Doing Clinical Research: Biostatistics with the Wolfram Language
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Approx. 15 hours to complete
Course Summary
This course provides an introduction to clinical research and biostatistics. Students will learn about the design and analysis of clinical trials, as well as the principles of biostatistics and clinical research. The course is taught by experts in the field and is designed for healthcare professionals, researchers, and students.Key Learning Points
- Understand the design and analysis of clinical trials
- Learn the principles of biostatistics and clinical research
- Gain insight into the role of healthcare professionals in clinical research
Related Topics for further study
Learning Outcomes
- Understand the design and analysis of clinical trials
- Learn the principles of biostatistics and clinical research
- Gain insight into the role of healthcare professionals in clinical research
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with healthcare terminology
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Lecture-based
Similar Courses
- Clinical Trials: Design and Management
- Clinical Data Science
Related Education Paths
Notable People in This Field
- Cardiologist and Digital Medicine Expert
- Surgeon and Healthcare Researcher
Related Books
Description
This course has a singular and clear aim, to empower you to do statistical tests, ready for incorporation into your dissertations, research papers, and presentations. The ability to summarize data, create plots and charts, and to do the tests that you commonly see in the literature is a powerful skill indeed. Not only will it further your career, but it will put you in the position to contribute to the advancement of humanity through scientific research.
Outline
- Week 1
- Welcome
- The Klopper Research Group
- Assumptions
- Learning a computer language
- Why the Wolfram language?
- Getting Mathematica
- The new Wolfram Cloud
- The Wolfram Cloud
- The Wolfram Programming Lab
- Free-form input and Wolfram Alpha in the Cloud
- Mathematica
- Free-form input and Wolfram Alpha in the desktop
- Help and documentation
- Assignment notebooks
- How this Course Works
- Welcome to Module 1
- Meet the Course Instructor
- Module 1 Notebook
- Welcome to Wolfram Cloud
- Welcome to Module 3
- Module 3 Notebook
- Module 3 Exercise
- Week 2
- Create Your Own Computational Essay
- Simulated data demonstration - part 1
- Simulated data demonstration - part 2
- Simple arithmetic
- Addition and subtraction
- Multiplication and division
- Powers
- Arithmetical order
- Calculating a mean
- Working with data
- Lists part 1
- Lists part 2
- Tables
- Index
- Datasets
- Selecting
- Dataset functions
- Creating lists from datasets
- Spreadsheets
- Spreadsheets in the cloud
- Welcome to Module 4
- Module 4 Notebook
- Welcome to Module 5
- Module 5 Exercise
- Welcome to Module 6
- Module 6 Notebook
- Module 6 Exercise
- Coronavirus data analysis
- Week 3
- Summary Statistics
- Descriptive statistics
- Data import for descriptive statistics
- Creating lists for descriptive statistics
- Point estimates
- Measures of dispersion
- Data Visualization
- Data import for visualization
- Scatter plots
- Box plots
- Histograms
- Bar and pie charts
- Distributions
- Probability
- PDF and CDF
- Discrete distributions
- Continuous distributions
- Sampling distributions
- Simulated data
- 01: Introduction to neural networks
- 02: Introduction to machine learning
- 03: The fundamentals
- 04: Basic framework of a neural network
- 05: Layers in a neural network
- 06: Reviewing a neural network
- 07: From inputs to predictions
- 08: Finding a solution
- Welcome to Module 7
- Module 7 Notebook
- Module 7 Exercise
- Welcome to Module 8
- Module 8 Notebook
- Module 8 Exercise
- Welcome to Module 9
- Module 9 Notebook
- Module 9 Exercise
- Neural networks in the Wolfram language
- Honors: Deep learning basics
- Week 4
- Inferential Statistics
- Linear regression
- Importing data
- Descriptive statistics and visualization
- Linear model
- Comparing means
- Data import
- Comparing two means
- Comparing more than two means
- Comparing categorical variables
- Contingency tables
- Chi-squared test
- Creating a Computational Essay
- Data import
- Main research question
- Secondary research questions
- Congratulations on reaching the end
- 09: Introduction to Wolfram Language machine learning
- 10: Automated Machine Learning
- 11: Running an automated algorithm
- 12: Testing the automated algorithm
- 13: Setting the method to neural network
- 14: Normalizing the data
- 15: Manually created neural networks
- 16: Regression - part 1
- 17: Regression - part 2
- 18: Regression - part 3
- Welcome to Module 10
- Module 10 Notebook
- Module 10 Exercise
- Welcome to Module 11
- Module 11 Notebook
- Module 11 Exercise
- Welcome to Module 12
- Module 12 Notebook
- Module 12 Exercise
- Welcome to Module 13
- Module 13 Notebook
- Final Exam Instructions
- Continuing your journey with deep neural networks
- Honors: Deep learning functions
Summary of User Reviews
The Clinical Research and Biostatistics course on Coursera is highly recommended by users. They found the course to be comprehensive in its coverage of the subject matter, easy to understand, and informative.Key Aspect Users Liked About This Course
Many users appreciated the practical examples and case studies presented in the course, which helped them apply the concepts to real-world scenarios.Pros from User Reviews
- Comprehensive coverage of topics
- Easy to understand explanations
- Practical examples and case studies
- Engaging and interactive lectures
- Flexible schedule and pacing
Cons from User Reviews
- Some users found the course to be too basic or introductory
- Limited interaction with instructors
- Not enough opportunities for hands-on practice
- Some technical issues with the online platform
- Some users found the assessments to be too easy
Juan H Klopper
4.7 Raiting
Doing Clinical Research: Biostatistics with the Wolfram Language
- 4.7
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This course has a singular and clear aim, to empower you to do statistical tests, ready for incorporation into your dissertations, research papers, and presentations....
