Google BigQuery & PostgreSQL : Big Query for Data Analysis

Google BigQuery & PostgreSQL : Big Query for Data Analysis

Google BigQuery & PostgreSQL : Big Query for Data Analysis

Become BigQuery expert by mastering Google BigQuery for data analysis. Cover all SQL qureies in PostgeSQL & Big Query

Language: english

Note: 4.6/5 (1,028 notes) 154,742 students

Instructor(s): Start-Tech Academy

Last update: 2022-08-23

What you’ll learn

  • Knowledge of all the essential SQL commands in BigQuery and PostgreSQL
  • Become proficient in SQL tools like GROUP BY, JOINS and Subqueries
  • Become competent in using sorting and filtering commands in SQL

 

Requirements

  • Just a PC with any web browser

 

Description

6 Reasons why you should choose this PostgreSQL and BigQuery course

  1. Carefully designed curriculum teaching you everything in SQL and Google BigQuery that you will need for Data analysis in businesses

  2. Comprehensive – covers basic and advanced SQL statements in both PostgreSQL and Google BigQuery

  3. Business related examples and case studies on SQL and Google BigQuery

  4. Ample practice exercises on Google BigQuery because SQL and Google BigQuery require practice

  5. Downloadable resources on SQL and Google BigQuery

  6. Your queries will be responded by the Instructor himself

A Verifiable Certificate of Completion is presented to all students who undertake this SQL and Google BigQuery course.

Why should you choose this course?

This is a complete tutorial on Google BigQuery and PostgreSQL which can be completed within a weekend. SQL is the most sought-after skill for Data analysis roles in all the companies. Google BigQuery is also in high demand in data analysis field. So whether you want to start a career as a data scientist or just grow you data analysis skills, or just want to learn Google BigQuery this course will cover everything you need to know to do that.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. Instructors of the course have been teaching Data Science and Machine Learning for over a decade. We have experience in teaching and using Google BigQuery and PostgreSQL for data analysis purposes.

We are also the creators of some of the most popular online courses – with over 400,000 students and thousands of 5-star reviews like these ones:

I had an awesome moment taking this course. It broaden my knowledge more on the power use of SQL as an analytical tools. Kudos to the instructor! – Sikiru

Very insightful, learning very nifty tricks and enough detail to make it stick in your mind. – Armand

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, Google BigQuery, PostgreSQL, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Quizzes, and complete Assignments

With each lecture, there is a practice sheet attached for you to follow along. You can also take quizzes to check your understanding of concepts on Google BigQuery and PostgreSQL. Each section contains a practice assignment for you to practically implement your learning on Google BigQuery and PostgreSQL. Solution to Assignment is also shared so that you can review your performance.

By the end of this course, your confidence in using Google BigQuery and PostgreSQL will soar. You’ll have a thorough understanding of how to use Google BigQuery and PostgreSQL for Data analytics as a career opportunity.

Go ahead and click the enroll button, and I’ll see you in lesson 1 of this Google BigQuery and PostgreSQL course.

Cheers

Start-Tech Academy


FAQ’s

Why learn SQL?

  1. SQL is the most universal and common used database language.It powers the most commonly used database engines like PostgreSQL, SQL Server, SQLite, and MySQL. Simply put,If you want to access databases then yes, you need to know SQL.

  2. It is not really difficult to learn SQL. SQL is not a programming language, it’s a query language. The primary objective where SQL was created was to give the possibility to common people get interested data from database. It is also an English like language so anyone who can use English at a basic level can write SQL query easily.

  3. SQL is one of the most sought-after skills by hiring employers.

  4. You can earn good money

How much time does it take to learn SQL?

SQL is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn SQL quickly starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to learn SQL quickly.

What are the steps I should follow to learn SQL?

  1. Start learning from the basics of SQL. The first 10 sections of the course cover the basics.

  2. Once done with the basics, try your hands on advanced SQL. Next 10 sections cover Advanced topics

  3. Practice your learning on the exercise provided in every section.

What’s the difference between SQL and PostgreSQL?

SQL is a language. Specifically, the “Structured Query Language”

PostgreSQL is one of several database systems, or RDMS (Relational Database Management System). PostgresSQL is one of several RDMS’s, others of which are Oracle, Informix, MySQL, and MSQL.

All of these RDMSs use SQL as their language. Each of them have minor variations in the “dialect” of SQL that they use, but it’s all still SQL.

What is BigQuery used for?

BigQuery is a web service from Google that is used for handling or analyzing big data. Google BigQuery is part of the Google Cloud Platform. As a NoOps (no operations) data analytics service, Google BigQuery offers users the ability to manage data using fast SQL-like queries for real-time analysis.

Is BigQuery free?

For users of Google BigQuery the first 10GB of storage per month is free and the first 1TB of query per month is free. Post these limits, Google BigQuery is chargeable.

Which is better, PostgreSQL or MySQL?

Both are excellent products with unique strengths, and the choice is often a matter of personal preference.

PostgreSQL offers overall features for traditional database applications, while MySQL focuses on faster performance for Web-based applications.

Open source development will bring more features to subsequent releases of both databases.

Who uses these databases?

Here are a few examples of companies that use PostgreSQL: Apple, BioPharm, Etsy, IMDB, Macworld, Debian, Fujitsu, Red Hat, Sun Microsystem, Cisco, Skype.

