What we're about

ODSC brings together the open-source and data science communities with the goal of helping its members learn, connect and grow.

The focus of this Meetup group is to allow ODSC to work with Meetup groups, non-profits, and other organizations to present informative lectures, workshops, code sprints and networking events to help grow the use of open source languages and tools within the data science and data-centric community. As such, our specific goals are:

1. Build a collaborative group to work with other Meetup groups, non-profits, and other organizations.

2. Promote the use of open source languages and tools amongst data scientists and others.

3. Host educational workshops.

4. Spread awareness of new open source languages and tools that can be used in data science.

5. Contribute back to the open-source community.

Who is this meetup for?

• Data engineers, analysts, scientists, and other practitioners

• R, Python and other software engineers who work with data or want to learn

• Data visualization developers and designers

• Non-technical team leads, executives, and other decision-makers from data-centric startups and large companies looking to utilize open-source tools

Get Involved with our Meetups:

• Speaker Form ( https://docs.google.com/a/odsc.com/forms/d/1trkCoecAMa8za_ZzfN5bW6ZNBaRlmqJSQvuME_2nbJA/edit?usp=drive_web ) - Submit a talk, tutorial, or panel.

• Suggest a Meetup Topic Form ( https://docs.google.com/forms/d/1rEjO3UMMXRXtY8Yr_J_jj3ebYwsIFqcGA6FZzWK4rd0/edit )

• Volunteer Form ( https://docs.google.com/forms/d/1Vu3B72avz2I1xx618pEFGsuywZE9t4n78br9vSEX9oE/edit )

• Host or Sponsor Form ( https://docs.google.com/forms/d/1eyM9hJ3l8TlNmw35re65mH7mFCmsPoRZ1p5RJQEVhnk/edit )

• Showcase your Startup Form ( https://docs.google.com/forms/d/1oz8A4fbfe6HHs71v4nMpcf9FP_kpS9CcCfd3qIBS5HU/edit )

Get free access to more talks like this at LearnAI

· LearnAI: https://learnai.odsc.com/

· Facebook: https://www.facebook.com/OPENDATASCI/

· Twitter: https://twitter.com/odsc & @odsc ( https://twitter.com/odsc )

· LinkedIn: https://www.linkedin.com/company/open-data-science/

Upcoming events (5+)

SK-Burn: Tips for new data scientists

CIC Cambridge

Kaggle Days Meetup in Boston #1 Speaker: Raymond Grossman, Machine Learning Engineer at Kensho Technologies https://www.linkedin.com/in/raymond-grossman-bb4664114/ Topic: SK-Burn: Tips for new data scientists Schedule: 6:00pm - 6:30pm - ODSC and LogicAI Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Raymond Grossman has been an avid machine and deep learning practitioner since graduating from Princeton Mathematics in 2016. He specializes in natural language processing and speech at Kensho Technologies under S&P Global, where he works as an ML Engineer. He also is an accomplished Kaggler, recently achieving overall rank 14 out of over 100,000 competitors globally under the moniker "To Train Them Is My Cause". His work on Kaggle includes winning Google's Toxic Comment Classification Challenge (1st/4551). Outside of work, Raymond enjoys playing the violin and bouldering. Abstract: Information about how different SOTA models or technologies work is readily available, but information on how to apply those models quickly and effectively is hard to come by. Covering everything from modeling tips and tricks to engineering pipelines and workflows, this talk attempts to bridge that information gap by using SK-learn to demonstrate the impact of engineering decisions on modeling pipelines. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

