We are in the early planning stages for a Maryland data-focused conference in 2021. If you would like to stay informed, please sign-up for updates
Be a Do-Gooder
Are you looking for a way to get involved in the community and make an impact? Check out the volunteer opportunities with U.S. Digital Response
Book Review Opportunity
Are you interested in reviewing an O'Reilly book for the publisher and sharing your views with the world? As if that isn't enough, you get to take a book home to enjoy as well. Send us an email
and we'll get you started.
Data Analysis Volunteer Work to Support Baltimore City
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 have put together a group to focus on a few problems working with Baltimore City data and need your help. The current project focuses on data parsing and analysis for the Baltimore Board of Estimates. 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.
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introducing your company and needs.
Get Involved with Data Works!
Want to be more involved in our data science community? If you have experience running workshops, hack-a-thons, 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
Introducing the MAD (ML, AI, Data) Public Company Index – Matt Turuk previews a new public market index – the MAD (for machine learning, AI and data) index. Tags: AI, Money, Market, ML
Facebook’s Next Big AI Project Is Training Its Machines on Users’ Videos – Teaching AI systems to understand what’s happening in videos as completely as a human can is one of the hardest challenges — and biggest potential breakthroughs — in the world of machine learning. Today, Facebook announced a new initiative that it hopes will give it an edge in this consequential work: training its AI on Facebook users’ public videos. Tags: AI, Facebook
Lessons from a Year of Contributing to Open Source - Niall Rees Woodward – A year ago I decided a top priority for 2020 was finding an open source project to contribute to. In this post I reflect on my experience, what I wish I’d known before I started, and to what extent my initial expectations held true. Tags: Open Source
Leveraging Machine Learning for Game Development – This article discusses the use of machine learning (ML) to adjust game balance by training models to serve as play-testers, and demonstrate this approach on the digital card game prototype Chimera, which we’ve previously shown as a testbed for ML-generated art. Tags: ML, Gaming
GPT-3 Powers the Next Generation of Apps – Nine months since the launch of our first commercial product, the OpenAI API, more than 300 applications are now using GPT-3, and tens of thousands of developers around the globe are building on our platform. We currently generate an average of 4.5 billion words per day, and continue to scale production traffic. Tags: app, platform, Viable
Uber's Journey Toward Better Data Culture from First Principles – Uber has revolutionized how the world moves by powering billions of rides and deliveries connecting millions of riders, businesses, restaurants, drivers, and couriers. At the heart of this massive transportation platform is Big Data and Data Science that powers everything that Uber does, such as better pricing and matching, fraud detection, lowering ETAs, and experimentation. Petabytes of data are collected and processed per day and thousands of users derive insights and make decisions from this data to build/improve these products. Tags: Uber, Big Data, Data Analysis
Synthetic Data: Sometimes Better Than the Real Thing – Having a large stockpile of data is still a prerequisite for advanced analytics and AI. But companies building AI models increasingly are finding that artificially created data can be just as good as the real thing. And in some cases, synthetic data is a superior alternative, specifically when it comes to issues of bias and ethics. Tags: Data, bias, AI
Graphing the Pandemic – 2020 was literally off the charts. Data journalists saw changes that typically take months instead happening in weeks — numbers that will be breaking the y-axis for years to come. Tags: Pandemic, Data Visualization
How We Built a Context-Specific Bidding System for Etsy Ads – Etsy Ads is developing a neural-network-powered platform that can determine, at request time, when the system should bid higher or lower on a seller’s behalf to promote their item to a buyer. We call it contextual bidding, and it represents a significant improvement in the flexibility and effectiveness of the automation in Etsy Ads. Tags: Predictive Analysis, Etsy
