Photo by Franki Chamaki on Unsplash

Note: This is part 2 as the conclusion of this article. You might want to read part 1 first before you start with this article.

Applying aggregation functions to the example dataset

Having a basic understanding of the background of the dataset and the aggregation functions, it is time to combine these 2 elements and see how they work together in action.

COUNT

Example query here:

SELECT COUNT(*)
FROM `bigquery-public-data.austin_bikeshare.bikeshare_stations`


Photo by Franki Chamaki on Unsplash

Update: In order to make it easier to digest, this article is split into 2 parts. What you are reading now is part 1 as the introduction, and we put everything together in part 2 as the conclusion.

Data powers all kinds of organizations. No matter it is a commercial firm, government bodies, NGOs, they need data. …


Photo by Stephen Phillips - Hostreviews.co.uk on Unsplash

Preface

Google Analytics is widely used in various industries for tracking user activities in Web and mobile applications. For most of the users, the default reporting provided by GA is usually sufficient as long as the tracking of the application is in place (If not, go for the custom reporting! :p ).

Yet, once the business grows into a certain scale (20–50 employees or even bigger), there comes to a need to have the GA data available in a Data Warehouse for further in-depth analysis and even Machine Learning purposes.

Setup

The GA public data from BigQuery is used in the illustration:


Disclaimer: The original article was written by me in Traditional Chinese in 17th September 2016. I am now rewriting this article in English for wider readers population.

“Nothing is true, everything is permitted.” — Ezio Auditore

This is the core creed of the Assassins in the Assassin’s Creed game series.

Story Background

Since ancient time, people have 2 polars of belief.

The mainstream one is the faith in organizations and structures. …


“Data Science”, along with the other buzz words

It has been widely discussed in the industry: The term “Data Science” itself is poorly defined. Loads and loads of people want to crash into the field, desperately want to figure out an answer and got overwhelmed with the buzz words like Deep Learning, Machine Learning, Algorithms, Big data, etc.

After all the pitches to convince the investors to pour more and more money to the tech companies using “data science”, maybe now is a good time to do some “Disenchantment”.

Buzz Words in Data Science

To have a more solid discussion, let's list out some of the most popular buzz words of data science…


2018 has been a year full of changes and new stuff for me.

Before anything else, I would like to quote a part of some lyrics. It is from the Japanese anime Attack on Titans:

Wohlan Freie! Jetzt hier ist an Sieg
(Well friend! Here’s to the victory)
Dies ist der erste Gloria
(This is the first glory)
Wohlan Freie! Feiern wir dieser Sieg
(Well friend! Let’s celebrate this victory)
für den nächsten Kampf
(For the next fight)

Yes, 2018 is a year of glory to me.

Actions are the beginning of dreams

“Actions are the beginning of dreams”, in Traditional Chinese. A souvenir from Taiwan.

Being born in Hong Kong in this era, it means lots of hopeless…

Jimmy Pang

Data Leader, Evangelist and Educator, dedicated to the data journey. Interested in tech and classy stuffs: art, whiskey, coffee, tea, spirituality, history etc.

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