Webinar: Human Machine Learning

Needs a location

We want to invite you to participate in the FREE ODSC Webinar! Date: October 2 Time: 1 pm - 2 pm EST To access this webinar, please register using the link below: https://attendee.gotowebinar.com/register/3383942049520076557 Could we, as humans, improve the way we learn by applying techniques machines use to learn? From early in the field of AI, researchers have been looking to cognitive psychology for inspiration on how to teach machines to learn. The effectiveness of this approach is evidenced by recent advances in, and growing prevalence of, deep learning. However, we’ve reached a point where machine learning methodologies are deviating from the way humans learn and gaining impressive efficiencies as a result. For humans, this issue is amplified by current findings in cognitive psychology which strongly suggests that many of our long-standing learning methods have been largely misguided. In this webinar, we explore the challenges with common studying practices, contrasting those with methods that machines successfully use to learn, and drawing parallels to recent cognitive psychology research. Talk will be delivered by: - Matt Cowell, CEO of QuantHub Matt serves as the CEO at QuantHub, spearheading the drive to help companies overcome the extreme analytics talent shortage and build exceptional data science and engineering teams. Matt has a passion for developing authentic relationships with customers to truly understand what drives them, and then crafting creative solutions to their most critical problems. Prior to joining QuantHub, Matt spent the last 15 years running product and tech at PE-backed companies, including building a product and engineering organization at Daxko to deliver 10x revenue growth, 7 acquisitions, and 3 enormously successful recapitalizations in just 10 years. While at Daxko, Matt led the team to deliver the first machine learning/AI solution to the market, predicting customer membership churn and also propensity to donate. - Nathan Black, Chief Data Scientist at QuantHub Nathan Black is a Data Science Professional and AI Researcher with over 5 years of experience leading and working alongside quant teams to develop cutting-edge, end-to-end data solutions in manufacturing, healthcare, food retail, finance, and education industries. Nathan has a proven track record of using data to help people thrive, assisting organizations in capturing value from data and technology through the deployment of BI, Prescriptive Modeling, and Artificial Intelligence applications.

An overview of machine learning interpretability

CIC Cambridge

Speaker: Mehrnoosh Sameki, Technical Program Manager at Microsoft https://www.linkedin.com/in/mehrnoosh-sameki-a2a02245/ Topic: An overview of machine learning interpretability Schedule: 6:00pm - 6:30pm - ODSC Intro, Pizza & Refreshments 6:30pm - 7:20pm - Talk 7:20pm - 7:30pm - Q&A 7:30pm - 8:00pm - Networking Bio: Mehrnoosh Sameki is a technical program manager at Microsoft responsible for leading the product efforts on machine learning interpretability and fairness within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in retail space to drive revenue and enhance personalized shopping experiences of customers. She earned her Ph.D. degree in computer science at Boston University. Abstract: With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a ‘black box’, harder to understand and interpret. To gain the user’s trust, there is a strong need to develop tools and methodologies to help the user to understand and explain how predictions are made. Data scientists also need to have the necessary insights to learn how the model can be improved. Much research has gone into model interpretability and recently several open sources tools, including LIME, SHAP, and GAMs, etc., have been published on GitHub. In this talk, we present popular state-of-the-art interpretability algorithms and introduce a Machine Learning Interpretability toolkit which incorporates the cutting-edge technologies in the domain of AI transparency. Using this toolkit, data scientists can explain machine learning models using state-of-art technologies in an easy-to-use and scalable fashion. ODSC Links: • Get free access to more talks like this at LearnAI: https://learnai.odsc.com/ • Facebook: https://www.facebook.com/OPENDATASCI/ • Twitter: https://twitter.com/odsc & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science/ • West Conference Oct 29 - Nov 1: https://odsc.com/california • Europe Conference Nov 19 - 22: https://odsc.com/london

ODSC West Mini-Bootcamp 2019

Hyatt Regency San Francisco Airport

Are you ready to learn new skills, connect to the data science community and accelerate your career? Learn the fundamentals and prep for immersive ODSC Trainings. Then get hands-on with in-demand data science tools and frameworks while building your network to grow your career! Register now: https://www.eventbrite.com/e/odsc-west-mini-bootcamp-2019-tickets-65577424843?aff=ebdssbeac Register with code Meetup19 to save 40% on your passes!

Past events (80)

Photos (16)