About this course
Data science plays an important role in many industries. In facing massive amounts of heterogeneous data, scalable machine learning and data mining algorithms and systems have become extremely important for data scientists. The growth of volume, complexity and speed in data drives the need for scalable data analytic algorithms and systems.
In this course, we study such algorithms and systems in the context of healthcare applications.
In healthcare, large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment.
In this course, we introduce the characteristics of medical data and associated data mining challenges in dealing with such data. We cover various algorithms and systems for big data analytics. We focus on studying those big data techniques in the context of concrete healthcare analytic applications such as predictive modeling, computational phenotyping and patient similarity.
What you'll learn
- Understand health data and big data analytic technology;
- Health data standards;
- Scalable machine learning algorithms such as online learning and fast similarity search;
- Big data analytic systems such as Hadoop family (Hive, Pig, HBase), Spark and Graph DB;
- Deep learning models and packages such as tensorflow.
Week 1: Intro to Big Data Analytics/Course Overview
Week 2: Predictive Modeling
Week 3: MapReduce
Week 4/5: Classification evaluation metrics/ Classification ensemble methods/ Phenotyping & Clustering
Week 6: Spark
Week 7: Medical ontology
Week 8: Graph analysis
Week 9: Dimensionality Reduction
Week 10: Patient similairty
Week 11: AWS
Week 12: AZURE
Week 13: Peer Review for Draft
Week 14: Final Project (code+presentation+ final paper)
Week 15: Final Exam Week
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