Want to learn how to query and process petabytes of data in just seconds? Curious about data analysis that automatically scales as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization will make professionals learn how to derive insights through data analysis and visualization using the Google Cloud Platform. From diverse Google BigQuery datasets, the professionals will explore, visualize, and extract insights on interactive scenarios and lab implementation. The courses also cover data visualization, data loading, schema modeling, querying, query pricing and optimizing performance. This specialization is intended for the following professionals: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend professionals have some proficiency with ANSI SQL.


Radiant Techlearning offers From Data to Insights with Google Cloud Platform training program in Classroom & Virtual Instructor Led / online mode.


Duration: 3 Days


Learning Objectives:

  • Data Infrastructure
  • Machine Learning


Audience Profile:

  • Data Analysts
  • Business Analysts
  • Business Intelligence professionals
  • Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform

Course Content

The course includes presentations, demonstrations, and hands-on labs.


Module 1: Introduction to the Google Cloud Data

  • Highlight Analytics Challenges Faced by Data Analysts
  • Compare Big Data On-Premises vs. on the Cloud
  • Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud
  • Navigate Google Cloud Platform Project Basics
  • Lab: Getting started with Google Cloud Platform


Module 2: Big Data Tools Overview

  • Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools
  • Demo: Analyze 10 Billion Records with Google BigQuery
  • Explore 9 Fundamental Google BigQuery Features
  • Compare GCP Tools for Analysts, Data Scientists, and Data Engineers
  • Lab: Exploring Datasets with Google BigQuery


Module 3: Exploring your Data with SQL

  • Compare Common Data Exploration Techniques
  • Learn How to Code High Quality Standard SQL
  • Explore Google BigQuery Public Datasets
  • Visualization Preview: Google Data Studio
  • Lab: Troubleshoot Common SQL Errors


Module 4: Google BigQuery Pricing

  • Walkthrough of a BigQuery Job
  • Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs
  • Optimize Queries for Cost
  • Lab: Calculate Google BigQuery Pricing


Module 5: Cleaning and Transforming your Data

  • Examine the 5 Principles of Dataset Integrity
  • Characterize Dataset Shape and Skew
  • Clean and Transform Data using SQL
  • Clean and Transform Data using a new UI: Introducing Cloud Dataprep
  • Lab: Explore and Shape Data with Cloud Dataprep


Module 6: Storing and Exporting Data

  • Compare Permanent vs. Temporary Tables
  • Save and Export Query Results
  • Performance Preview: Query Cache
  • Lab: Creating new Permanent Tables


Module 7: Ingesting New Datasets into Google BigQuery

  • Query from External Data Sources
  • Avoid Data Ingesting Pitfalls
  • Ingest New Data into Permanent Tables
  • Discuss Streaming Inserts
  • Lab: Ingesting and Querying New Datasets


Module 8: Data Visualization

  • Overview of Data Visualization Principles
  • Exploratory vs Explanatory Analysis Approaches
  • Demo: Google Data Studio UI
  • Connect Google Data Studio to Google BigQuery
  • Lab: Exploring a Dataset in Google Data Studio


Module 9: Joining and Merging Datasets

  • Merge Historical Data Tables with UNION
  • Introduce Table Wildcards for Easy Merges
  • Review Data Schemas: Linking Data Across Multiple Tables
  • Walkthrough JOIN Examples and Pitfalls
  • Lab: Join and Union Data from Multiple Tables


Module 10: Advanced Functions and Clauses

  • Review SQL Case Statements
  • Introduce Analytical Window Functions
  • Safeguard Data with One-Way Field Encryption
  • Discuss Effective Sub-query and CTE design
  • Compare SQL and JavaScript UDFs
  • Lab: Deriving Insights with Advanced SQL Functions


Module 11: Schema Design and Nested Data Structures

  • Compare Google BigQuery vs. Traditional RDBMS Data Architecture
  • Normalization vs. Denormalization: Performance Tradeoffs
  • Schema Review: The Good, The Bad, and The Ugly
  • Arrays and Nested Data in Google BigQuery
  • Lab: Querying Nested and Repeated Data