Data Scientists Are Predicting Sports Injuries with an Algorithm – Athletes and parents of athletes take note! Machine learning can tell athletes when to train and when to stop. Tags: ML, Medical Prevention
An Artificial Intelligence Tool That Can Help Detect Melanoma – Using deep convolutional neural networks, researchers devise a system that quickly analyzes wide-field images of patients’ skin in order to more efficiently detect cancer. Tags: AI, Medical Prevention, Neural Networks
The Cult of CryptoPunks – Ethereum's 'oldest NFT project' may not actually be the first, but it's the wildest. Tags: NFT, CryptoCurrency
The Ghosts in the Data – The basic block of labor of machine learning is cleaning data and setting up engineering pipelines, the detailed and tedious work of making all the pieces fit together. Tags: ML, Data, Models
A View of The Future of Our Data – In this essay, addressed to the reader from 2022, I will depict and defend an attractive, feasible vision for the future of the data economy. But make no mistake: We’re going to have to fight for it. Tags: Data Coalition
Why the Pandemic Experts Failed – We’re still thinking about pandemic data in the wrong ways. For months, the American government had no idea how many people were sick with COVID-19, how many were lying in hospitals, or how many had died. And the COVID Tracking Project at The Atlantic, started as a temporary volunteer effort, had become a de facto source of pandemic data for the United States. Tags: Data, COVID, Pandemic
The Foundations of AI Are Riddled With Errors – Deep learning, which involves feeding examples to a giant simulated neural network, proved dramatically better at identifying objects in images than other approaches. That kick-started interest in using AI to solve different problems. But research revealed this week shows that ImageNet and nine other key AI data sets contain many errors. Tags: AI, deep learning, ImageNet, Data
How-To's and Tutorials
One Question To Make Your Data Project 10x More Valuable
– Data folks are in the business of helping people make decisions. Whether it's a quick and dirty ad-hoc query or a super sophisticated statistical model, at the end of the day we're informing a decision. Tags: Data Collection
Get Better at Programming by Learning How Things Work –
When we talk about getting better at programming, we often talk about testing, writing reusable code, design patterns, and readability. All of those things are important. But in this blog post, I want to talk about a different way to get better at programming: learning how the systems you’re using work!
Tags: Programming, how to
The Architecture Behind A One-Person Tech Startup
– This is a long-form post breaking down the setup I use to run a SaaS. From load balancing to cron job monitoring to payments and subscriptions. There's a lot of ground to cover, so buckle up! Tags: SaaS, StartUp, Kubernetes, AWS, Django, Rails
Building Powerful Data Teams: On Investing in Junior Talent
– I want to share a sort of step-by-step recounting of how I built a culture of learning and growth on my data team and spent a year investing in junior talent. My hope is that other managers adopt a similar attitude for their data teams, and that you also can share with me what you have done for your teams! Tags: Management, Retention, Recruiting
Progressively Approaching Kaggle
– The Titanic Competition is most people’s first attempt at getting started on Kaggle. It has a wonderful archive of resources but if you’re looking for something newer, quicker and progressive that gets you acquainted with Kaggle competitions, then the Tabular Playground Series is a fantastic place to start. Tags: Kaggle, TPS
Data Tools and Resources
Is It a Pokémon or a BigData Tech?
– Take a break from the stresses of the day and test your knowledge of Pokémon or BigData tech stacks. How many will you get right? Tags: Pokémon, Tech Stack
Understanding front-end data visualization tools ecosystem in 2021
– Ploomber is a Python package for building data pipelines for data science and machine learning. In a given pipeline, tasks can be anything from Python functions, notebooks, Python/R/Shell scripts, and SQL scripts. Tags: Python, SQL
My Love / Hate Relationship With Jupyter
– Why Jupyter delights Data Scientists and terrifies Machine Learning Engineers. Tags: Jypter, Julia, ML
GitHub - encoredev/encore
– The Go backend framework with superpowers: Encore is a Go backend framework for rapidly creating APIs and distributed systems. It uses static analysis and code generation to reduce the boilerplate you have to write, resulting in an extremely productive developer experience. Tags: API, Framework
GitHub - elixir-nx/livebook