Top 8 Recommended BigQuery Self-Study Materials! [November 2024]

BigQuery is a popular data warehouse service that allows you to easily work with petabytes of data.  Learn how to quickly get up to speed with BigQuery and start querying and analyzing data efficiently using the BigQuery graphical user interface, command line utilities, and even programming languages.  If you are familiar with basic database concepts, like tables, you are ready to start learning one of the most important data analytics platforms available.

While some courses will focus just on using SQL with BigQuery, this course starts with the basics of signing up for Google Cloud and working the BigQuery graphical user interface (GUI), introduces SQL for BigQuery, and then moves to loading data and working with BigQuery using the command line and Python.  Perhaps most importantly, you will learn how BigQuery is different from other databases and how to use that knowledge to use BigQuery efficiently and cost effectively.

You will learn how to explore data, tables, and datasets. Write queries efficiently using BigQuery hints and formatting helps.  Work with SELECT statements, including creating FROM, WHERE, GROUP BY, HAVING, and ORDER BY clauses to create queries that answer driving questions you have about your data.  If you are not familiar with working with multiple tables and using joins, that's no problem, you will learn that in this course.

Use features of BigQuery designed to make you more productive, like Saved Queries, Exporting Data, and Execution Details that help you improve the performance of your queries.

Learn how to create tables and data sets and load data into BigQuery directly and by using Cloud Storage, Google Cloud's large scale object storage system.

While the graphical user interface, known as the BigQuery console, is an excellent tool for interactive work with BigQuery, sometimes we need to run the same queries or operations repeatedly to generate reports or download data. In this course you will learn about the bq command line utility that lets you query data and work with datasets from the command line.  If you prefer to work with Python or other programming languages, you can use the BigQuery client libraries for running queries and other jobs right from your programs and scripts. You don't even need to have Python installed on your device because we'll use CoLab, a free Google service for working with Python notebooks.

Quizzes and assignments in this course allow you to check your understanding as you progress through the course by answering questions and writing queries.

To use BigQuery effectively though, you need to understand how BigQuery is designed. Building a data warehouse and designing data models in BigQuery is fundamentally different than building and modeling in relational databases like Oracle, SQL Server and PostgreSQL. In this course, you will learn about BigQuery's architecture and how it influences how we structure and query data.

Learn insights from an instructor with decades of experience in working with data. Dan Sullivan is a Principal Data Architect and author of books and numerous articles on databases and Google Cloud. Dan is the author the Official Google Cloud Professional Data Engineer Study Guide as well as study guides for the Google Cloud Professional Cloud Architect and Associate Cloud Engineer certifications.  He has developed courses for Google Cloud, data modeling, data science, exploratory data analysis, machine learning, DevOps, and more. His courses can be found on Udemy and LinkedIn Learning.

1. Introduction to BigQuery
2. Welcome to Introduction to BigQuery
3. Overview of BigQuery
4. BigQuery Data Types
5. Key Topics to Understand about BigQuery
6. Quiz
7. BigQuery Console Quickstart
8. Signing Up to Use BigQuery
9. Introduction to the BigQuery Console
10. Using a Public Dataset
11. Working with Multiple Tables
12. Exporting Data from BigQuery
13. Saving Queries
14. Quiz
15. Using the BigQuery Console
16. Querying BigQuery Data Using SELECT Statements
17. Basic SELECT Statements
18. SELECT with a WHERE Clause
19. Using the ORDER BY Clause
20. Using the GROUP BY Clause
21. Using the WHERE, GROUP BY and ORDER BY Clauses
22. Using the HAVING Clause
23. Joining Two Tables
24. Find the name of the station with the smallest station_id
25. Find the name of the station that was the start of the longest trip duration.
26. SQL Functions
27. Working with Aggregate Functions
28. Using the ROUND Function with Numeric Data Types
29. Find average trip durations by station
30. Working with Date and Time Functions
31. Simple Math Expressions
32. Working with String Functions
33. Pretty print the names of start and end stations
34. Using the CASE Statement and Error Checking
35. Quiz
36. Creating Tables and Loading Data
37. Create Dataset and Simple Table
38. Inserting, Updating, and Deleting Data in Tables
39. Loading Data from a File
40. Creating a Cloud Storage Bucket
41. Quiz
42. BigQuery Command Line
43. Installing Cloud SDK
44. Initializing the Command Line Environent
45. Uploading Data with gsutil Command
46. Using the bq Command Line to work with BigQuery
47. Quiz
48. BigQuery and Python
49. Introduction to Colab and Using Python with BigQuery
50. Clients, Jobs and Dataframes
51. Quiz
52. BigQuery Architecture
53. Component Services that Make Up BigQuery
54. Columnar Data Storage
55. Partitioning Tables
56. Clustering
57. Performance Tuning
58. Quiz

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.

