Über diesen Kurs

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.

Kursgebühren
Kostenlos
Niveau
Fortgeschrittene
Vorteile

Rich Learning Content

Interactive Quizzes

Taught by Industry Pros

Self-Paced Learning

Student Support Community

Begib' dich auf den Weg des Erfolgs

Dieser kostenlose Kurs ist der erste Schritt auf dem Weg zu einer neuen Karriere mit dem Machine Learning Engineer Programm.

Kostenlose Kurse

Big Data Analytics in Healthcare

mit Georgia Institute of Technology

Erweitere deine Fähigkeiten und Karriere durch innovatives und unabhängiges Lernen.

Icon steps 54aa753742d05d598baf005f2bb1b5bb6339a7d544b84089a1eee6acd5a8543d
 
 

Tutoren

Jimeng Sun
Jimeng Sun

Instructor

David Joyner
David Joyner

Instructor

Was du lernst

Voraussetzungen

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).

Detaillierte technische Voraussetzungen

Was spricht für diesen Kurs?

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

Was bekomme ich?
Instructor videos Learn by doing exercises Taught by industry professionals