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LinkedIn Learning

Data Science on Google Cloud Platform: Exploratory Data Analytics

via LinkedIn Learning


Learn how to conduct exploratory data analytics on Google Cloud Platform.

Cloud computing brings unlimited scalability and elasticity to data science applications. Expertise in the major platforms, such as Google Cloud Platform (GCP), is essential to the IT professional. This course—one of a series by cloud engineering specialist and data scientist Kumaran Ponnambalam—shows how to conduct exploratory data analytics with GCP. First, review the concepts of segmentation and profiling. Then get hands on, as you learn to perform both text and visual analysis of data using tools provided by GCP: Cloud Datalab, BigQuery, Cloud Dataflow, and Data Studio. Finally, look at an end-to-end use case that applies what you've learned in the course.


  • Why EDA on Datalab?
  • Data science modules covered
1. Exploration Options in GCP
  • BigQuery
  • Datalab
  • Data Studio
  • Cloud Dataflow
2. Cloud Datalab Basics
  • What is Datalab?
  • Setting up the Cloud SDK
  • Setting up Datalab
  • Managing Datalab
  • Using the exercise files
  • Other capabilities
3. Datalab: BigQuery
  • Setting up BigQuery
  • BigQuery commands
  • Reading data from BigQuery
  • Working with DataFrames
  • Writing data to BigQuery
4. Datalab: Cloud Storage
  • Listing bucket contents
  • Managing buckets
  • Reading objects from a bucket
  • Writing to buckets
5. Datalab: Visualizations
  • Introduction to the charting API
  • Line charts with BigQuery data
  • Pie charts with BigQuery data
  • Time series analysis with Cloud Storage
6. EDA with GCP: Use Case
  • Loading data into a DataFrame
  • Cleansing and transforming data
  • Statistics and correlations
  • Segmentation and profiling
  • Writing results to Cloud Storage
7. Managing Datalab
  • Datalab instance management
  • Adding new packages
  • Managing source code
  • Datalab best practices
  • Next steps

Taught by

Kumaran Ponnambalam

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