1. Introduction
2. Welcome to the Course
3. Course Flow
4. Course Resources
5. Installation and getting started
6. This is a milestone!
7. Installing PostgreSQL and pgAdmin in your System
8. If pgAdmin is not opening...
9. Setting up BigQuery on Google Cloud Platform
10. BigQuery Interface
11. Fundamental SQL statements
12. CREATE
13. CREATE in BigQuery
14. Exercise 1: Create DB and Table
15. INSERT
16. INSERT in BigQuery
17. Import data from File
18. Importing data from File using BigQuery Web User Interface
19. File Upload in Google Big Query through Google cloud sdk
20. Importing data from Google Drive
21. Exercise 2: Inserting and Importing
22. SELECT
23. Quick coding exercise on Select Statement
24. SELECT in BigQuery
25. SELECT DISTINCT
26. Quick coding exercise on Distinct Command
27. SELECT DISTINCT in BigQuery
28. WHERE
29. Quick coding exercise on Where Statement
30. WHERE in BigQuery
31. Logical Operators - AND, OR, NOT
32. Quick coding exercise on Logical Operators
33. Logical Operators in BigQuery
34. Exercise 3: SELECT & WHERE
35. UPDATE
36. Quick coding exercise on Update Command
37. UPDATE in BigQuery
38. DELETE
39. Quick coding exercise on Delete Command
40. DELETE in BigQuery
41. ALTER
42. ALTER in BigQuery
43. Exercise 4: Updating Table
44. Quiz
45. Restore and Back-up
46. Restore and Back-up
47. Debugging Restoration
48. Creating DB using CSV files
49. Data Set creation in BigQuery
50. Exercise 5: Restore and Back-up
51. Selection commands: Filtering
52. IN
53. Quick coding exercise on IN operator
54. IN in BigQuery
55. BETWEEN
56. BETWEEN in BigQuery
57. Quick coding exercise on Between Operator
58. LIKE
59. Quick coding exercise on Like operator
60. LIKE in BigQuery
61. Exercise 6: In, Like & Between
62. Quiz
63. Selection commands: Ordering
64. ORDER BY
65. Quick coding exercise on Order by Clause
66. ORDER BY in BigQuery
67. LIMIT
68. Quick coding exercise on Limit Command
69. LIMIT in BigQuery
70. Exercise 7: Sorting
71. Alias
72. AS
73. Quick coding exercise on AS operator
74. AS in BigQuery
75. Aggregate Commands
76. COUNT
77. Quick coding exercise on Count function
78. COUNT in BigQuery
79. SUM
80. Quick coding exercise on Sum function
81. SUM in BigQuery
82. AVERAGE
83. Quick coding exercise on Average function
84. AVERAGE in BigQuery
85. MIN MAX
86. Quick coding exercise on MIN & MAX function
87. MIN MAX in BigQuery
88. Exercise 8: Aggregate functions
89. Quiz
90. Group By Commands
91. GROUP BY
92. Quick coding exercise on Group By Clause
93. GROUP BY in BigQuery
94. HAVING
95. Quick coding exercise on Having Clause
96. HAVING in BigQuery
97. Exercise 9: Group By
98. Quiz
99. Conditional Statement
100. CASE WHEN
101. Quick coding exercise on CASE WHEN Statement
102. CASE WHEN in BigQuery
103. JOINS
104. Introduction to Joins
105. Concepts of Joining and Combining Data
106. Preparing the data
107. Creating Datasets for Joins in BigQuery
108. Inner Join
109. Quick coding exercise on Inner Join
110. INNER JOIN in BigQuery
111. Left Join
112. Quick coding exercise on Left Join
113. LEFT JOIN in BigQuery
114. Right Join
115. Quick coding exercise on Right Join
116. RIGHT JOIN in BigQuery
117. Full Outer Join
118. Quick coding exercise on Full Outer Join
119. FULL OUTER JOIN in BigQuery
120. Cross Join
121. Quick coding exercise on Cross Join
122. CROSS JOIN in BigQuery
123. Intersect and Intersect ALL
124. Quick coding exercise on Intersect and Intersect ALL
125. Except
126. Quick coding exercise on Except
127. EXCEPT in BigQuery
128. Union
129. Quick coding exercise on Union Operator
130. UNION in BigQuery
131. Exercise 10: Joins
132. Quiz
133. SUBQUERIES
134. Subqueries
135. Quick coding exercise on Subquery
136. Subqueries in BigQuery
137. Exercise 11: Subqueries
138. Fun way to practice SQL
139. Solve Murder Mystery using SQL
140. Views and Indexes
141. Views
142. Quick coding exercise on Views
143. Views in BigQuery
144. Index
145. Quick coding exercise on Index
146. Index in BigQuery
147. How Index works in SQL
148. Exercise 12: Views
149. Quiz
150. String Functions
151. LENGTH
152. Quick coding exercise on LENGTH function
153. LENGTH in BigQuery
154. UPPER LOWER
155. Quick coding exercise on UPPER LOWER function
156. Changing Case in BigQuery
157. REPLACE
158. Quick coding exercise on REPLACE function
159. REPLACE in BigQuery
160. TRIM, LTRIM, RTRIM
161. Quick coding exercise on TRIM, LTRIM, RTRIM functions
162. TRIM, LTRIM, RTRIM in BigQuery
163. CONCATENATION
164. Quick coding exercise on CONCATENATION function
165. CONCATENATION in BigQuery
166. SUBSTRING
167. Quick coding exercise on SUBSTRING function
168. SUBSTRING
169. LIST AGGREGATION
170. LIST AGGREGATION
171. Exercise 13: String Functions
172. Mathematical Functions
173. CEIL & FLOOR
174. Quick coding exercise on CEIL & FLOOR functions
175. CEIL & FLOOR in BigQuery
176. RANDOM
177. RANDOM in BigQuery
178. SETSEED
179. SETSEED in BigQuery
180. ROUND
181. Quick coding exercise on ROUND function
182. POWER
183. Quick coding exercise on POWER function
184. POWER in BigQuery
185. Exercise 14: Mathematical Functions
186. Quiz
187. Date-Time Functions
188. CURRENT DATE & TIME
189. Quick coding exercise on CURRENT DATE & TIME function
190. CURRENT DATE & TIME in BigQuery
191. AGE
192. AGE in BigQuery
193. EXTRACT
194. EXTRACT in BigQuery
195. Exercise 15: Date-time functions
196. Quiz
197. PATTERN (STRING) MATCHING
198. PATTERN MATCHING BASICS
199. Quick coding exercise on Pattern Matching Basics
200. ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS)

