World Data Science Institute Summer Cohort 2020
The World Data Science Institute is a Specialized Consulting & Training Agency offering DSaaS (Data Science as a Service). They are now taking applications to join their 13-week internship program which runs from July 27 – October 26. For more information, please apply here
Interested in side projects or a study group?
Are you an expert with data and willing to mentor, or are you an up and coming hobbyist looking for a side project to work on? We are looking to put together a group and need help
. If interested, please send us an email
or join us on Slack
to discuss building a side project group.
Considering a career change?
Are you a software or system engineer, data scientist, analytic developer, or cybersecurity expert interested in learning about new opportunities?
Please send us an email
to learn about the opportunities available with our partners.
Are you hiring?
If your company is looking for data scientists, data engineers, software engineers, and other data related experts, please reach out so that we can help our members find new opportunities.
Please send us an email
introducing your company and needs.
Want to be more involved in our data science community? If you have experience running workshops, hackathons, curating newsletters, or are just interested in helping to grow the meetup, please send us an email
Erias has an immediate need for Software Engineers, System Engineers, Test Engineers, Data Scientists, and System Administrators. External referral bonuses are available. For more information, please contact us at email@example.com
Data News and Articles
The Dumb Reason Your AI Project Will Fail — Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. Then everything comes to a screeching halt — the company finds themselves stuck, at a dead end, with their outstanding proof of concept mothballed and their teams frustrated. What explains the disappointing end? Tags: AI
Nitpicking Machine Learning Technical Debt — Tech debt is an analogy for the long-term buildup of costs when engineers make design choices for speed of deployment over everything else. This article discusses the Hidden Technical Debt in Machine Learning Systems paper and provides additional advice based on more recent trends and data. Tags: Machine Learning
The Recession’s Impact on Analytics and Data Science — There has been a huge demand for data scientists in the past decade. Is that about to change? Will the current recession slow the growth in demand for analytics and data science? Will changes in organizational goals and focus make job losses in these fields likely? Tags: Data Science
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. Tags: Analytics
A State-of-the-Art Open Source Chatbot at Facebook — Facebook AI has built and open-sourced BlenderBot, the largest-ever open-domain chatbot. It outperforms others in terms of engagement and also feels more human, according to human evaluators. This is the first chatbot to blend a diverse set of conversational skills — including empathy, knowledge, and personality — together in one system. Tags: Machine Learning, Bots
Don't Democratize Data Science — A plethora of online courses and tools promise to democratize the field, but just learning a few basic skills does not a true data scientist make. In this article, the author discusses some of these problems and the adverse effect they could have on the field. Tags: Data Science
How Data Science Delivers Value in a Post-pandemic World — Applying data science to help companies achieve their business objectives during this time should no longer be considered a luxury, but a necessity to help organizations stabilize and enter a phase of strategic growth. This article discusses the top areas where data science is delivering value for businesses post-pandemic, and how the roles within data science teams are shifting to facilitate this. Tags: Data Science, COVID
The Cost of AI Training is Improving at 50x the Speed of Moore’s Law: Why It’s Still Early Days for AI — The cost to train an artificial intelligence (AI) system is improving at 50x the pace of Moore’s Law. For many use cases, the cost to run an AI inference system has collapsed to almost nil. After just five years of development, deep learning – the modern incarnation of AI – seems to have reached a tipping point in both cost and performance, paving the way for widespread adoption over the next decade. Tags: Machine Learning, Business
How-To's and Tutorials
What You Need to Know About Product Management for AI
and Practical Skills for the AI Product Manager
— Multi-part series of becoming a successful product manager for AI products. A product manager for AI does everything a traditional PM does, and much more, so which skills are needed to build successful AI products? Tags: Product Management
Deep Learning in Satellite Imagery —
Satellite images allow you to view Earth from a broader perspective. You can point to any location on Earth and get the latest satellite images of that area. Also, this information is easy to access. There are free sources that allow you to download the mapped image onto your computer, and then, you can play with it locally. This article introduces some of the concepts needed to begin exploring satellite imagery datasets. Tags: Deep Learning
Evaluation of COVID-19 Models —
This repository presents the evaluation of models from the COVID-19 Forecast Hub
. Evaluations are conducted weekly and summaries are provided. Tags: COVID
A Practical Guide to A/B Testing —
There's a dramatic mismatch between what you learn in statistics textbooks and how experimentation works in practice. Being successful at A/B testing doesn't come down to analytical methods. It's about process and people. This article discusses key concepts to know about testing such as knowing what test you are running and getting everything captured in writing. Tags: Data Science
Data Tools and Resources
Scaling Pandas: Comparing Dask, Ray, Modin, Vaex, and RAPIDS — Python and its most popular data wrangling library, Pandas, are soaring in popularity. Compared to competitors like Java, Python and Pandas make data exploration and transformation simple but suffers from issues with scalability and efficiency. So it’s no surprise that many developers are trying to add more power to Python and Pandas in various ways. Tags: Tools, Pandas
What I Learned From Looking at 200 Machine Learning Tools — An extensive overview of the trends in machine learning tools covering the landscape, open-source, and problems facing ML Ops. Tags: Tools, ML Ops
STUMPY: A Powerful and Scalable Python Library for Modern Time Series Analysis —
STUMPY is a powerful and scalable Python library for modern time series analysis and, at its core, efficiently computes something called a matrix profile. The goal of this multi-part series is to explain what the matrix profile is and how you can start leveraging STUMPY for all of your modern time series data mining tasks! Tags: Tools, Time Series
Comprehensive Python Cheatsheet
and 10 Smooth Python Tricks For Python Gods —
Python is one of those things that is rather easy learn, but can be difficult to master. Although there are probably two-million gigabytes of Python modules, there are some useful tips that you can learn with the standard library and packages usually associated with scientific computing in Python. Tags: Tools, Python