About this Course

Data science plays an important role in many industries. In facing massive amount of heterogeneous data, scalable machine learning and data mining algorithms and systems 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.

Course Cost
Free
Skill Level
advanced
Included in Product

Rich Learning Content

Interactive Quizzes

Taught by Industry Pros

Self-Paced Learning

Student Support Community

Join the Path to Greatness

This course is your first step towards a new career with the Become a Machine Learning Engineer Program.

Free Course

Big Data Analytics in Healthcare

byGeorgia Institute of Technology

Enhance your skill set and boost your hirability through innovative, independent learning.

Icon steps
 
 

Course Leads

Jimeng Sun

Jimeng Sun

Instructor

David Joyner

David Joyner

Instructor

What You Will Learn

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).

See the Technology Requirements for using Udacity.

Why Take This Course

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 do I get?
Instructor videosLearn by doing exercisesTaught by industry professionals