**[Updated 2024]** - This course is updated as per latest BigQuery UI and features.

Note : This Bigquery course is NOT intended to teach SQL or PostgreSQL. The focus of the course is kept to give you In-depth knowledge of Google Bigquery concepts/Internals.

"BigQuery is server-less, highly scalable, and cost-effective Data warehouse designed for Google cloud Platform (GCP) to store and query petabytes of data."

What's included in the course ?

• Brief introduction to the set of services Google Cloud provides.

• Complete In-depth knowledge of Google BigQuery concepts explained from Scratch to ADVANCE to Real-Time implementation.

• Each and every BigQuery concept is explained with HANDS-ON examples.

• Includes each and every, even thin detail of Big Query.

• Learn to interact with BigQuery using its Web Console, Bq CLI and Python Client Library.

• Create, Load, Modify and Manage BigQuery Datasets, Tables, Views, Materialized Views etc.

• *Exclusive* - Query Execution Plan, Efficient schema design, Optimization techniques, Partitioning, Clustering.

• Build and deploy end-to-end data pipelines (Batch & Stream) of Real-Time case studies in GCP.

• Services used in the pipelines- Dataflow, Apache Beam, Pub/Sub, Bigquery, Cloud storage, Data Studio, Cloud Composer/Airflow etc.

• Learn Best practices and Optimization techniques to follow in Real-Time Google Cloud BigQuery Projects.

After completing this course, you can start working on any BigQuery project with full confidence.

