In this course you will learn how to:
- Organize the predictive modeling task and data flow
- Develop machine learning models with linear and logistic regression, and with decision tree and ensemble algorithms using R
- Assess the performance of these models with holdout data
- Apply predictive models to generate predictions for new data
- Use various R packages to implement the models in the course
Who Should Take This Course
Healthcare providers (both administrative and medical), healthcare insurers and government health agencies.
- Key considerations of data mining in the healthcare context
- What is supervised learning
- Data partitioning and holdout samples
- Choosing variables (features)
- Handling missing data
- Assessing models
- Confusion matrix
- Misclassification costs
Linear and Logistic Regression
- Fitting a linear regression model
- Fitting a logistic regression model
- Assessing model performance
- Case study – opioid use prediction
Decision Trees and Ensembles
- Full Bayes classifier
- Naive Bayes classifier
- Classification and Regression Trees (CART)
- Growing the tree
- Avoiding overfit – pruning
- Using trees for classifications and predictions
- Random forests
- Boosted trees
Bias and Unfairness
- Why it happens
- The healthcare context
- The role of “black box” models
- Interpretability methods can help
- Auditing for fairness
You should be comfortable with using R to fit models.
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This program has been a life and work game changer for me. Within 2 weeks of taking this class, I was able to produce far more than I ever had before.
The material covered in the Analytics for Data Science Certificate will be indispensable in my work. I can’t wait to take other courses. Great work!
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
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Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and end of course data modeling project.
In addition to assigned readings, this course also has supplemental video lectures.
The required text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: “Predictive Analytics 2 – Neural Nets and Regression – with R” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R.” Additional readings with specific application to healthcare will be provided.
This is a hands-on course, and participants will apply data mining algorithms to real data. The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
Take a 10-question quiz on analytics: Test Yourself
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
- Navidys (for colorblindness, dyslexia, and reading difficulties)
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