Call for Speakers

Big Mountain Data and Dev Conference

Call for Speakers is closed. Submissions are no longer possible. Sorry.
finished 279 days ago

Big Mountain Data and Dev Conference

event starts

01 Nov 2019

event ends

02 Nov 2019


4 Locations in Downtown SLC, Eccles Theater, Goldman Sachs and Neumont College of Computer Science Salt Lake City, Utah, United States

Big Mountain Data and Dev Conference is the premier technical agnostic conference in the state.  We are seeking presentations on all technical topics. From Coding in Java to Big data analysis in R. We want our attendees the chance to see many different technologies and how to use them.     The event will Span 2 days. November 1st-2nd.  We will have 70+ sessions.  We will hold these sessions in 4 different buildings over the 2 days. 

1. Neumont College of Computer Science 143 Main St, Salt Lake City, UT 84111.

2. Goldman Sachs 111 S Main Street Salt Lake City, UT 84111. 

3. Goldman Sachs 222 S Main Street Salt Lake City, UT 84111.

4. Eccles Theater for the keynotes and Vendor floor 131 Main St, Salt Lake City, UT 84111. 

See this map for an idea of where everything is. 

We have 4 keynotes planned right now. Friday morning will kick off in the Eccles theater with 2 keynotes then lunch. Saturday will be sessions in the morning and then keynotes in the afternoon. We will not have sessions running in parallel with the keynotes.

The sessions will start at 9:00 a.m each day and end around 4:30 p.m. ALL sessions will be 60 minutes. 

Why present? 

  1. Improve your skills and help others.  Presenting allows you to learn something even better than before and in the process you are helping others to gain information.  
  2. Advocate something you are passionate about.  If you really love a technology/topic share it with others!  Passion and drive show during presentations and will help others see that as well.  
  3. Name/Services Recognition.  If you are interested in getting more projects in the future or perhaps a new job/different career. Presenting on something not only shows how willing you are to learn but you are willing to help others. It is a great addition to any resume to be part of a community. 
  4. Make connections/Network.  Networking is key to moving forward in your career and presenting to others is a great way to build your network since people will want to hear from you.  
  5. You will get a really cool shirt!  I know we all have too many of these but you can say you are just one of a few that has a coveted "Speaker Shirt" from Big Mountain Data and Dev.

Session Selection

How we approach speaker selection. The schedule right now has room for about 70 sessions(still in flux). Sessions will be 60 minutes in length we suggest 45 minutes for presentations and 15 minutes for Q&A.  All presenters will only have 1 speaking slot until all presenters have a slot. Once the schedule has been filled we will then go back to the sessions and take second sessions from presenters based ranking of the abstract/presentation by committee.. I have included an example below to make this clear.

Pat Submitted 3 sessions to speak and the vote breakdown was like this. session1 = 2nd rank, session2 = 3rd rank, session3 =1st rank.

Nick Submitted 2 sessions to speak and the vote breakdown was like this. session1 = 2nd rank, session2 = 1st rank.

Pat would get Session3 placed on the schedule. Nick would then get his session2 placed on the schedule. This would continue through all speakers, after the last speaker had a slot chosen then we would go back and pat would also get session1 for 2nd rank(depending on other speakers and other ranks as well).

One of the primary goals of the event is to grow the speaker community. We encourage you submit to speak.  If you are a new speaker and would like a review of your presentation or help with practicing please contact us below.  We are happy to help you out.  

This event is designed to be a combination of Big Data and Utah Code Camp.  As we mentioned earlier we want as many technical topics represented as possible.  

If you have any additional questions contact 

Pat Wright 

finished 313 days ago
Call for Speakers
CfS opens at 3:00 AM

12 Jun 2019

CfS closes at 11:59 PM

27 Sep 2019

This event is in UTC-07:00 - Mountain Time US & Canada - time zone.
Closing time in your time zone is .