Add-Ons

• Questions and Queries will be answered very quickly.

• Queries and datasets used in lectures are attached in the course for your convenience.

• I am going to update it frequently, every time adding new components of Bigquery.

1. Introduction to GCP & its services
2. Introduction to Google Cloud Platform
3. GCP vs AWS vs Azure - Why choose GCP
4. Compute Services in GCP
5. Storage Services in GCP
6. Big data Services in GCP
7. AI & ML Services in GCP
8. Big data ecosystem in GCP
9. Quiz 1
10. Introduction to BigQuery
11. Conventional Datawarehouse Problems
12. What is BigQuery
13. BigQuery Out-of-the Box Features
14. Architecture of BigQuery
15. Dataset & Table creation
16. Setup a GCP account
17. Important note
18. Create a Project
19. BigQuery UI Tour
20. Create a Dataset - Part 1
21. Region Vs Multi-region
22. Create a Dataset - Part 2
23. Create a Table
24. Assignment 1
25. Using BigQuery Dashboard options
26. Running query with various Query Settings
27. Caching features & limitations
28. Querying Wildcard Tables
29. Wildcard Table Limitations
30. Schedule, Save, Share a Query
31. Schema Auto detection
32. Efficient Schema Design in BigQuery
33. Design an Efficient schema for BigQuery Tables
34. Nested & Repeated Columns
35. Quiz 2
36. Assignment 2
37. Operations on Datasets & Tables
38. Copying Datasets
39. Transfer Service for scheduling Copy Jobs
40. Modifying Table Schema - Part 1
41. Modifying Table Schema - Part 2
42. Restore Deleted data from Table
43. Execution Plan of BigQuery
44. How BigQuery creates Execution Plan of a Query
45. Understanding Execution Plan in UI Dashboard
46. Partitioned Tables in BigQuery
47. What is Partitioning & its benefits
48. Ingestion time Partitioned Tables
49. Date column Partitioned Tables
50. Integer based Partitioned Tables
51. ALTER, COPY operations on Partitioned Tables
52. DML operations on Partitioned Tables
53. Best Practices for Partitioning
54. Clustered Tables in BigQuery
55. What is Clustering
56. When to use Clustering OR Partitioning OR Both
57. Create Clustered Table
58. Dos & Don'ts for Clustering
59. Loading & Querying External Data Sources
60. Introduction and Create Cloud Storage Bucket
61. Create & Query Permanent Table on Cloud Storage bucket
62. External data source Limitations
63. Views in Bigquery
64. Introduction to Views & its Advantages
65. Create Views in BigQuery
66. Restrict rows at User level in Views
67. Limitations of Views
68. Materialized Views in BigQuery
69. What are Materialized Views
70. Create a Materialized View
71. ALTER Materialized View
72. Design an optimized query for Materialized View
73. Auto & Manual Refreshes of Materialized Views
74. Limitations & Quotas of Materialized Views
75. Best Practices in Materialized Views
76. Quiz 3
77. BQ Command Line
78. Introduction
79. Cloud SDK Setup
80. BQ Basic commands
81. BQ - Querying Commands
82. BQ- Dataset creation command
83. BQ - Create all types of Tables
84. BQ - Load data into Table
85. BQ - Exclusive operations
86. Assignment 3
87. Python Client Library of BigQuery
88. Setup
89. Python code to create dataset
90. Python code to create table
91. Python code to query tables
92. Build End-to-End Data Pipelines (Apache Beam)
93. Case Study Requirements
94. GCP approach to case study
95. Apache Beam Pipeline creation
96. Write Transformations in Beam
97. Write to BigQuery
98. Create View for Daily data
99. Python 3 code
100. Run the Beam Pipeline
101. Create Reports in Cloud DataStudio
102. Create monthly reports in DataStudio
103. Write Airflow DAG to schedule
104. Create Cloud Composer environment and run DAG
105. Build Streaming Data Pipelines
106. Introduction
107. Google Pub/Sub Architecture
108. Publish messages to Pub/Sub
109. Beam pipeline for Streaming data
110. BigQuery Pricing
111. Storage Pricing
112. Query Pricing
113. API, DML pricing
114. Free operations in BigQuery
115. Google Cloud Pricing Calculator
116. Best Practices / Optimization Techniques
117. Introduction
118. Methods to restrict data scan
119. Ways to reduce CPU time
120. Which SQL anti-patterns to avoid
121. Additional Learnings - Different File Formats & Apache Beam
122. What do we need from a File
123. Text, Sequence, Avro Files
124. RC, ORC, Parquet Files
125. Performance Test results of Various Files
126. Which File Format to choose
127. Introduction to Apache Beam
128. Batch Vs Stream processing
129. Thankyou
130. Bonus
131. Bonus

Woah, another SQL course? Yes! Here's why this one is different.

