Share Description
In this course, you will :
- This course, part of a series by cloud engineering specialist and data scientist Kumaran Ponnambalam, demonstrates how to use GCP for exploratory data analytics.
- Review segmentation and profiling concepts.
- Get your hands dirty as you learn to perform text and visual data analysis using GCP tools such as Cloud Datalab, BigQuery, Cloud Dataflow, and Data Studio.
- Examine an end-to-end use case that demonstrates what you've learned in the course.
Syllabus :
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
