July 30-31 at 10am - 5pm ET

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Python for Data Analysis with Metis

Level up your analytics teams with cutting edge tools and unlock the hidden potential of your data.

Introduction to Data Science Course

Learn the data science principles required to tackle real-world, data-rich problems. Starts Sept 9!

Enterprise Data Science & Analytics Training

Find out why Intel, Wells Fargo, and other Fortune 500 companies have chosen Metis to skill up their workforce.

Share your favorite #DemystifyDS insights with the Metis community!
Metis Enterprise Case Study

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

Beginner Python & Math for Data Science Course

Designed for absolute beginners preparing to apply for the bootcamp. Starts August 13!

Foundations of Data Literacy with Metis

What good is a fancy dashboard to a data illterate workforce? Make sure all your employees have the foundation they need to make better business decisions.

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Machine Learning Foundations with Metis

Get your team the skills they need to put machine learning techniques to work in your business.

Join the Metis Slack Community!

Connect with peers interested in taking their data science careers to the next level.

Metis Enterprise Case Study

Learn how Python training with Metis helped CMA Strategy Consulting build their models 22.5 times faster than before.

All data science bootcamps are not created equal.

12 questions to help you do your research

Speaker Schedule

Day 1: July 30

For Aspiring

Data Scientists

Day 2: July 31

For Business Leaders,

Managers & Practitioners

    30-Minute Talks

    • 10:00am
      Keynote
      From Aspiring to Full-fledged Data Science Professional
      Tarry Singh

      Data science is the most coveted jobs in the industry today, yet 80-90% of aspiring professionals tend to get stuck inside the launch pad unable to unleash their full potential as proficient data science professionals. In this talk, Tarry will walk through a real-world project example to illustrate fundamental techniques to help aspiring professionals confidently move from experimentation phase into a full-fledged data scientist professional role.

    • 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
      Structured Thinking and Communications for Data Scientists
      Kunal Jain

      Too often, data science aspirants focus a lot on learning the tools and the techniques involved in data science. They learn R / Python, undergo courses, but feel lost at the very first encounter with any real life data science problem. This talk focuses on the importance of structured thinking and problem solving for data scientists and provides a few frameworks which people can use.

    • 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
      Deep Learning for All
      Gabriela de Queiroz

      As a Data Scientist (or aspiring Data Scientist) we are overwhelmed by the amount of knowledge we need to have and acquire. Every day there is a new technique, a new framework, a new state of the art model. For the last few years, Deep Learning has become a hot topic and it is the main driver of many applications. But how can we start our Deep Learning journey? Which of the several deep learning frameworks should we use? Where can I find examples of code that work and that I can use without worrying about the license?

      In this talk, I will show you how you can start with Deep Learning without any previous Deep Learning knowledge and how you can have a basic ready-to-use deep learning “service” running in less than five minutes.

    • 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.

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

      Stay tuned! Talk description coming soon.

    • 10:30am
      The Joys and Perils of Scaling AI
      Peter Guerra

      This talk will detail examples of different journeys to scaling AI -- the joys and perils for each journey.

    • 11:00am
      The challenges “legacy” (pre-dot.com) companies face in becoming “Data Centric”
      Aubrey HB

      Technology evolves far faster than the Darwinian variation of the word. Companies with a legacy data estate face Brobdingnagian challenges catching up with their post-.com era competitors when it comes to using data science and becoming data driven.  While a legacy company’s longer history may mean it has richer historical data stores and industry knowledge that could be leveraged to provide it with a comparative advantage over its less well-established peers, my experience is that it is often difficult to mine these resources effectively because such efforts are fatally undermined by the legacy company’s inevitably haphazard history of technological evolution.  Invariably, a legacy company’s data ecosystem is underpinned by a massive technological mess that was inadvertently created when disparate decisions were made to put technology in place in different areas of the organization over time without any overarching roadmap for how those different technologies and datasets might one day provide deeper, more valuable insights through their integration and the subsequent revelation of intricate connections and hidden patterns that can drive innovation.  Because data is the language through which technologies speak to each other, it is important to understand the historical choices made to generate the entanglement of the technology ecosystem that underwrites a company’s many processes. Only then can one appreciate the steps necessary to bring together pertinent data that will allow a company to strategically excel at using data to drive business outcomes and benefits from blending the current data being captured across different arms of the business with the enduring repositories that already reside on premise.

    • 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

      We will explore emerging approaches for improving retail operations. By utilizing existing camera and network infrastructure, Retailers can better understand detailed information on the movements and interactions of customers and associates. The ability to bring this new “observational” data set into a larger context that includes product sales, promotions, and inventory unlocks new retail value. We will also discuss how capabilities are delivered while ensuring the preservation of customer privacy standards and regulations.

    • 1:30pm
      Smart Cities: Doing Good with Data Science
      Tom Schenk, Jr.

      Technology and data have 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 this mean for you and how can we learn best practices in data science? We’ll explore practical, real-life ways that data science, open data, and open source are being used to help cities become more livable and healthy. From this, we can also learn how to deploy data science projects to be successful and make an impact in any organization.

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