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Data is multidisciplinary.

A data driven organization needs to be able to work on a number of fronts simultaneously.

Data Science and Analytics.

Data Science is an emerging discipline at the intersection of statistics, analytics, programming, and machine learning.

Programming and Software Development.

Learning to code involves learning a language, mastering new ways of expression, and becoming immersed in a culture. It enables you to express data-driven ideas and accomplish entirely new things.

DevOps.

DevOps (a mashup of "development" and "operations") is the convergence of everything. As companies seek to deliver software more rapidly, DevOps tools and practices can help in that endeavor.

Infrastructure and Big Data.

Data is powered by tools such as databases (MySQL, PostgreSQL, Oracle), NoSQL storages (Cassandra, MongoDB, Neo4j), search engines (ElasticSearch), distributed computational engines (Apache Spark, Dask), and distributed storage (HDFS, Ceph, MinIO). To be successful they must integrate and work in concert.

Data Science Foundations with Python

In a deeply connected world, data can help us to inform and empower. In this course, you will learn to use the Python programming language to gain insight from your information.

Length: 5 days
Audience: Software engineers seeking to understand analytics and work with data in Python

Apache Spark for Data Analytics

Apache Spark is the computation engine that powers big data. In this course, you will learn how to use Apache Spark to work with data, gain insight using Machine Learning, and analyze streams at scale.

Length: 3 days
Audience: Software engineers seeking to understand Spark and Big data analytics, database professionals looking to add analytics skills in a big data environment

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