July 24-25 at 10am - 5pm ET

SPEAKERS | SCHEDULE
Immersive Data Science Bootcamp

Taught by instructors seasoned with real industry experience, our full-time, 12-week data science immersive bootcamp is where data science careers are made.

Explore Bootcamp
Corporate Data Science Training

Expert data science training to strengthen your business. Find out why Intel, Wells Fargo, and other Fortune 100 companies chose Metis to skill up their workforce.

Learn More
Beginner Python & Math for Data Science Live Online Sample Class

Register for a free sample class with Sergey Fogelson, VP of Analytics at Viacom on August 8th at 6:30pm ET.

Metis NYC Bootcamp Open House: July 26th

Join Metis staff & alumni for an overview of what to expect during and after bootcamp.

Live Online Professional Development Courses

Part-time evening courses taught by world-class data science practitioners. Attend class from anywhere!

Hear From Our Bootcamp Alumni
“The Careers team at Metis was AMAZING!”
DANGAIA SIMS, Sr. Data Scientist, IBM | Metis Chicago Grad
Share your favorite #DemystifyDS insights with the Metis community!
Metis Chicago Bootcamp Open House: July 26th

Join Metis staff & alumni for an overview of what to expect during and after bootcamp.

Corporate Training Case Study

Find out how Metis skilled up 240 employees in analytics roles at a Fortune 500 financial services company.

View Case Study
Skill Up with Metis Admissions Prep

Develop your data science fundamentals & prepare to apply to the Metis data science bootcamp with this free curriculum.

Sign Up for Free
Machine Learning & Artificial Intelligence Principles: 7/30 - 8/29

Learn from top-notch instructors and receive real-time support. Live Online course starts July 30!

SQL Fundamentals Live Online Course

Designed to up-skill beginners in the SQL language. No prereqs required. Two-week course starts Aug 14.

Metis Seattle Bootcamp Open House: July 26th

Join Metis staff & alumni for an overview of what to expect during and after bootcamp.

Beginner Python & Math for Data Science Live Online Course

Designed for absolute beginners — no prerequisites required! Class starts 8/13.

Introduction to Data Science Live Online Course

Learn principles required to tackle real-world problems in business. Live Online course starts August 20th.

Metis San Francisco Bootcamp Open House: July 26th

Join Metis staff & alumni for an overview of what to expect during and after bootcamp.

Hear From Our Bootcamp Alumni
“A rare opportunity to learn and grow.”
RENKOH KATO, Data Scientist, JP Morgan Chase | Metis NYC Grad
SQL Fundamentals Live Online Sample Class

Register for a free sample class with Jonathan Balaban, Sr. Data Scientist at Metis on August 1st at 5:30pm PT.

Speaker Schedule

  • July 24th: For Aspiring Data Scientists

    All times in EST

    • 10:00am
      Keynote
      Keynote Title Coming Soon
      Tarry Singh

      Stay tuned for more! Talk description coming soon.

    • 10:30am
      Qualities of an Exceptional Data Scientist
      Kate Strachnyi

      The data scientist role is considered to be lucrative; it attracts talent with the promise of high demand for the skill-set, attractive salaries, as well as the potential of working on interesting projects. In writing the Journey to Data Scientist, and The Disruptors: Data Science Leaders; as well as interviewing data scientists for the Humans of Data Science video-podcast, Kate Strachnyi uncovered specific qualities of exceptional data scientists. Watch this presentation to learn about the findings from the in-depth interviews conducted.

    • 11:00am
      Coming Soon!
      Kunal Jain

      Talk description is coming soon! 

    • 11:30am
      How Charts Lie — Getting Smarter About Data Visualization
      Alberto Cairo

      Data visualization is a powerful tool to explore data, and also to communicate with other people. However, visualization can be misleading if we believe that is intuitive or if we embrace myths such as “a picture is worth a thousand words”, "data speaks for itself”, or “we should show, not tell”. This presentation, based on my upcoming book 'How Charts Lie', explains how we can approach data visualization more critically, and take advantage of it by becoming more attentive readers.

    • 12:00pm
      Coming Soon!
      Gabriela de Quieroz

      Talk description is coming soon! 

    • 12:30pm
      Joining the Data Science Community
      Emily Robinson

      Have you heard that you need to “network” if you want to get ahead in your career? Have you wondered if you could get to know data scientists at companies you admire? In this presentation, I’ll start by addressing some of the barriers and misconceptions about building your data science network, share motivation, help you get started, and give you tips on how to network most effectively.

    • 1:00pm
      Tech Won’t Save Us: Reimagining Digital Information for the Public
      Safiya Noble, Ph.D.

      Critical information scholars continue to demonstrate how technology and its narratives are shaped by and infused with values, that is, that it is not the result of the actions of impartial, disembodied, unpositioned agents. Technology consists of a set of social practices, situated within the dynamics of race, gender, class, and politics. This talk, stemming from the new book, Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press), addresses the issues of Internet search, and how language and meaning are derived in ways that pose particular harms to various publics who are increasingly reliant upon commercial technologies.