Google BigQuery is used by companies such as Spotify, The New York Times, Stack Etc.

 

Who this course is for

  • Working Professionals beginning their Data journey
  • Anyone curious to master SQL from beginner to Advanced in short span of time

 

Course content

  • Introduction
    • Welcome to the Course
    • Course Flow
  • Installation and getting started
    • Course Resources
    • This is a milestone!
    • Installing PostgreSQL and pgAdmin in your PC
    • If pgAdmin is not opening…
    • Setting up BigQuery on Google Cloud Platform
    • BigQuery Interface
  • Fundamental SQL statements
    • CREATE
    • CREATE in BigQuery
    • Exercise 1: Create DB and Table
    • INSERT
    • INSERT in BigQuery
    • Import data from File
    • Importing data from File using BigQuery Web User Interface
    • File Upload in Google Big Query through Google cloud sdk
    • Importing data from Google Drive
    • Exercise 2: Inserting and Importing
    • SELECT
    • SELECT in BigQuery
    • SELECT DISTINCT
    • SELECT DISTINCT in BigQuery
    • WHERE
    • WHERE in BigQuery
    • Logical Operators – AND, OR, NOT
    • Logical Operators in BigQuery
    • Exercise 3: SELECT & WHERE
    • UPDATE
    • UPDATE in BigQuery
    • DELETE
    • DELETE in BigQuery
    • ALTER
    • ALTER in BigQuery
    • Exercise 4: Updating Table
  • Restore and Back-up
    • Restore and Back-up
    • Debugging Restoration
    • Creating DB using CSV files
    • Data Set creation in BigQuery
    • Exercise 5: Restore and Back-up
  • Selection commands: Filtering
    • IN
    • IN in BigQuery
    • BETWEEN
    • BETWEEN in BigQuery
    • LIKE
    • LIKE in BigQuery
    • Exercise 6: In, Like & Between
  • Selection commands: Ordering
    • ORDER BY
    • ORDER BY in BigQuery
    • LIMIT
    • LIMIT in BigQuery
    • Exercise 7: Sorting
  • Alias
    • AS
    • AS in BigQuery
  • Aggregate Commands
    • COUNT
    • COUNT in BigQuery
    • SUM
    • SUM in BigQuery
    • AVERAGE
    • AVERAGE in BigQuery
    • MIN MAX
    • MIN MAX in BigQuery
    • Exercise 8: Aggregate functions
  • Group By Commands
    • GROUP BY
    • GROUP BY in BigQuery
    • HAVING
    • HAVING in BigQuery
    • Exercise 9: Group By
  • Conditional Statement
    • CASE WHEN
    • CASE WHEN in BigQuery
  • JOINS
    • Introduction to Joins
    • Concepts of Joining and Combining Data
    • Preparing the data
    • Creating Datasets for Joins in BigQuery
    • Inner Join
    • INNER JOIN in BigQuery
    • Left Join
    • LEFT JOIN in BigQuery
    • Right Join
    • RIGHT JOIN in BigQuery
    • Full Outer Join
    • FULL OUTER JOIN in BigQuery
    • Cross Join
    • CROSS JOIN in BigQuery
    • Intersect and Intersect ALL
    • Except
    • EXCEPT in BigQuery
    • Union
    • UNION in BigQuery
    • Exercise 10: Joins
    • Quiz
  • SUBQUERIES
    • Subqueries
    • Subqueries in BigQuery
    • Exercise 11: Subqueries
  • Views and Indexes
    • Views
    • Views in BigQuery
    • Index
    • Index in BigQuery
    • Exercise 12: Views
  • String Functions
    • LENGTH
    • LENGTH in BigQuery
    • UPPER LOWER
    • Changing Case in BigQuery
    • REPLACE
    • REPLACE in BigQuery
    • TRIM, LTRIM, RTRIM
    • TRIM, LTRIM, RTRIM in BigQuery
    • CONCATENATION
    • CONCATENATION in BigQuery
    • SUBSTRING
    • SUBSTRING
    • LIST AGGREGATION
    • LIST AGGREGATION
    • Exercise 13: String Functions
  • Mathematical Functions
    • CEIL & FLOOR
    • CEIL & FLOOR in BigQuery
    • RANDOM
    • RANDOM in BigQuery
    • SETSEED
    • SETSEED in BigQuery
    • ROUND
    • POWER
    • POWER in BigQuery
    • Exercise 14: Mathematical Functions
  • Date-Time Functions
    • CURRENT DATE & TIME
    • CURRENT DATE & TIME in BigQuery
    • AGE
    • AGE in BigQuery
    • EXTRACT
    • EXTRACT in BigQuery
    • Exercise 15: Date-time functions
  • PATTERN (STRING) MATCHING
    • PATTERN MATCHING BASICS
    • ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS)
    • PATTERN MATCHING in BigQuery
    • Exercise 16: Pattern Matching
  • Google Data Studio for visualizing BigQuery Data
    • Google Data Studio for visualizing BigQuery Data
    • The final milestone!
  • Congratulations & about your certificate
    • Bonus Lecture

 

Google BigQuery & PostgreSQL : Big Query for Data AnalysisGoogle BigQuery & PostgreSQL : Big Query for Data Analysis

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