Scientific Data Processing
Advanced · 7 Labs · 44 Credits · 5h 45mUse Case (Advanced)
Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
In this lab you spin up a virtual machine, configure its security, access it remotely, and then carry out the steps of an ingest-transform-and-publish data pipeline manually. This lab is part of a series of labs on processing scientific data.
introductory 1 Credit 40 Minutes
In this lab you analyze historical weather observations using BigQuery and use weather data in conjunction with other datasets. This lab is part of a series of labs on processing scientific data.
fundamental 5 Credits 35 Minutes
In this lab, you will learn how to use Apache Spark on Cloud Dataproc to distribute a computationally intensive image processing task onto a cluster of machines.
advanced 7 Credits 1 Hour
In this lab you process Landsat data in a distributed manner using Apache Beam and Cloud Dataflow. This lab is part of a series of labs on processing scientific data.
advanced 7 Credits 45 Minutes
In this lab you analyze a large (137 million rows) natality dataset using Google BigQuery and Cloud Datalab. This lab is part of a series of labs on processing scientific data.
advanced 7 Credits 30 Minutes
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
expert 9 Credits 1 Hour 30 Minutes
In this lab, you carry out a transfer learning example based on Inception-v3 image recognition neural network. The objective is to classify coastline images captured using drones based on their potential for flood damage.
expert 9 Credits 1 Hour 25 Minutes