Resumo do curso

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.

Valor do curso
Grátis
Tempo estimado Tempo total entre hoje e dia da formatura depende do seu compromisso semanal. Em média, os nossos graduados completam este nanodegree em 13 semanas
13 semanas
Nível
avançado
O curso inclui

Videoaulas

Testes interativos

Aulas com profissionais do setor

Ritmo individual de aprendizado

Comunidade de apoio aos alunos

Sua jornada de aprendizagem

Este curso aberto é seu primeiro passo em direção a uma nova carreira com o programa Engenheiro de Machine Learning

Curso Aberto

CSE 8803 Special Topics: Big Data

por Georgia Tech

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

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Jimeng Sun
Jimeng Sun

Pré-requisitos

  • 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).

Por que fazer este curso?

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

Quais são os recursos?
Vídeos dos instrutores Exercícios práticos Aulas com profissionais do setor