1. We write 100% of the code together and I explain everything precisely and with abundant context. I have over a decade of industry experience over have taught this at the university level.

   
   

2. 100% we do is application based. I rarely use toy data to illustrate points, unless it's more illustrative. Hopefully you'll learn much more than SQL throughout the course.

   
   

3. ZERO SET-UP. As long as you have a Google Account, you can login to BigQuery and get started immediately. No headaches configuring databases locally. You'll be up and running in under 3 minutes.

   
   

4. Spaced repetition to develop mastery. This is NOT a table of contents course. This is NOT a list of disparate exercises. Everything is connected. Concepts are revisited throughout the course so you can see them from different angles and maximize understanding.

   
   

5. Can you solve it? I provide tons of mini-challenges throughout the lecture material. The lecture material is basically us solving the problems. No time wasted on theory without context.

   
   

6. I'm not boring. I am human. I do make mistakes. I dwell on them so you can master the debugging process. Debugging is much more important than writing code.

1. Should you take this course?
2. How this course works - please watch!
3. BigQuery Set-up - Writing Our First Queries!
4. [Must Watch] - January 2023 - Pinning the Project and getting set-up
5. WATCH FIRST PLEASE: New BigQuery UI - Set-up (January 26, 2021 update)
6. [Optional - but very useful]Using ChatGPT to accelerate learning and progress.
7. [Optional - but very useful] Asking ChatGPT for Examples
8. Overview of Tables/Columns and Writing Queries
9. Anatomy of a SELECT statement
10. Our First Real Question - How Many Questions Each Tag Has?
11. Does a Question Have Multiple Tags? More aggregations, GROUPING and the HAVING
12. Everything is a Table: Subqueries, Common Table Expressions
13. Solution - All Views for Titles Containing "python" - Tricky!
14. Solution - All Questions for Each Tag, plus some more subqueries.
15. The CASE Statement - Quick Case study using StackOverflow Questions
16. Using CASE Statements to Classify Stack Overflow Questions
17. Solution - Using Question Titles And Tags in our CASE statement
18. Solution - Programming Language Growth Using Stack Overflow Data
19. Using Python to Generate Larger SQL Statements (Optional)
20. Join me and learn to JOIN data + Window/Analytic Functions
21. INNER, LEFT, RIGHT, OUTER JOIN's - in a nutshell
22. JOIN'ing Order and Customers - How to identify JOIN column candidates
23. Solution - Most Popular Cities and Intro to ROW_NUMBER()
24. Solution - New Customers Acquired by Year/Month
25. Breaking Down ROW_NUMBER() in Using order_items
26. Breaking Down LAG() in Using order_items
27. Solution - Average Days Between Orders Using ROW_NUMER() and LAG()
28. Other JOIN's You Might Need
29. Final Solution - Customers with >1 Orders, Avg Time Between Orders
30. FULL OUTER JOIN - Motivation and Problem Statement
31. FULL OUTER JOIN - Solution
32. JOIN's Mini Project - Hourly Revenue Trends
33. Hourly Revenue Trends Intro
34. Hourly Revenue Trends CASE Solution
35. CASE Statement Trick
36. UNNEST, Correlated Subqueries
37. UNNEST Intro, Running Totals w/Correlated Subqueries
38. Moving Averages and Quick Comprehension Check
39. LAG using a correlated subquery!
40. Customer Order Summary - Part 1
41. Customer Order Summary - Using Correlated Subqueries Part 2
42. Stock Price Project
43. Analyzing Stock Prices Using Moving Averages
44. Stock Price Analysis Solution
45. More Complex JOIN's and PARTITION'ing
46. Top 2 Customer Rentals by Moving Rating (Top N Problem) - Part 1
47. Top 2 Customer Rentals by Moving Rating (Top N Problem) - Part 2 Solution
48. Using Multiple Partitions with ROW_NUMBER()
49. Stack Overflow MiniProject - Top Phrases used in Title by Tag
50. Splitting Titles into Tokens and Counting Them
51. Analyzing Top Occurring Unigrams From the Question Titles Grouped by Tag
52. Using ML.NGRAMS to get Bigrams, Trigrams and Ngrams
53. More Text Processing With Regular Expressions
54. Regular Expressions Intro - Advent of Code 2020 Problem 2 Part A
55. Regular Expressions Intro - Advent of Code 2020 Problem 2 Part A Continued
56. Pair Programming With a Former Student - Watch if you can!
57. Advent of Code 2020 Problem - Part 2 - First Video
58. Advent of Code 2020 Problem - Part 2 - Second Video
59. Advent of Code 2020 Problem - Part 2 - Javascript UDF Solution
60. Google Analytics Attribution Analysis
61. Intro to Google Analytics Multichannel Funnels and Paths
62. Starting to write the code to analyze first and last touch channels
63. Dealing with some edge cases and bigger questions
64. Solution for paths of length > 1
65. NEW for 2024 - GA4 w/BigQuery - Data Model, Event Params and Attribution Part 1
66. Intro to BigQuery for GA4 - Understand the data model
67. 2 - Learning how to UNNEST and deal with event_params
68. 3 - Analyze Landing Pages
69. 4 - Adding Transactions to the Landing Page Report
70. 5 - Interlude - Speeding up the Solution from Lecture 4
71. 6 - Last Click Attribution, intro to First Click
72. 7 - First Click Solved, intro to Uniform or Linear Attribution
73. 8 - Simple Uniform Solved, intro to Segmented Uniform
74. 9 - Segmented Uniform Attribution Explained Conceptually - Partially Solved
75. 10 - Segmented Uniform Attribution Solved - Add one user challenge
76. 11 - Adding another user and partitions for Uniform Attribution

