The Pragmatic Engineer

NOVEMBER 16, 2022

I get a lot of scoop sent by readers (thank you!). Sadly, in 2022, a good part of the scoop is about companies laying off people. Some of this scoop has not been reported before. I don't want to broadcast layoffs on Twitter or LinkedIn continuously, but also don't want this information to be lost. This page collects scoops I receive, some of which might not have been reported elsewhere.

Healthcare 130

Healthcare Software Engineer Software Engineering Data Workflow 130

Confessions of a Data Guy

NOVEMBER 16, 2022

Ok, so I don’t really mean all that. Or do I? I have no idea what the future holds. Sometimes it’s easy to pick out the winners, like Databricks and Snowflake, you can see, feel, and taste the results of those data products, a delicious and delectable bounty to feast upon. Other things are harder […] The post A Diatribe against Data Contracts and their Abuses. appeared first on Confessions of a Data Guy.

Data 130

Data IT Data Engineering Data Engineer 130

KDnuggets

NOVEMBER 16, 2022

While different ways to learn Data Science for the first time exist, the approach that works for you should be based on how you learn best. One powerful method is to evolve your learning from simple practice into complex foundations, as outlined in this learning path recommended by a physicist who turned into a Data Scientist.

Data Science 160

Data Science IT Data 160

Cloudera

NOVEMBER 16, 2022

I recently had the privilege of attending the CDAO event in Boston hosted by Corinium. Tracks represented financial services, insurance, retail and consumer packaged goods, and healthcare. Overall, it struck me that while data science is not new, most firms are still defining the mission of the data office and data officer. It’s clear firms seek to leverage data and embrace its potential insights, but most are forging ahead in largely uncharted territory.

Insurance 80

Insurance Retail Healthcare Data 80

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data

KDnuggets

NOVEMBER 16, 2022

Based on the recent discussions with dozens of Fortune 500 data science teams, we can expect to see a continued spotlight on AI model quality in 2023.

Data Science 159

Data Science Data 159

Engineering at Meta

NOVEMBER 16, 2022

Code reviews are one of the most important parts of the software development process At Meta we’ve recognized the need to make code reviews as fast as possible without sacrificing quality We’re sharing several tools and steps we’ve taken at Meta to reduce the time waiting for code reviews When done well, code reviews can catch bugs , teach best practices , and ensure high code qualit y.

Coding 55

Coding Machine Learning Engineering Process 55

Monte Carlo

NOVEMBER 16, 2022

Last month at IMPACT 2022: The Data Observability Summit, I had the distinct privilege of chatting with Daniel Kahneman, Nobel Prize-winning economist and author of one of my favorite books, Thinking, Fast and Slow. Most notably, Daniel discussed the difference between two major types of thinking: System 1, decision making that operates automatically (say, doing simple multiplication) and System 2, decision making that requires effort and attention (for instance, a complex Calculus problem).

Systems 52

Systems Programming IT Data 52

KDnuggets

NOVEMBER 16, 2022

How LinkedIn Uses Machine Learning To Rank Your Feed • Confusion Matrix, Precision, and Recall Explained • Matrix Multiplication for Data Science (or Machine Learning) • Machine Learning from scratch: Decision Trees • 7 Python Projects for Beginners.

Machine Learning 111

Machine Learning Data Science Python Project 111

Booking.com Engineering

NOVEMBER 16, 2022

In my career, I’ve seen many times how teams started with automated testing. Not all attempts were successful. In this post, I’m going to share a few tips on creating a culture of automated testing in your team, and shaping the journey from zero-tests to a reliable set of tests at different levels. A common way in which some teams approach automated testing is that they set up a target, something like: “In this quarter, we will increase test coverage to X percent”.

Coding 52

Coding Kafka Engineering Technology 52

KDnuggets

NOVEMBER 16, 2022

10 Cheat Sheets You Need To Ace Data Science Interview • 7 Free Platforms for Building a Strong Data Science Portfolio • The Complete Free PyTorch Course for Deep Learning • 3 Valuable Skills That Have Doubled My Income as a Data Scientist • 25 Advanced SQL Interview Questions for Data Scientists • A Data Science Portfolio That Will Land You The Job in 2022 • Top Free Git GUI Clients for Beginners • Essential Books You Need to Become a Data Engineer.

Data Science 101

Data Science Portfolio Deep Learning SQL 101

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

Engineering

Tweag

NOVEMBER 16, 2022

JupyterWith has been around for several years with growing popularity. Over the years, we found that researchers struggled with the Nix language and jupyterWith API. Since researchers are our primary target audience, we decided to improve the usability of jupyterWith. Today, we are proud to announce the release of a new version! The new simplified API makes jupyterWith easier to use and provides more options for creating kernels.

Python 40

Python Coding Project Systems 40