Data Science for Healthcare Training Overview
How does data science fit into high-value healthcare analytics? What are the differences between Machine Learning (ML) and Artificial Intelligence (AI)? This live, online Executive Overview of Data Science for Healthcare Data training is a workshop-style briefing and discusses how data science fits into the healthcare landscape, demystifies data science buzzwords, compares data science programming languages like R and Python, and more. Participants are given a thorough overview of data science concepts and then complete hands-on exercises with their instructor.
Location and Pricing
This course is taught as a private, live online class for teams of 3 or more. All our courses are hands-on, instructor-led, and tailored to fit your group’s goals and needs. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for online corporate training, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Understand how data science fits into the existing landscape of traditional biostatistics, epidemiology, and informatics
- Place the phrase ‘data science’ in the broader context of implementing high-value healthcare analytics
- Describe the changing data environment that has motivated this shift
- Understand the definitions and intuition of key elements of data science such as machine learning and distributed computing
- Differentiate machine learning from deep learning/AI techniques
- Contrast the differences and similarities of open source analytic solutions like R and Python with commercial software such as SAS and SPSS
- Identify the different roles and related skillsets required to implement high-value data science workflows from a team management perspective
Prerequisites
No prior experience is presumed.
Outline
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Using Data to Solve Healthcare Issues (What changed and how did we get here?)
- New data sources and new demands on data insight
- The democratization of data science tools
- What changed in the past 10 years; why ‘data science’?
- Coming up with definitions for data science: operational and conceptual
- How does data science differ from ‘traditional’ biostatistics, informatics, or epidemiology?
Implementing High-value Data Science in the Organization
- Is big data the right data?
- Building the right data infrastructure
- Data versus insights, interesting reports versus high-value products
- Defining value in data science products
- The cost of low-value data science
- The typical data science team
- Integrating human-centered design principles to increase the value of these products
Understanding Explanatory Models
- P-values and hypothesis testing
- Correlation versus causation, observational versus experimental data
- Multivariable modeling approaches to explain the relationship between inputs and outputs
- Assumptions for causal inference and associated interpretation
- Bayesian modeling: turning the traditional paradigm around
Developing Predictive Modeling with Machine Learning
- Clustering versus Supervised models
- Classification versus Regression
- Regression example in-depth with example code
- Validation strategies for avoiding overfitting, understanding model capacity
- Different families of algorithms: high-level overview
- Classification example in-depth with example code
- Understanding accuracy: what do these measures mean?
- Clustering in-depth: use cases and explaining output
- Clustering on treatment effects: does the exposure cause a different reaction in different people?
Deep Learning and AI
- What is a neural network? How is it different from other ML?
- Artificial feed-forward neural networks and applications
- Neural networks for time series data (recurrent neural networks and convolutional neural networks)
- Neural networks for natural language processing
- Predictive modeling for image classification
Building and Maintaining a Highly Effective Data Science Team
- Traits of high performing (and low performing) organizational analytic cultures
- What cultural shifts are required for your department?
- Roles on the data science team:
- Data architects and engineers (organize, move, and store data)
- Data managers (extract and transform data for use)
- Analysts/statisticians (answer questions using data for insight)
- Topical experts (subject matter experts)
- Identify roles/skillsets for each of these workflows
- Combining these skills and roles into a single team
- Training trajectories for core members of these teams (who needs what)
- Hiring strategies to build successful data science teams
- Developing training opportunities for staff doing work in data science
- Hardware/software infrastructure required
Conclusion
Training Materials
All Data Science for Executives training students receive comprehensive courseware.
Software Requirements
Detailed setup will be provided upon request.
