VIRTUAL EVENT - Zero Gravity – the Modern Cloud Data Pipeline Event - 26 May 2022

 

Sponsored News from Data Science Central

JOIN US LIVE AT THE INDUSTRY’S FIRST CLOUD DATA PIPELINE EVENT Data science and analytical workloads are rapidly transitioning from on-premises infrastructure to the cloud. But while the cloud offers many benefits, the migration is riddled with challenges and potential pitfalls. That’s why we’re bringing together technical experts, thought leaders, and industry visionaries with experience building and managing cloud data pipelines to share lessons learned and discuss emerging technologies. This event is virtual, 100% LIVE and open to everyone—at no cost. KEYNOTE SPEAKER THOMAS KURIAN Chief Executive Officer, Google Cloud Our keynote speaker will be Thomas Kurian, CEO of Google Cloud. New speakers will be added regularly, so keep checking back for updates! DO YOU HAVE UNIQUE INSIGHTS OR EXPERTISE ON CLOUD DATA PIPELINES? We are now accepting submissions from practitioners in the data architecture and engineering community who can share their experience and expertise on the following cloud data pipeline topics: Cloud architecture Best practices in cloud migration Data integrity and durability Optimizing performance Managing costs Leveraging native services Emerging technologies Open-source technologies New analytic approaches Security and governance The call for speakers is now open until Feb 25th – submit your speaking proposal today! IN PARTNERSHIP WITH OUR SPONSORS

Data Science Programming Languages and When To Use Them by Nate Rosidi via @kdnuggets

Read this guide through the most common data science programming languages and when to use them in data science.

This is a great level of detail and very useful to use if you wonder if your choice was correct or if you should expand into another language.

Top 10 Pandas Functions for Preparing Data by Holly Dalligan via @BttrProgramming

Because she wanted to create useful, accurate analysis with as little work as possible.

I found this really interesting and it looks very useful too - data preparation if done well can help you to produce much better results from your analysis,

A Step-by-Step Guide to Calculating Autocorrelation and Partial Autocorrelation by Eryk Lewinson via @TDataScience

How to calculate the ACF and PACF values from scratch in Python.

This was very clear and really helped me to understand how to calculate them in Python as I'm really not good at that language no matter how hard I try.

WEBINAR: Improving Your DataOps Practices with Data Fabric - 16 February 2022

 

Sponsored News from Data Science Central

Webinar: Improving your DataOps Practices with Data Fabric Today's enterprises struggle to unlock real value out of data that is stuck in legacy relational databases, data warehouses and data lakes. Modernizing disparate data silos to a single data fabric is key to managing change over time and creating data-driven innovation and value. Join this session to learn how you can build a modern data fabric leveraging the Lumada DataOps Suite from Hitachi Vantara along with MongoDB Atlas. In addition, hear from a utility customer that is reaping the benefits of the data fabric and see a demo of how you can create a modern data pipeline to drive great enterprise data value. Register to watch this webinar live when it airs February 16th. Register Now Hope to see you there, Sean Welch Data Science Central

A Senior Engineer at Google Reveals “The Best Programming Language To Learn in 2022” by @minhazav via @BttrProgramming

He is a senior software engineer at Google Singapore and very often he is asked about which programming language to learn.

I can agree that learning one is great and that it really depends on what you are working with. Learning the basic concepts via one of them is useful no matter what. Arrays, variables, loops etc are all basic skills that can be transferred across languages.

Good-bye Pandas! Meet Terality — Its Evil Twin With Identical Syntax by Bekhruz (Bex) Tuychiev via @TDataScience

… but up to 30 times faster.

This looks very interesting to experiment and play with.