| Sponsored News from Data Science Central | |
Code-first data science using DataRobot's AI cloud platform Hear more about DataRobot’s AI cloud tools for code-first data scientists and developers from Dave Anderson, VP of AI Evangelism, and Dan Maloney, EVP of Zepl, and watch how you can put it into practice with some short demos from Solutions Architect, Alec Seims. In this webinar, you'll learn about:
|
Scaling Graph Neural Networks to billions of connections.
I like this which is very clear and easy to use and understand. Great code examples.
Learn the Most Used SQL Queries in 5 Minutes with Examples
You need to keep these somewhere so you can access them. I have in the past used text files in a directory or Evernote. It is really easy then to copy and edit the code.
Learn how to run multiple machine learning models using lazy predict.
This is really neat and so you need to bookmark or add it to something like Evernote so you can use this in your Python code.
| ||||||
| ||||||
|
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||
Are you a Programmer? Want to take your productivity to a whole new level? Look no further, these apps will do the job nicely.
Some of these look good - definitely a few here worth investigation.
This detailed look at the modern data stack was initially created in 2020 and has been recently updated to show how things are evolving. Covers best-in-class architectures for both analytic and operational systems and lays out a hypothesis for why specific changes are happening. The article came out of discussions with dozens of practitioners.
I found this very interesting - and the fact that it highlights the changes from the original version gives us all a great idea as to how things have changed. Use it for discussion in your own teams and what needs to be changed.
| ||
|
Data Science is an ever-growing field, there are numerous tools & techniques to remember. It is not possible for anyone to remember all the functions, operations and formulas of each concept. That’s why we have cheat sheets and summaries. They help us access the most commonly needed reminders for making our Data Science journey fast and easy.
This is really like a one-stop-shop for cheatsheets - definitely worth a bookmark, a printout, adding to Evernote or whatever is your choice for preserving something important.
| ||||||||||||||||||||||
|
The incremental data load approach in ETL (Extract, Transform and Load) is the ideal design pattern. In this process, we identify and process new and modified rows since the last ETL run.
Code is available on Github. I can see that it is picking up just changes but I wonder for a lot of data how efficient that actually is and whether that comparison should be done at the source or off somewhere else in the cloud where it can't affect the source's performance. Something to consider.
Create a data science learning path with courses from the world’s most prestigious university.
Some great courses from an amazing place,
The best knowledge is still placed in the libraries; within books. In this article, discover some of the top recommended Data Science books catering to beginners.
I would recommend the middle ground - do them both and read books while doing some targeted training too.
