Data Science. Machine Learning. Big Data. Analytics.

Join us in Atlanta for a 3 day event, November 28-30, on Data Science and Machine Learning! First day is workshop day, and there will be 4 concurrent tracks.

A serious conference on all things Data Science, in a relaxed, fun environment.


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Click here to see last year's amazing content!

CFP NOW OPEN!

Have something to share? Call for papers is open!

CLICK HERE TO SUBMIT

REGISTRATION

Workshops must be purchased in a combo pass!

Conference-only tickets are available also.

WORKSHOPS AVAILABLE:

* subject to change, still being finalized

  • Introduction to Machine Learning with Python and TensorFlow
  • Image Classification in TensorFlow on Google Cloud
  • Data Science with R
  • Predictive Analytics - Forecasting 2.0 Machine Learning Algorithms to Help You See Around Corners
  • Data Analytics with Tableau
  • Deep Learning Models at Scale with IBM Watson
  • Intro to Data Science with Jupyter

INDIVIDUAL $445/745

November 28 and main conference Nov 29-30. For data scientists, data engineers/developers, and data architects.

GROUP $395/695

November 28 and main conference Nov 29-30. For data scientists, data engineers/developers, and data architects.

Sessions & Workshops

Industry experts and community members will present fundamentals and the latest in the world of Data Science.

Brought to you by the organizers of another world-class conference, Connect.Tech, DataSciCon.Tech will bring the energy and community spirit of our previous events for three days of learning and networking. We invite you to join us November 2018 for this unique experience!



FEATURED SPEAKERS



Keynotes to be announced soon!

Kirk Borne

Forecasting 2.0

Cui Lin

Multivariate Time Series Anomaly Detection with Deep Learning



AVAILABLE WORKSHOPS

* subject to change, still being finalized



Forecasting 2.0

Kirk Borne

Image Classification in TensorFlow on Google Cloud

Carl Osipov

Data Science with R

Jonathan Regenstein

Tableau Hands-on Workshop

Tim Lafferty

Introduction to Machine Learning with Python and TensorFlow

Brian Sletten

Deep Learning Models at Scale with IBM Watson

Nick Acosta

Intro to Data Science with Jupyter

Ozgur Ozturk

Kulsoom Abdullah




Register


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Speakers


Interested in speaking? The call for papers is still open for a few more days!

CLICK HERE TO SUBMIT

Real life use cases for Machine Learning

Heli Helskyaho

An Only One Step Ahead Guide for Machine Learning Projects

Chang Hsin Lee

Forecasting 2.0

Kirk Borne

Analytics By Design

Kirk Borne

Enriching Deep Learning (DL) & ML with Apache Solr for Questing Answering (QA) System.

Sanket Shahane

Incremental-decremental Methods for Time Series Analysis

Joe Ross

The Ultimate Stage of Machine Learning

Svetlana Levitan

Two years in the making: What’s new with Apache Cassandra 4.0?

Dinesh Joshi

Jupyter Notebook: What's in it For You

Douglas Starnes

Data visualization in mixed reality with Python

Anna Nicanorova

Artificial Intelligence Strategy: Digital Transformation Through Deep Learning

Chris Benson

Jamming with a Quantum Computer

James Weaver

The top 5 Items to make a Data Science Project Successful

Robert Joseph

Machine Learning Architecture at Scale with Spark and Elasticsearch

Dev Gupta

My Experiments with XGBoost

Vishal Patel

Machine Learning Crash Course

Samuel Taylor

Machine Learning Live: Let’s Build a Taxi Fare Predictor

Bjoern Rost

The Future of Voice Interface

S J Mallik

We’re Under Attack: The Security Weaknesses in AI

Todd Sundsted

How ML is Redefining Competitive Market Intelligence

Todd Sundsted

Choosing the right cloud, a machine learning approach

Nick Acosta

Garbage In, Model Out?

Francois Dion

Game on: a Data Analytics use case in the Financial industry

Arjuna Chala

Looking back is therapeutic but looking ahead is sometimes useful

Daniel Chertok

Stream analytics using Kafka

Tejas Patel

Data Science with R

Jonathan Regenstein

Image Classification in TensorFlow on Google Cloud

Carl Osipov

Introduction to Machine Learning with Python and TensorFlow

Brian Sletten

Tableau Hands-on Workshop

Tim Lafferty

Deep Learning Models at Scale with IBM Watson

Nick Acosta

Multivariate Time Series Anomaly Detection with Deep Learning

Cui Lin

Intro to Practical Data Science in Jupyter

Kulsoom Abdullah

Ozgur Ozturk






Workshop day: November 28

Main conference days: Nov 29-30

Wednesday Nov 28

08:00
09:00

Thursday Nov 29

Time
Room 1
Room 2
Room 3
Room 4

08:00
09:00
Conf start and welcome

Pratik Patel

09:10
10:00
Sessions
11:20
Sessions
13:20
Sessions
14:30
Sessions
16:00
Sessions

Friday Nov 30

Time
Room 1
Room 2
Room 3
Room 4

09:00
Morning Keynote
10:10
Sessions
11:20
Sessions
13:00
Sessions
14:10
Sessions
15:20
Sessions
16:30
Closing Keynote



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Location

DataSciCon.Tech will take place at the Cobb Galleria

Hotel Info

    Coming soon

Sponsors




Do you want to sponsor the event and connect with attendees in a meaningful way?

Click here for sponsorship info!

Code Of Conduct

DataSciCon.Tech is dedicated to providing an outstanding conference experience for all attendees, speakers, sponsors, volunteers and organizers (DataSciCon.Tech participants) regardless of gender, sexual orientation, disability, physical appearance, body size, race, religion, financial status, hair color (or hair amount), platform preference, or text editor of choice. We do not tolerate harassment of DataSciCon.Tech participants in any form. Please treat your fellow DataSciCon.Tech participants with respect, regardless of the context you’re interacting with them.



See complete code of conduct