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Instructor
John Sukup, Instructor - An Introduction to Diagnosing Diseases with Patient Data

John Sukup

Has 11 years experience in research and data analytics with The Nielsen Company, BlueCross BlueShield, and others. His experience span across several industries including healthcare, CPG, energy, financial services, insurance, consumer tech, and manufacturing. He has expertise in data modeling, Big Data solutions consulting, Python, R, Microsoft Cognitive Services, etc.. John holds a M.S. & B.S. from Purdue University and several professional certifications.

Instructor: John Sukup

  • Learn modern tools for diagnosing disease with patient data.
  • The course covers how Big Data is impacting healthcare.
  • Instructor has 11 years of experience in research and data analytics with The Nielsen Company, Blue Cross Blue Shield, and others.

Duration: 5h 30m

Course Description

This course gives beginners an introduction to modern tools for diagnosing disease with patient data. The course covers how Big Data is impacting healthcare, statistical tools for modeling patient data, some applications of these tools, and the future of healthcare (and philosophical implications) regarding AI.

What am I going to get from this course?

Analytic techniques for patient diagnosis, foundational concepts of big data, big data solutions in modern healthcare

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Basic statistics/probability, basic data analytics practices, some (Python) programming knowledge

Who should take this course? Who should not?

Executives, Analysts, Providers, Insurers, and Ancillary Professionals in Healthcare

Curriculum

Module 1: Introduction

Lecture 1 Course Overview

Module 2: Making Sense of (Big) Data

Lecture 2 Data in the 21st Century: Where is It All Coming From? 01
Lecture 3 Data in the 21st century: Where is it all coming from? 02
Lecture 4 What makes “big data” big? 01
Lecture 5 What makes “big data” big? 02
Lecture 6 What makes “big data” big? 03
Lecture 7 The importance of data in disease diagnosis: Can an algorithm beat a doctor?
Quiz 1 Making Sense of (Big) Data

Module 3: Techniques to Model and Learn from Data

Lecture 8 How does a statistic “predict” and a machine “learn?” 01
Lecture 9 How does a statistic “predict” and a machine “learn?” 02
Lecture 10 How does a statistic “predict” and a machine “learn?” 03
Lecture 11 Methods to make sense of data 01
Lecture 12 Methods to make sense of data 02
Lecture 13 Methods to make sense of data 03
Lecture 14 Methods to make sense of data 04
Lecture 15 Methods to make sense of data 05
Lecture 16 Evaluating the effectiveness of models in predicting disease 01
Lecture 17 Evaluating the effectiveness of models in predicting disease 02
Lecture 18 Evaluating the effectiveness of models in predicting disease 03
Quiz 2 Techniques to Model and Learn from Data

Module 4: Applications in Patient Disease Diagnosis

Lecture 19 Cancer and heart disease: Predicting major causes of death in the U.S. 01
Lecture 20 Cancer and heart disease: Predicting major causes of death in the U.S. 02
Lecture 21 Population Health Management: A big picture approach to epidemiology
Quiz 3 Applications in Patient Disease Diagnosis

Module 5: Future Applications in Healthcare

Lecture 22 Advanced methods for disease prediction: Neural Networks and AI 01
Lecture 23 Advanced methods for disease prediction: Neural Networks and AI 02
Lecture 24 Wearables and Internet of Things: How do they fit in the future of disease prevention and diagnosis?
Lecture 25 The robot will see you now: Acceptance and ethical concerns of data- driven disease diagnosis
Quiz 4 Future Applications in Healthcare

Module 6: Course Wrap-Up

Lecture 26 Course Summary and Key Point Review
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