all submitted sessions

publicly listed on this page
105 submissions
Submitted sessions
Josh Kushner
  • Communicate with Impact through Data Visualization
Billy Newport
  • A data lake is just a big data warehouse, not true...
Sara Jones
  • Keynote: Humanizing personal identity in a tech world
Khaja Siddiqui, Mitchell Murray, Mitchell Murray
  • Demystifying the Blockchain for Beginners
Brian Conneally, Andrew Webb
  • Organic, Free-Range Data Intelligence
Andrea(Yaojun) Zheng
  • Natural Language Processing with R
Pat Wright
  • Why does all this community stuff matter?
Ben Taylor
  • Scary AI Models: Teaching AI To Play The Xbox
David Batten
  • Zero Bugs - Developing with a No-Bug Mindset
Matthew Davids, Ajay Vijayan
  • Cronus - Generic Task Execution / Management
show all submissions
Melissa Kitto
  • Lessons Learned from High-Performance Java
Dan Roper
  • Simplifying Data Analysis for Small to Medium Sized Businesses
  • Applying Gartner's Business Maturity Model to Take Your Business to the Next Level
Chris Rains
  • Start and grow a data and analytics company - Part 1
Andrew Jensen
  • Turn your source code into data with Static Analysis
Asaeli Junior Matelau
  • Processing Data at Scale with Broadway and Elixir
Jacob Lyman
  • Harmful Data Analysis Habits & Mistakes and How to Avoid Them
Marlon Vilorio
  • F#: .NET's Functional Programming Language
Asher Murphy
  • Dude, where's my parallel port?
Nikhil Nanivadekar
  • Do It Yourself: Collections
Dennis Beatty
  • Real-Time with Phoenix LiveView
  • Getting Started with Live Coding
Wes Novack
  • Automate AWS with the CLI and Shell Scripting
Courtney Paulson
  • How to Control for Non-Randomized Control Groups: Exploring the MatchIt Package in R
Curtis Harris
  • Working with APIs in Alteryx
Pratap Vardhan
  • Data-driven PowerPoints with Python
  • Exploratory Data Analysis with Pandas
  • Exploratory Spatial Data Analysis with Python
Dustin McQuay, Spencer Smith, Spencer Smith
  • Destroy your development environment
Dustin McQuay
  • Applying basic Functional Programming concepts to make testing easier
  • Getting Started with AWS Step Functions, Lambdas and Elastic Beanstalk
Nick Humrich
  • Strategies for Zero Down Time, Frequent Deployments
Miriah Peterson
  • Out-of-the-Box Machine Learning using GCP and other tools
Aaron Cooley
  • Lies, Damned Lies, and Statistics: Drawing the right conclusions from your data
Ilya B. Reznik
  • It’s almost 2020, where’s AI?
Kiely Clemens
  • Approaching the Technical Interview With Confidence
Michelle Hardwick
  • Creating a Data Literate Culture
Benjamin Tesch
  • Predicting the Super Bowl Winner with Python
Abby Kaplan
  • How We Built a Data Warehouse from Scratch in 8 Months
Mat Kent
  • Business Value from Technical Practices? - The Poker Chip Game
Scott Black
  • Smart Cities: Improving Public Transportation With Data
Neil Sorensen
  • Converting to .NET Core
  • On Beyond Scrum
Giri Vislawath
  • SEO basics
Josh Cummings
  • XXE Says What?
  • Look How Easy It Is For Me to Middle You
  • Multi-tenant OAuth with Spring
  • 30 Vulns in 30 Minutes
Ben Greenwalt
  • Alternative Uses For Blockchain Technology
Levi Thatcher
  • Scaling Product Experimentation
Marissa Saunders
  • Data science workflows in Python: from notebooks to production
Adam Saunders
  • Wait, Someone is still using that? Managing legacy Software
Maureen Botoman
  • How to Embrace Discomfort and be an Effective Ally
jeff Baird, Lauren Shipley
  • It's STILL Not About The Cookie
Matt Horton
  • Reporting 101
Jacob Miller
  • withdrawn topic
  • Decision Science for Data Scientists: Creating and Capturing Value
Eunice Santos
  • Maximize Time Series Data for Data Science with PostgreSQL in 3 Steps
Matthew Rasmussen
  • Getting Started With Progressive Web Apps
Brian Broderick
  • Graph Databases vs. Relational Databases: Is it time to make the switch?
Lee Jensen
  • Hot Hot and Global
Michael Berry
  • Introducing The Stable Framework™
Derek Doel
  • Analytical Maturity and Building Data Products in SAAS Start-ups
  • Data Science as a Product Discipline in Agile Software Development
Andres Arias
  • Let's build a small Serverless application in AWS
  • Let's build a small mobile application with Ionic and Firebase
Alan Quigley
  • An intro to Oracle APEX, make data work for you
Becky Christman
  • Self-Sovereign Identity (SSI) and Verified Credentials
Lindsay Egginton
  • Thinking Broadly: Primary Research For Data Scientists
Linda Rawson
  • System vs Process: What is the difference?
  • Leveraging LinkedIn for Business Development
  • Overview of NIST Risk Management Framework (RMF)
  • Introduction to Self-Publishing
  • From Country Girl to CEO
  • Advantages of using Xamarin for Cross Platform Development
Jay Askren
  • Lightning Fast Java
Jack Wilburn
  • Using Graph Databases to De-Anonymize 20,000 Ethereum Users The Easy Way
Ayla Khan
  • Build GraphQL APIs in Python with Graphene
Luciano Pesci, PhD
  • One Identity: The Unique Blockchain Identifier Data Science Needs
Dave Stokes 🐬
  • Why Boring Tech is Better than Too Exciting Tech
  • MySQL's JSON Data Type
  • MySQL Without the SQL - Oh My!
  • MySQL 8.0 Features for Developers
Oindrilla Sen
  • Aiming for a Confident Customer over a Happy Customer
Vinay Srihari
  • How to Cloud Data Warehouse
Justin Larsen
  • The Future of Work: Adapting to Technological Change
Justin Dickerson
  • How to Build a Compass for Data Science: The Data Value Chain
Norm Warren
  • Data tips and tricks for full-stack developers
  • Serverless ETL with AWS Glue
Vasu Chetty
  • Successfully Integrating Machine Learning at a Startup
Matt Horton
  • SSAS 101
Barton Poulson
  • AI accountability: Navigating the legal, ethical, and social challenges of artificial intelligence
  • Prescriptive analytics: A hands-on introduction to getting actionable insight from your data
Jonathan Turner
  • TDD in the Real World
  • Scrum Considered Harmful?
  • Absolute Beginner's Guide to Docker
  • C# 7 and 8 Features
Mark Nielsen
  • Interpretable Machine Learning with SHAP
Allan Stewart
  • Levels of Abstraction
  • Your Architecture is Coupled to Your Culture
Dave Adsit
  • How to get more done by doing less
Michael Black
  • Graph Databases - Bringing relationships to relational data
Brian Ivie
  • Build an IoT Device
  • Hands-on Arduino Workshop