Big Mountain Data & Dev

Big Mountain Data and Dev Conference

  • Cost: Free! Goldman Sachs is providing lunch both days as well!
  • Event Date: Friday October 12th & Saturday October 13th 9:00 a.m. - 5:00 p.m.
  • Location: Eccles Theater, Goldman Sachs, Neumont College of Computer Science  Map Link
  • Download the flyer: here

Utah Code Camp and Big Mountain Data have combined to give you a great diverse technical event.  Whether you are working on a data project or coding the next great cloud/mobile app the event will have something for you.  It will also give you a chance to rub elbows with 500+ of your technical friends.  This is a great chance to network with others in the community to find out what they are working on as well.  

We have some great keynotes that are listed below. We are still adding more.  The schedule should be up the week of October 1st.  

Keynotes (More Coming)


Title: Any Advanced Technology Is Indistinguishable from Magic


Famous futurist, Arthur C. Clarke said we cannot distinguish advanced technology from magic. In this keynote session magician and technologist Suyash Joshi will showcase live what that really means in a High Tech Magic Show. This session will be highly educational, inspirational and give you a peek into the future of technologies such as AI, ML, Voice User Interfaces, Augmented and Mixed Reality. You will leave not only inspired but full of possibilities to innovate in your industry on emerging technologies.

Presenter: Suyash Joshi

Title: The Incredible Disappearing Data Scientist


Critical Integration Points for the Next 10 Years of Machine Learning The last decade saw advances in compute power combine with an avalanche of open source software development, resulting in a revolution in machine learning and scalable analytics. “Data science” and “data product” are now household terms. This led to a new job description, the Data Scientist, which quickly became one of the most significant, exciting, and misunderstood jobs of the 21st century. One part statistician, one part computer scientist, and one part domain expert, data scientists seem poised to become the most pivotal value creators of the information age. And yet, danger (supposedly) lies ahead: human decisions are increasingly outsourced to algorithms of questionable ethical design; we’re putting everything on the blockchain; and perhaps most disturbingly, data science salaries are dropping precipitously as new graduates and Machine Learning as a Service (MLaaS) offerings flood the market. As we move into a future where predictive analytics is no longer a differentiator but instead a core business function, will data scientists proliferate or be automated out of a job?

In this talk, one humble data scientist attempts to cut through the hype to present an alternate vision of what data science is and can become. If not the “Sexiest Job of the 21st Century" as the Harvard Business Review once quipped, what is it like to be a workaday data scientist? What problems are we solving? How do we integrate with mature engineering teams? How do we engage with clients and product owners? How do we deploy non-deterministic models in production? In particular, we’ll examine critical integration points — technological and otherwise — we are currently tackling, which will ultimately determine our success, and our viability, over the next 10 years.

Presenter: Dr. Rebecca Bilbro is a data scientist, Python and Go programmer, teacher, speaker, and author in Washington, DC. She specializes in visual diagnostics for machine learning, from feature analysis to model selection and hyperparameter tuning, and has conducted research on natural language processing, semantic network extraction, entity resolution, and high dimensional information visualization. An active contributor to the open source software community, Rebecca enjoys collaborating with other developers on inclusive projects like Scikit-Yellowbrick - a pure Python visualization package for machine learning that extends scikit-learn and Matplotlib to support model selection and diagnostics. In her spare time, she can often be found either out-of-doors riding bicycles with her family or inside practicing the ukulele. Rebecca earned her doctorate from the University of Illinois, Urbana-Champaign, where her research centered on communication and visualization in engineering.

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