- Setting up Cloud DataLlb for exploratory data analytics
- Segmentation and profiling
- Reading and writing data from BigQuery
- Managing cloud storage buckets
- Creating visualizations of BigQuery data with the GCP Charting API
- Managing Datalab instances
Skill Level Intermediate
- [Instructor] Data science is the key technology for any IT professional. More and more data science applications are being built today and they are built on cloud platforms, like Amazon Web Service, Google Cloud Platform, and Microsoft Azure. Cloud brings unlimited scalability and elasticity to data science. Expertise in these platforms is essential to an IT professional. In this course, I will show you the technologies available on Google Cloud Platform for exploratory data analytics that segment, refine, analyze, and visualize data in the cloud to enable data science.
You need prior familiarity with the basics of GCP platform as well as Python programming. So join me, Kumaran Ponnamalam, in my course. Let's explore and experience the options for exploratory data analytics.
Predictive Customer Analyticswith Kumaran Ponnambalam1h 37m Intermediate
1. Exploration Options in GCP
2. Cloud Datalab Basics
3. Datalab: BigQuery
4. Datalab: Cloud Storage
5. Datalab: Visualizations
6. EDA with GCP: Use Case
7. Managing Datalab
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