect with other attendees and exchange ideas
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Practice Working with Temporal Data
Show steps
Get hands-on experience working with different types of temporal data to get better acquainted with the complexities of time in clinical data. Doing so will prepare you for deeper exploration later in the course.
Show steps
Show steps
Show steps
- Find a dataset that contains temporal data.
- Create a data visualization that shows how the data changes over time.
- Write a report that summarizes your findings.
Attend a Workshop on Clinical Data Science
Show steps
Supplemental workshops can help provide face-to-face engagement with content and concepts covered in the course. Seek out workshops on specific topics where you may be struggling.
Browse courses on
Clinical Data Science
Show steps
Show steps
Show steps
- Find a workshop that is relevant to your interests.
- Register for the workshop.
- Attend the workshop.
Join study groups and collaborate with peers
Show steps
Collaborating with classmates can reinforce concepts, provide diverse perspectives, and enhance motivation.
Show steps
Show steps
Show steps
- Identify classmates or online forums for peer collaboration.
- Organize regular study sessions to discuss course material and work on assignments.
Infographic on Missing Data Handling Techniques
Show steps
Creating an infographic will force you to synthesize information you've learned about missing data handling techniques and find a way to communicate them in a visually appealing way.
Browse courses on
Missing Data
Show steps
Show steps
Show steps
- Gather information about missing data handling techniques.
- Create a visual representation of the information you gathered.
- Write a brief summary of your findings.
Practice manipulating clinical data
Show steps
Practicing data manipulation specific to clinical datasets will help you develop the skills necessary to perform advanced clinical data science analysis.
Browse courses on
Missing Data
Show steps
Show steps
Show steps
- Find practice datasets that contain clinical data.
- Manipulate the data using appropriate techniques, such as filtering, sorting, and aggregating.
- Apply data visualization techniques to explore the data and identify patterns.
Practice analyzing clinical data with real-world datasets
Show steps
Practice your data science skills in clinical data analysis by working with datasets in the real world.
Browse courses on
Clinical Data Science
Show steps
Show steps
Show steps
- Find a dataset that is relevant to your interests.
- Clean and prepare the data for analysis.
- Perform exploratory data analysis.
- Develop and test a model.
Practice Handling Missing Data
Show steps
Engaging in practice drills will strengthen your ability to effectively handle missing data, a common challenge in clinical data science.
Browse courses on
Missing Data Imputation
Show steps
Show steps
Show steps
- Identify the different types of missing data
- Learn how to impute missing data using different methods
- Practice using statistical methods to analyze data with missing values
Write a Blog Post on a Clinical Data Science Topic
Show steps
Engage with the content and articulate your understanding of the material in a way that is helpful to other students. This type of knowledge translation can help reinforce your learning.
Browse courses on
Clinical Data Science
Show steps
Show steps
Show steps
- Choose a topic that you are interested in.
- Research the topic.
- Write a blog post that explains the topic in a clear and concise way.
- Publish your blog post.
Develop a Clinical Data Science Application
Show steps
Synthesize what you are learning in the course with practical application. This course is largely grounded in technical application, so endeavor to build a project based on what you are learning.
Browse courses on
Clinical Data Science
Show steps
Show steps
Show steps
- Identify a clinical problem that you want to solve.
- Gather data that is relevant to the problem.
- Develop a model that can solve the problem.
- Deploy the model in a clinical setting.
- Evaluate the performance of the model.
Develop a case study of a clinical data science project
Show steps
Creating a case study will provide you with hands-on experience applying advanced clinical data science techniques to a real-world problem.
Browse courses on
Missing Data
Show steps
Show steps
Show steps
- Select a clinical data science problem or question to explore.
- Gather and prepare the necessary data.
- Apply advanced clinical data science techniques to analyze the data.
- Develop insights and conclusions based on the analysis.
- Present your findings in a clear and concise manner.
Design and implement a hypothetical clinical data science project
Show steps
Create a project to put into practice the skills acquired in this course and expand your knowledge in different areas of clinical data science.
Browse courses on
Clinical Data Science
Show steps
Show steps
Show steps
- Define the scope and objectives of your project.
- Gather and preprocess the necessary data.
- Develop and implement your analysis plan.
- Interpret and present your results.
Develop a Clinical Data Science Research Proposal
Show steps
Creating a research proposal will allow you to apply the principles of clinical data science to a specific research question, enhancing your understanding and practical skills.
Browse courses on
Research Proposal Writing
Show steps
Show steps
Show steps
- Identify a research question and develop a hypothesis
- Design a study to test your hypothesis
- Write a research proposal that outlines your study design and methods
Create a Blog or Article on a Clinical Data Science Topic
Show steps
Writing and sharing your knowledge helps solidify your understanding, fosters critical thinking, and contributes to the broader community.
Show steps
Show steps
Show steps
- Choose a topic and conduct research
- Write a well-structured and informative article or blog post
- Publish your work and share it with others
Mentor Junior Clinical Data Scientists
Show steps
Mentoring others allows you to reinforce your knowledge and improve your communication skills while contributing to the development of junior professionals in the field.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior data scientists
- Provide guidance and support to your mentees
- Share your knowledge and experience
Career center
Learners who complete Advanced Clinical Data Science will develop knowledge and skills
that may be useful to these careers:
Clinical Informatics Specialist
Clinical Informatics Specialist
A Clinical Informatics Specialist is a professional who uses informatics to improve healthcare delivery. This role typically requires a bachelor's or master's degree in nursing, medicine, or a related field. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest clinical informatics methods and tools used in the field.
