Data Science on Google Cloud Platform: Exploratory Data Analytics

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Kumaran Ponnambalam

57:30

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01 - Introduction

01 - Why EDA on Datalab.mp4

01:02

02 - Data science modules covered.mp4

02:11

02 - 1. Exploration Options in GCP

01 - BigQuery.mp4

02:32

02 - Datalab.mp4

02:14

03 - Data Studio.mp4

02:10

04 - Cloud Dataflow.mp4

02:17

03 - 2. Cloud Datalab Basics

01 - What is Datalab.mp4

01:03

02 - Setting up the Cloud SDK.mp4

03:11

03 - Setting up Datalab.mp4

03:03

04 - Managing Datalab.mp4

02:12

05 - Using the exercise files.mp4

01:12

06 - Other capabilities.mp4

00:58

04 - 3. Datalab BigQuery

01 - Setting up BigQuery.mp4

03:15

02 - BigQuery commands.mp4

01:36

03 - Reading data from BigQuery.mp4

01:13

04 - Working with DataFrames.mp4

01:29

05 - Writing data to BigQuery.mp4

01:30

05 - 4. Datalab Cloud Storage

01 - Listing bucket contents.mp4

02:38

02 - Managing buckets.mp4

01:18

03 - Reading objects from a bucket.mp4

01:15

04 - Writing to buckets.mp4

01:26

06 - 5. Datalab Visualizations

01 - Introduction to the charting API.mp4

01:13

02 - Line charts with BigQuery data.mp4

01:29

03 - Pie charts with BigQuery data.mp4

00:57

04 - Time series analysis with Cloud Storage.mp4

01:45

07 - 6. EDA with GCP Use Case

01 - Loading data into a DataFrame.mp4

01:30

02 - Cleansing and transforming data.mp4

01:18

03 - Statistics and correlations.mp4

01:37

04 - Segmentation and profiling.mp4

01:15

05 - Writing results to Cloud Storage.mp4

01:05

08 - 7. Managing Datalab

01 - Datalab instance management.mp4

01:13

02 - Adding new packages.mp4

01:08

03 - Managing source code.mp4

01:16

04 - Datalab best practices.mp4

01:17

09 - Conclusion

01 - Next steps.mp4

00:42

Description

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.

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Data Science

Data Analysis

Google Cloud

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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