Big Data Analytics in Healthcare (Udacity)

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 on 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. We also study big data analytic technology:

Scalable machine learning algorithms such as online learning and fast similarity search;

Big data analytic system such as Hadoop family (Hive, Pig, HBase), Spark and Graph DB

What You Will Learn

Lesson 1

Big Data

- Predictive Modeling

- Dimensionality Reduction & Tensor Factorization

- Graph Analysis

Lesson 2

Healthcare

- Computational Phenotyping

- Patient Similarity Metrics

- Medical Ontology

Lesson 3

Technologies

- MapReduce

- Spark

- Hadoop

Prerequisites and Requirements

Basic machine learning and data mining concepts such as classification and clustering;Proficient programming and system skills in Python, Java and Scala;Proficient knowledge and experience in dealing with data (recommended skills include SQL, NoSQL such as MongoDB).