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January 2020

Upcoming Events
 

Data Workshop: Applied Supervised Machine Learning for Cyber Security
January 21 | 6:30 PM | Loyola

Bring your laptops and come learn how to build a supervised learning model for a security-related data set. Charles Givre will walk attendees through the supervised learning process with a particular focus on automating model tuning and feature engineering.
For more information and to register, please click here.
 

Making Apache Spark Better With Delta Lake
February 13 | 6:30 PM | APL

Managing data lakes, which are are data repositories that store large and varied sets of raw data in its native format, can be challenging. Join us in February to learn about Delta Lake, an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
For more information and to register, please click here.

Past Events


Robotics and Machine Learning: Working with NVIDIA Jetson Kits | December 2019
Video available here.

Connect Data and Devices with Apache NiFi | November 2019
Video available here.

Special Session: Introduction to Machine Learning | September 2019
Video available here.

Additional videos are available here.
 

Partner Announcements

 


Software Architecture Conference | February 23 - 26 | New York
 
The O'Reilly Software Architecture Conference is designed to provide the necessary professional training that software architects and aspiring software architects need to succeed. A unique event, it covers the full scope of a software architect's job, from IT to leadership and business skills. It also provides a forum for networking and hearing what other professionals have learned in real-world experiences. For more information and to register, click here.

We are raffling off a free Bronze Pass on January 26. For a chance to win, register here.
 

Opportunities

 
Upcoming Baltimore Hackathon
Have an idea for Baltimore Hackathon taking place in Spring 2020? Submit your idea here

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 opportunities available with our sponsors and partners.

Interested in side projects?
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?
If so, please send us an email to discuss building a side project group.

Get involved!
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 Ventures
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 info@eriasventures.com.
 

Data News and Articles

 


 

Soft Skills for Data Science It's one thing to have the tech skills, but being a data scientist requires a few soft skills: skepticism, perseverance, creativity, business acumen, and communication.
For more, click here.

The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended and Kubeflow — Blog post outlining Spotify's winding” journey that established the building blocks for their platformized Machine Learning experience and specifically how they leverage TensorFlow Extended (TFX) and Kubeflow in their Paved Road for ML systems.
For more, click here.

First AI that Beats Pros in 6-player Poker  — Facebook and Carnegie Mellon built the AI bot Pluribus, the first one capable of beating human experts in six-player no-limit Hold’em, the most widely played poker format in the world. This is the first time an AI bot has beaten top human players in a complex game with more than two players or two teams.
For more, click here.

How The New York Times is Experimenting with Recommendation Algorithms — Interesting articles about how algorithmic curation at The Times is used in designated parts of their website and apps.
For more, click here.

Why I Use R  — While Python grows in popularity, this article argues there are reasons to stick with R: native data science structures, non-standard evaluation, packaging consensus, and functional programming.
For more, click here.
 

How-To's and Tutorials

 

Data Project Checklist — A questionnaire from the folks of fast.ai developed to help consultants better understand the organization they are creating data products for.
For more information, click here.

How Should I Structure My Data Team? The data team is a brand new thing: it’s not “IT”, it’s not finance, it’s not any of the typical business functions within an operating business. So...who does it report to? How does it interact with the rest of the organization? How big is it?
For more information, click here.

A Guide to Production Level Deep Learning Gitub repository providing an engineering guideline for building production-level deep learning systems that will be deployed in real-world applications.
For more information, click here.

Data Tools and Resources

 

 
R Cookbook, 2nd Edition — This book is full of how-to recipes, each of which solves a specific problem. The recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. We know these recipes are useful and we know they work, because we use them ourselves.
For more information, click here.

Top 10 Python Libraries of 2019 You Should Know About — A list of libraries to get you started on a new project or spice of an existing one.
For more information, click here.

Metaflow: Human-Centric Framework for Data Science — A new open-source project from Netflix to make data scientists more productive by allowing the creation of simple directed acyclic graphs.
For more information, click here and here.

Comet.ml: Github for Machine Learning — A meta machine learning (ml) platform that helps data scientists and teams track, compare, explain and reproduce their machine learning experiments. Improve productivity, collaboration and visibility.
For more information, click here.

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If you are interested in speaking, hosting, or sponsoring a meetup, have opportunities to list, or local news to share, please email info@dataworksmd.org.






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