An Introduction to Healthcare Informatics: Building Data-Driven Tools (Paperback)

Section 1: Accessing Data 1. The Healthcare IT Landscape 2. Example Project Part 1: Defining Need and Conceptualizing 3. Relational Databases 4. SQL 5. Non-Relational Databases 6. MUMPS 7. Connecting to and Querying a Database 8. Example Project Part 2: Gathering Data 9. Regulatory Considerations: HIPAA and Privacy

Section 2: Introduction to Data Analysis 10. The Role of Data Analysis 11. Introduction to Python 12. Introduction to R 13. Connecting to Databases with Python and R 14. Data Science: Assessing Data Quality and Attributes 15. Data Science: Basic Modeling with Regression, Classification and Clustering 16. Interactive Computing with Jupyter and Zeppelin 17. Example Project Part 3: Hypothesis Testing and Modeling 18. Specialized Techniques: Introduction to Machine Learning, Deep Learning and Artificial Intelligence

Section 3: The Big Data Landscape 19. Big Data vs Small Data 20. Overview of Big Data Tools: Hadoop, HDFS and MapReduce 21. Overview of Big Data Tools: SQL on Hadoop with Impala and Hive 22. Overview of Big Data Tools: Spark 23. Example Project Part 4: Using Big Data 24. Overview of Big Data Tools: Kafka, Flink and Spark Streaming 25. Example Project Part 5: Integrating Streaming and IoT Data

Section 4: Basics of Application Architecture 26. Infrastructure: Servers and Networking 27. Infrastructure: Virtualization, Containerization and Microservices 28. Infrastructure: Hybrid and Full Cloud 29. Example Project Part 6: Implementing in the Cloud 30. Infrastructure: Introduction to Serverless Computing

Section 5: Where to Go Next 31. Specialization: Healthcare Informatics Data Scientist 32. Specialization: Healthcare Informatics Data Architect 33. Specialization: Healthcare Informatics Data Developer 34. Being a Healthcare Innovator