Google Cloud for Data Scientists

Enable data scientists to ingest, analyse, visualise, and model data at scale on GCP using BigQuery, Data Studio, Dataproc, and integrated Python tooling.

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Audience: Data Analysts, Data Scientists

Duration: Three to four days depending on the agenda

Format: Lectures and hands-on labs. (50 %, 50 %)

Overview

Data Science is all the rage today, and Google is one of the major promoters of it. Google Cloud Platform (GCP) is one of the leading platforms for Data Science. In this course, the students will learn to do Data Science with Python, and the capabilities of Google Cloud specific to Data Science.

Objective

Enable data scientists to ingest, analyse, visualise, and model data at scale on GCP using BigQuery, Data Studio, Dataproc, and integrated Python tooling.

What You Will Learn

Understand Google Cloud's features for Data Science
Process of doing Data Science
Using Google Compute Engine
Using Google Cloud Storage
Visualizing data using Google Data Studio
Running SQL queries using Big Query
Data analytics with Python
Running Python code on Google Cloud
Large-scale data analytics with Apache Spark
Running Spark using Google DataProc
Machine Learning fundamentals
Spark ML library
Doing ML with Spark ML on Google Cloud

Course Details

Audience: Data Analysts, Data Scientists

Duration: Three to four days depending on the agenda

Format: Lectures and hands-on labs. (50 %, 50 %)

Prerequisites:

Interest in Data Science (overview included) • Some basic Python recommended • Some programming experience is highly recommended

Setup: Zero-Install cloud lab • Modern laptop • Unrestricted Internet • Google Cloud account highly recommended

Detailed Outline

Benefits of Cloud computing
Google Cloud ecosystem overview
Lab: Getting up and running in Google Cloud

Compute Engine Intro
Types of computing resources
Customising a cloud VM
Lab

Bringing data into the cloud
Data-storage options
Ingesting & scheduling
Lab

Overview & visualising data
Labs

Introduction
Running queries
Labs

Exploring datasets
Cleaning, feature selection, visualisation
Labs

Colab, Datalab, Jupyter
Installing packages
Labs

Spark Intro, DataFrames, SQL
Labs

Running Hadoop & Spark clusters
Labs

ML overview & algorithms
Feature engineering, regressions, classifications, clustering

Spark ML library
CPU vs GPU benchmarking
Labs

Team project on a real-world DS problem using Google Cloud