Epidemiologist
Epidemiologist
An Epidemiologist is a professional who studies the distribution and patterns of health and disease in populations. This role typically requires a master's or PhD degree in epidemiology or public health. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest epidemiological methods and tools used in the field.
Biostatistician
Biostatistician
A Biostatistician is a professional who uses statistics to analyze biological data. This role typically requires a master's or PhD degree in statistics or biostatistics. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest biostatistical methods and tools used in the field.
Healthcare Information Technology Specialist
Healthcare Information Technology Specialist
A Healthcare Information Technology Specialist is a professional who designs, implements, and maintains healthcare information systems. This role typically requires a bachelor's or master's degree in computer science, information technology, or a related field. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest healthcare information technology methods and tools used in the field.
Health Policy Analyst
Health Policy Analyst
A Health Policy Analyst is a professional who analyzes and develops health policy. This role typically requires a master's or PhD degree in public health, health policy, or a related field. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest health policy analysis methods and tools used in the field.
Clinical Data Manager
Clinical Data Manager
A Clinical Data Manager is a professional who manages and analyzes clinical data. This role typically requires a bachelor's degree in a life science field, such as biology, chemistry, or nursing. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest clinical data management methods and tools used in the field.
Clinical Research Associate
Clinical Research Associate
A Clinical Research Associate is a professional who coordinates and manages clinical trials. This role typically requires a bachelor's degree in a life science field, such as biology, chemistry, or nursing. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest clinical research methods and tools used in the field.
Healthcare Consultant
Healthcare Consultant
A Healthcare Consultant is a professional who provides advice and guidance to healthcare organizations. This role typically requires a master's or PhD degree in business, healthcare administration, or a related field. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest healthcare consulting methods and tools used in the field.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses data to solve business problems. This role typically requires a master's or PhD degree in a quantitative field, such as mathematics, statistics, or computer science. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest data science methods and tools used in the field.
Medical Writer
Medical Writer
A Medical Writer is a professional who writes and edits medical content. This role typically requires a bachelor's degree in a life science field, such as biology, chemistry, or nursing. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest medical writing methods and tools used in the field.
Medical Device Sales Representative
Medical Device Sales Representative
A Medical Device Sales Representative is a professional who sells and markets medical devices. This role typically requires a bachelor's degree in a life science field, such as biology, chemistry, or nursing. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest medical device sales methods and tools used in the field.
Pharmaceutical Sales Representative
Pharmaceutical Sales Representative
A Pharmaceutical Sales Representative is a professional who sells and markets pharmaceutical products. This role typically requires a bachelor's degree in a life science field, such as biology, chemistry, or nursing. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest pharmaceutical sales methods and tools used in the field.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a professional who designs and builds machine learning models. This role typically requires a bachelor's or master's degree in a quantitative field, such as mathematics, statistics, or computer science. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest machine learning methods and tools used in the field.
Data Analyst
Data Analyst
A Data Analyst is a professional who collects, analyzes, and interprets data to help organizations make better decisions. This role typically requires a bachelor's degree in a quantitative field, such as mathematics, statistics, or computer science. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest data analysis methods and tools used in the field.
Research Scientist
Research Scientist
A Research Scientist is a professional who conducts scientific research and develops new knowledge in a particular field. This role typically requires an advanced degree, such as a master's or PhD. Advanced Clinical Data Science may be useful for this role, as it can help build a foundation in the principles and techniques of clinical data science. This course can also provide insights into the latest research methods and tools used in the field.
For more career information including salaries, visit:
OpenCourser.com/course/azx4eh/advanced
Reading list
We've selected six 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
Advanced Clinical Data Science.
Provides a comprehensive introduction to the theory, methods, and applications of temporal data science, with a focus on clinical research. It covers topics such as longitudinal data analysis, survival analysis, and event history analysis.
Provides a comprehensive overview of the advanced statistical methods used in clinical research. It covers topics such as survival analysis, longitudinal data analysis, and Bayesian methods.
Save
Provides a practical guide to clinical data analytics for researchers and practitioners. It covers topics such as data acquisition, data wrangling, statistical analysis, machine learning, and clinical decision support.
Provides a practical guide to machine learning for healthcare practitioners. It covers topics such as data preprocessing, feature selection, model evaluation, and clinical decision support.
Save
Provides a comprehensive overview of the machine learning algorithms used in medicine. It covers topics such as data mining, natural language processing, and image processing.
Provides a practical guide to clinical data management for clinicians. It covers topics such as data collection, data entry, data validation, and data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/azx4eh/advanced
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Effort
4 weeks of study, 1-2 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado System
Instructors
Laura K. Wiley, PhD
Michael G. Kahn, MD, PhD
Language
English
Transcripts
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This course belongs to a
collection
called
Clinical Data Science. It's comprised of six courses:
Good to know
Designed for existing clinical data scientists seeking further expertise
Provides advanced techniques for handling time, missing data, and research quality analysis in clinical data
Taught by instructors with extensive experience in clinical data science
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