Module 12: More Visualization with Google Data Studio

  • Create Case Statements and Calculated Fields
  • Avoid Performance Pitfalls with Cache considerations
  • Share Dashboards and Discuss Data Access considerations


Module 13: Optimizing for Performance

  • Avoid Google BigQuery Performance Pitfalls
  • Prevent Hotspots in your Data
  • Diagnose Performance Issues with the Query Explanation map
  • Lab: Optimizing and Troubleshooting Query Performance


Module 14: Advanced Insights

  • Introducing Cloud Datalab
  • Cloud Datalab Notebooks and Cells
  • Benefits of Cloud Datalab


Module 15: Data Access

  • Compare IAM and BigQuery Dataset Roles
  • Avoid Access Pitfalls
  • Review Members, Roles, Organizations, Account Administration, and Service Accounts


Q: What are data visualization techniques?


A: Some of the essential data visualization techniques are listed below:-

  • Know Your Audience.
  • Set Your Goals.
  • Choose The Right Chart Type.
  • Take Advantage Of Color Theory.
  • Handle Your Big Data.
  • Use Ordering, Layout, And Hierarchy To Prioritize.
  • Utilize Word Clouds And Network Diagrams.
  • Include Comparisons.


Q: What is the difference between data mining and data profiling?


A: Data Mining refers to the analysis of data with respect to finding relations that have not been discovered earlier. It generally focuses on the detection of unusual records, dependencies and cluster analysis. Data Profiling refers to the process of analysing individual attributes of data. It generally focuses on providing valuable information on data attributes such as data type, frequency etc.


Q: What are the important steps in the data validation process?


A: In data validation two processes are involved and i.e. data screening and data verification.

  • In Data Screening different kinds of algorithms are used in this step to screen the entire data to find out any inaccurate values.
  • In Data Verification process each and every suspected value is evaluated on multiple use-cases, and then a final decision is taken on whether the value has to be included in the data or not


Q: What is the benefit of doing training from Radiant Techlearning?


A: Radiant Techlearning is receptive to new ideas and always believes in a creative approach that makes learning easy and effective. We stand strong with highly qualified & certified technology Consultants, trainers and developers who believe in amalgamation of practical and creative training to groom the technical skills.

Our training programs are practical oriented with 70% – 80% hands on the training technology tool.  Our training program focuses on one-on-one interaction with each participant, latest content in curriculum, real time projects and case studies during the training program.

Our experts will also share best practices & will give you guidance to score high & perform better in your certification exams. To ensure your success, we provide support session even after the training program.  You would also be awarded with a course completion certificate recognized by the industry after completion of the course & the assignment.


Q: How is the Radiant Techlearning verified certificate awarded?


A: Radiant awards course completion certificate to all the participants who have completed the training program which includes various real time projects, assignments, quizzes and some other tasks.  Once the course is done you would be assigned with a project which you would have to submit in 2 weeks’ time.

Radiant Techlearning experts will be evaluating the project on various parameter. To be eligible for the verified certificate you would have to score more than 60% marks.

Only after completion of these criteria you would be awarded with Radiant verified certificate and which the participants can use for their future job purpose.

Participants will be awarded with grades according to the following criteria:

  • 90% – 100% – AAA+
  • 80% – 90% – AA+
  • 70% – 80% – A+
  • 60% – 70% – A


Q: Is there any job assistant guarantee?


A: No. These training programs are helpful to improve your skills & knowledge on the technology which would help you to land in your dream job by learning them.

Our training program will maximize your ability and chances of getting a successful job. You have to select job according to your convenience. Your performance in the training program and interview is crucial for getting good job.


Q: How I will be accessing the labs?


A: Radiant Techlearning has a data center containing the Virtual Training environment for the purpose of participant’s hand-on-practice.

Participants can easily access these labs over Cloud with the help of remote desktop connection.

Radiant virtual labs provides you the flexibility to learn from anywhere in the world and in any time zone.

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