daytum

daytum daytum: An education platform and marketplace that bridges data scientists, energy industry professionals, and technology

03/11/2020

All - We do recognize the concerns regarding the Coronavirus cases. We are closely monitoring the situation and will take actions as necessary.

As of now, our workshops (including the March 31st workshop) are on schedule.

In case of any changes or cancelations, we will be sure to keep you notified.

Thank you for your support. And, as always, please feel free to reach out to us if you would have any questions.

[email protected]

11/25/2019

🗣️ 📢 Announcing Black Friday & Cyber Monday deals soon.
Stay Tuned!

...Sometimes the ...becomes the
11/01/2019

...Sometimes the ...becomes the

10/11/2019

daytum.eventbrite.com

Curious how to apply data analytics and machine learning to your workflow? Have you wanted to implement to automate so...
10/07/2019

Curious how to apply data analytics and machine learning to your workflow? Have you wanted to implement to automate some processes? Join us in Austin [Oct. 28 - Nov 1] for workshops that cover topics like , , , , , , and so much more.

Prices are going up soon and space is limited! Be sure to reserve your spot as soon as possible.

LINK: daytum.eventbrite.com

😳 100+ Engineers, geologists, and industry professionals learning from Dr. Michael Pyrcz!!!! Thanks to Society of Petr...
09/27/2019

😳 100+ Engineers, geologists, and industry professionals learning from Dr. Michael Pyrcz!!!! Thanks to Society of Petroleum Engineers Gulf Coast Section SPE - GCS for hosting us yesterday and all those who attended!

HISTORICAL OIL/GAS/WATER FORMATION - is a Python package for loading, investigating, and organizing data. From the pers...
09/24/2019

HISTORICAL OIL/GAS/WATER FORMATION -
is a Python package for loading, investigating, and organizing data. From the perspective of a , it has the following features:

1) Loads data into containers you already use: NumPy arrays, Pandas/Dask Dataframes, etc.
2) Reduces boilerplate code
3) Facilitates reusable workflows
4) Install datasets as packages
5) "Self-describing" data sources
6) "Quick look" plotting

The example below imports a built-in "catalog" (cat) of data sources from Intake. We will then load the total production histories (oil, gas, water) from a well identified by the API number '33007000110000' and store it in a dataframe.

**Note: .head() displays the first 5 rows of the dataframe

😳SOLD OUT! 100 Tickets! As we get more requests, please stay tuned for upcoming workshops and events....
09/20/2019

😳SOLD OUT! 100 Tickets! As we get more requests, please stay tuned for upcoming workshops and events....

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