Welcome to the revolutionary course that will transform the way you analyze data in the cloud! In a world where innovation and efficiency are imperative, Google BigQuery emerges as a giant, offering powerful tools for visionary data professionals.

In this exciting program, you will dive into the fascinating universe of Google BigQuery, leaving behind the limitations of traditional analytics. From startups to industry titans, more and more organizations are discovering the untapped potential of this leading cloud platform.

Throughout this dynamic course, you'll learn not only how to be productive and efficient, but how to master the complexities of data analytics at scale. From understanding fundamental concepts to mastering various techniques, you'll equip yourself with the skills necessary to get started and excel in the competitive world of data analytics.

This is NOT a boring course of voice and PowerPoint lectures. Here I will discuss and present the material in an interactive and engaging style that will keep you interested and make it easier to understand. Take a look at the free videos available and you will see the difference.

If you already have SQL experience and are ready to take your skills to the next level, this course is your passport to success in the modern data landscape. Join me to discover how Google BigQuery can boost your career and take your analytics capabilities to new heights!

Get ready to defy convention and embrace the data revolution with Google BigQuery!

1. Introduction
2. Introduction
3. What you should know?
4. Introduction to Google BigQuery
5. Download Exercise Files
6. Google BigQuery Setup
7. Case study introduction
8. Set up BigQuery sandbox
9. Loading data into BigQuery
10. Data Exploration
11. SELECT statement
12. ORDER BY clause
13. WHERE clause conditional logic
14. WHERE clause BTEWEEN and IN
15. Fuzzy string matching
16. COUNT function
17. Calculation functions
18. Challenge - Wisdom Pet - Quiz-1
19. Solution - Wisdom Pet - Quiz-1
20. Data Aggregation
21. GROUP BY clause
22. CASE WHEN statements
23. COUNTIF function
24. PIVOT function
25. Challenge Financial analysis
26. Solution Financial analysis
27. Table Joins
28. Introduction to table joins
29. INNER JOIN
30. LEFT JOIN
31. Challenge - Wisdom Pet - Quiz-3
32. Solution - Wisdom Pet - Quiz-3
33. Window Functions
34. Introduction to Window Functions
35. Ranking functions
36. Cumulative metrics
37. Moving Averages
38. Challenge - Wisdom Pet - Quiz-4
39. Solution - Wisdom Pet - Quiz-4
40. Conclusion
41. Conclusion