Learn SQL for Data Analysis with Google Big Query
4.4 (512 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,870 students enrolled

Learn SQL for Data Analysis with Google Big Query

Learn SQL using Google's scalable analytics data warehouse. Super charge your data analysis skills with BigQuery
Bestseller
4.4 (512 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
1,870 students enrolled
Created by Annabel Lyle
Last updated 9/2019
English
English [Auto]
Current price: $55.99 Original price: $79.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 2 hours on-demand video
  • 16 articles
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Use SQL for Data Analysis
  • Navigate the BigQuery User Interface & Key Features
  • Export Data in a range of formats
  • Range of SQL statement e.g SELECT, WHERE, ORDER BY, GROUP BY and more
  • Join multiple tables together with JOIN and LEFT JOIN
  • Range of SQL Analytics Functions e.g MIN(), MAX(), AVG(), SUM(), COUNT() and more
  • All hosted online. Access Google BigQuery with google account
Requirements
  • No prior SQL or database knowledge required!
Description

Learn how to use SQL with BigQuery quickly and effectively with this course!

You'll learn how to read and write complex queries to a database using one of the most in demand skills and one of the most powerful databases: Google BigQuery.

In this course you will learn:

  • How to Navigate the BigQuery User Interface and its key features

  • How to write SQL syntax including a range of statements and functions to query your data sets.

  • Transferable SQL Skills that can be used with any SQL database (Whether you’ll be using Bigquery or another database such as MySQL or Postgresql)

  • How to export your data for a varied range of use cases after you have completed your analysis.

Learning SQL is one of the fastest ways to improve your career prospects as it is one of the most in demand tech skills and one of the most important skills as a Data Analyst.

Check out the free preview videos for more information!

Who this course is for:

  • Data Analysts

  • Data Scientists

  • Engineers

  • Anyone interested in learning more about SQL, BigQuery or data analysis

As a Data Scientist with over 6 years of experience in Data Analytics, I look forward to introducing you to the world of analytics and SQL with Google BigQuery.

Who this course is for:
  • Data Analysis
  • Data Scientist
  • Engineer
  • Anyone who wants to learn SQL or Data Analysis!
Course content
Expand all 55 lectures 02:01:29
+ Introduction
8 lectures 19:20
EXERCISE - Sandbox Account
00:25
EXERCISE - Your First Data Set
00:47
Explore Tables & Column Data Types
06:02
EXERCISE - Data Types
00:22
Exploring Tables
03:41
+ Retrieve the data you need with the SELECT Statement
6 lectures 11:31
SELECT with single & multiple columns
04:57
EXERCISE - Selecting Single & Multiple Columns
00:50
SELECT with EXCEPT
01:43
EXCERCISE - SELECT with EXCEPT
00:19
Query History & Saving Queries - Find your queries again
02:55

Woop ?

You now know how to :

1. Access Query History - Phew! This means that you'll never lose a query again. BigQuery keeps track of every query that you have run in the interface so that you can run it again

2. Save Queries - You can choose to 'save query' and name your query something memorable - so that you can always go back to the 'Saved Queries' tab and find the ones that are important to you.

3. Share Saved Queries with others - If you want others to also be able to run your queries - click 'link sharing ON' and then copy the url. This will allow whoever you share it with to also run and save your query.

EXERCISE:

  • If you haven't already:

    • Find a historical query in 'query history' and select 'run in editor'

    • Save a query and then find it again under 'Saved Queries'

    • Turn link- sharing on for this saved query, and copy & paste the url into a new tab in your browser to see what the experience will be like for the person you share the link with.

EXERCISE - Query History & Saving Queries
00:46
+ Filter the data you need with the WHERE Statement
24 lectures 41:54
WHERE statement with Integers
03:42
EXERCISE - Where statement with integers
00:31
ORDER BY to order your results
03:36
EXERCISE - ORDER BY
00:30
WHERE statement with Dates
02:32

Hurrah ?

You now know how to :

1. Use the WHERE clause to filter on dates

You have to specify the date in the format of 'year-month-date'

for example '2019-01-01' if it was 1st January 2019

2. You can use the same operators as we looked at for integers on dates

for example:

install_date >= '2018-01-01'

EXERCISE:

  • Filter out bike stations that were installed before 1st April 2018. How many were there?

  • Filter out any bike stations that were not installed on exactly 31st October 2018 (Halloween)

  • and then maybe have a look at the ones that were installed on Halloween ?

If you get stuck along the way remember you can always post in the Q&A forum!

EXERCISE - WHERE statement with Dates
00:26
WHERE statement with boolean values (True/False)
01:43
EXERCISE - WHERE with boolean values
00:41
AND - to combine your filters
02:49
EXERCISE - using AND
00:11
OR - to combine your filters
02:10
EXERCISE - OR
00:19
BETWEEN - to combine your filters
03:03
EXERCISE - BETWEEN
00:23
EXERCISE - IN
00:20
WHERE statement with strings (text)
04:02
EXERCISE - WHERE with text
00:10
LIKE - filter for strings
02:39
EXERCISE - LIKE
00:21
WHERE statement with strings - handling case sensitivity
02:02
NULL
03:03
NOT - filter
02:32
WHERE - Summary
00:46
+ Calculate more metrics with Aggregate Analytical Functions
5 lectures 15:06
COUNT() - to count number or rows or values
03:44
COUNT() with DISTINCT
03:11
SUM() AND AVG() - To calculate the sum and average
02:36
MIN() and MAX()
03:50
VAR() & STD_DEV()
01:45
+ GROUP BY
5 lectures 15:22
GROUP BY statement
03:32
GROUP BY & ORDER BY
02:12
GROUP BY multiple columns
03:20
GROUP BY & ORDER BY 1,2,3
03:34
HAVING & GROUP BY
02:44
+ JOIN tables together
3 lectures 08:27
JOINING tables
04:03
Aliasing Tables
01:54
LEFT JOIN
02:30
+ EXPORT your data
2 lectures 03:34
Exporting to Google Drive or .csv
02:22
Export to Google Data Studio
01:12