Learn about the methodology, practices and requirements behind data science to better understand how to problem solve with data and ensure data is relevant and properly manipulated to address a variety of real-world projects and business scenarios.

A beginner's guide to big data concepts and Hadoop framework, covering data storage, processing, and analysis techniques.

In this talk, we'll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, discover pros and cons, and a list of existing technologies implementing the latter.

Explore big data processing techniques using Apache Spark and Java, focusing on data analysis, manipulation, and real-time data processing.

Learn advanced big data processing techniques with PySpark, focusing on distributed computing, data manipulation, and analytics.

In this session, you'll learn about big data.

In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK.

This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow.

In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance.

Gain expertise in using Apache Spark for large-scale data processing and analytics.

An investigation into the convergence of relational SQL database technologies from several vendors and Big Data technologies like Apache Hadoop

AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.