    • 1:30pm
      You're Not Paid to Model
      Jacqueline Nolis

      People are always willing to tell you about the fancy different modeling techniques in data science, and suggest they are the key to success. As a practicing data scientist, I am here to say that models are only a small part of the complexity of a corporate data science project. Enterprises contain massive amounts of data, but this data is hard to find and harder to clean. Business stakeholders aren’t informed enough about machine learning to understand the level of difficulty of the tasks they’re asking, which leads to analysis after analysis because of additional requests and tweaks from upstream. Almost always these complexities outside of the model are what cause projects to fail, not the fact that a model wasn’t using a cutting-edge approach. In this talk, I’ll walk through the end-to-end lifecycle of a data science project in industry. I’ll demonstrate how organizational pressures can cause solid data science to fail and poor data science to succeed—and what you can do to maximize the chances of success. The goal of this talk is for you to leave with a new awareness of all the non-academic pieces of doing data science at scale.

  • July 25th: For Business Leaders, Managers & Practitioners

    All times in EST

    • 10:00am
      Keynote
      Keynote Title Coming Soon
      Hilary Mason

      Stay tuned for more! Talk description coming soon.

    • 10:30am
      Coming Soon!
      Announcement Coming Soon

      Talk description is coming soon! 

    • 11:00am
      Coming Soon!
      Announcement Coming Soon

      Talk description is coming soon! 

    • 11:30am
      The Ethics & Opportunities of Data Governance
      Natalie Evans Harris

      Through collective power, data can support transforming the human experience. One of the greatest challenges standing in the way, however, is finding the right balance between maximum social impact and also the protection of individual rights. Natalie Evans Harris will explore the critical role that public-private collaboration plays in this balance, and how building an ethical, equitable and sustainable framework for data governance can help organizations move beyond the limitations of traditional approaches to data sharing.

    • 12:00pm
      Licensed to Analyze? Who Can Claim to be a Data Scientist? IADSS: Defining Roles, Standards and Assessing Skills in Data Science
      Usama Fayyad, Ph.D.

      Are you confused about what it takes to be a data scientist? Curious about how companies recruit, train and manage analytics resources? You are not alone. Many employers, educators, and managers are struggling with these issues.  In fact, tremendous resources are being wasted by employers on interviewing candidates who claim knowledge of Data Science that are not even qualified for such positions. This presentation covers insight from the most comprehensive research effort to-date on the data analytics profession, proposes a framework for standardization of roles in the industry and methods for assessing skills.

      We have been running an industry initiative named:  Initiative for Analytics and Data Science Standards (IADSS) to support the development of standards regarding analytics role definitions, required skills and career advancement paths. The initiative kicked off a research study including a detailed survey for analytics executives and professionals, in-depth interviews with industry leaders and academicians as well as an extensive literature review. We will present our initial findings from the research and provide case studies of how bad this confusion and why it is important for the field, for practitioners and for employers and educators to have clarity on this front.

    • 12:30pm
      Data Science Unleashed: From Cottage Industry to an Industry Force—Leading the Way to Robust Models, Smart Decisions, and Digital Products in Demand
      Adrian Cartier, Ph.D.

      Adrian Cartier, Ph.D., Director of Data Science, Bayer, will demystify data science and unpack its tangible business value. Using his many years of experience building a data-driven culture across Monsanto and now Bayer Crop Science, Adrian highlights the focal points for not only creating a data strategy, but more importantly driving a sustainable digital (and business!) transformation.

      In his presentation, Adrian will walk you through the imperative steps to get there. Foremost, you must understand the key high value decisions across your enterprise. What is your mission and vision, your challenge? There are many tools in the toolbox for data science. If you have a nail, use a hammer. But don’t buy a hammer, and then look for a nail.  That is, apply data to key business decisions, which ultimately will drive substantial outcomes for the company.

      Next, create a data strategy that centers around data as an asset for the company with data science unlocking its value. Get buy-in and create a community. After all, it takes a community to build sustainable robust data science products, including data scientists, software engineers, data engineers and business partners. They don’t all have to be data scientists, but they should be able to access, understand and apply the data to solve their challenges and develop solutions. This community can also support user-centered data science, that is, identify and improve the user experience and user adoption to get the most value out of the output, whether it’s a robust model, a critical insight or a digital product.

      Be ready to fail fast and learn from it. That’s a good lesson. Always remember, data science is science: it should be tracked, measured, and repeatable.  Also remember to identify quick wins to show measurable value.

      And think how your enterprise can make an impact now, as well as the potential that lies in the future. While once a cottage industry, data science unleashed will become a value-added force in any industry.

    • 1:00pm
      Retail Vision - Applying AI to understand customer behaviors
      Atif Kureishy

      Talk description is coming soon! 

    • 1:30pm
      Will you be better off in a “Smart City”?
      Tom Schenk, Jr.

      Technology and data has begun to be embedded in the fundamental structure and processes of the cities and states in which we live. Smart cities are looking to incorporate data and technology that improve the quality of life for their residents and help governments be more efficient. But what does that mean and how can it be meaningful to you? We’ll explore practical, real-life ways that data science, internet of things (IoT), and open data are being used to help cities become more livable and modern that range from predictive analytics, API feeds of real-time public data, and connected sensors to provide block-by-block insights. More importantly, we’ll discuss how it matters to residents and visitors to the city.

Thank You to our Sponsors