Data Council Blog

Data Council Blog

Hot Data Tools pt. 2, End-to-End Data Scientists, and More: Top 10 Links From Across the Web

Here's our September 2020 roundup of good reads and podcast episodes that might be relevant to you as a data professional:

1. What Data Tools Don't Do (Data Council)

Our founder Pete Soderling co-authored a follow-on piece to his previous post with Great Expectations' core contributor Abe Gong and Partner at Amplify Partners Sarah Catanzaro, for which they had interviewed the makers of some of the hottest data tools. The focus is still the same: rather than what their data tools can do, we hear about what they don't do, as a way to better understand how they fit together. From ApertureData to Xplenty, this new installment covers 21 new tools, and you can read it here.

Large Datasets, Are Dashboards Dead, and More: Top 10 Links From Across the Web

Here's our August 2020 roundup of good reads and great podcast episodes for anyone working with data:

1. Processing Large Datasets with Python

AI engineer and author J.T. Wolohan was recently a guest of the Heroku’s Code[ish] podcast to discuss the contents of his book, “Mastering Large Datasets with Python.” Listen to the episode here or read the transcript for some practical advice on using Python to deal with massive datasets, especially in the context of machine learning.

Apache Airflow, Beyond Spreadsheets, and More: Top 10 Links From Across the Web

Here's our July 2020 roundup of relevant links for data professionals, from blog posts to podcast episodes:

1. The State of Airflow

Software Engineering Daily recently invited Apache Airflow's creator Maxime Beauchemin and Astronomer engineers Vikram Koka and Ash Berlin-Taylor to discuss the state of Airflow. Listen to the podcast episode or read the transcript to hear their comments on Airflow's use cases, its purpose, the open source ecosystem, and more.

AGI, Dask, Feature Stores, and More: Top 10 Links From Across the Web

Here's our June 2020 roundup of relevant links for data professionals, from blog posts to podcast episodes:

1. Self-Supervised Learning vs. AGI

"AGI does not exist — there is no such thing as general intelligence. We can talk about rat-level intelligence, cat-level intelligence, dog-level intelligence, or human-level intelligence, but not artificial general intelligence," Yann LeCun declared during an online session of the International Conference on Learning Representation (ICLR) 2020, which VentureBeat wrote about. Together with fellow Turing Award winner Yoshua Bengio, he advocated for pursuing humanlike AI through "self-supervised learning."

Emerging Data Roles: The Analytics Engineer

Analytics Engineer: this term has started showing up in blog posts and job listings. It all happened quickly; just a couple of years ago, it wasn't a thing our friends in the data ecosystem talked about. So how did it start trending, what is it exactly, and is it here to stay? We decided to take a closer look, and here's what we found out.

Open Source Highlight: Cube.js

Cube.js is an open source analytics framework meant to answer the "lack of tools for software engineers who are building production, customer-facing applications and need to embed analytics features into these applications," its co-founder and CEO Artyom Keydunov explained in a blog post

What Data Tools DON’T Do, CD4ML and NoSQL: Top 10 Links from Across the Web

Here's our monthly roundup of relevant links for data professionals, from blog posts and tutorials to podcast episodes:

1. Product Management for AI

Peter Skomoroch and Mike Loukides co-authored a very interesting post on what makes product management different in the context of AI. Based on the specificities of AI software development, they make a series of recommendations for a process that also takes business priorities into account. Their post also ends with a list of relevant resources, so it is worth checking out.

25 Hot New Data Tools and What They DON’T Do

“Wait, do tool X and tool Y work together? I thought they were competitive.”

There are dozens of new tools in the fast-growing data ecosystem today. Together, they are reshaping data work in exciting, productive and often surprising ways. The seeds of the data landscape for the next decade have been planted, and they’re growing wildly.

Turns out, cultivating a new ecosystem is messy.

Open Source Highlight: Streamlit

Streamlit officially launched out of beta on October 1st, 2019 with the promise to "turn Python scripts into beautiful ML tools." On the same day, Google's AI-focused venture fund Gradient Ventures announced its investment into the startup, which has since then attracted a considerable amount of attention despite its young age.

Data Science, Data Analytics, Data Engineering and Artificial Intelligence: 11 Online Courses You Should Check Out

With COVID-19 forcing almost one billion people to shelter in place around the world, many people have turned to new activities, such as drawing, baking, gardening… or online learning. If that doesn't sound like you, don't feel guilty by any means – sometimes, surviving is enough! But if you want to get more knowledgeable about data science, data engineering and artificial intelligence, we are here for you.

This is why we came up with this list of courses that can help you prepare for a future job in the data field, upgrade your existing skills, or just satisfy your personal curiosity. From free entry-level courses to full-time bootcamps, here's our selection for you to check out:

Wanna be our Pen Pal?

Receive the latest news, tips and special events from our community directly to your inbox once in a while (we promise no spam)

Data Council Blog Signup