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Glance Over Data Science Conferences

January 3, 2017


 


Last year I was a speaker at many conferences focused on data science, big data, and data analytics. In this post I would like to share my experience with these conferences to help you choose the right conference upon your needs.

 

 

Open Data Science Conference (ODSC)

 

Talks Quality: Multi Tracks

Track: Multi Tracks

Focus: Data Science, Big Data Analytics, Tools, A.I

Num of Sales Pitches: Medium 

Pros: Top speakers with interesting topics, low ticket price, great networking, cool swags

Cons: Many tracks run in parallel makes it impossible to attend all the talks you are interested in. With the massive number of attendees, you have no room to attend many interesting talks. No meals are provided in the conference and coffee breaks are poorly organized.
 


Spark Summit

Talks Quality: Low-High. The keynotes and Databricks talks were high quality and very informative, however many talks in the other sessions were low to medium quality.

Track: Multi Tracks

Focus: Data Science, Big Data Analytics, Tools, A.I
Num of Sales Pitches: Medium

MediumPros: The official conference of Spark, Put you up-to-date on all the new features in Spark, great learning opportunities from the Spark contributors, great networking, cool swags.

Cons: High ticket price, many tracks run in parallel makes it impossible to attend all the talks you are interested in. With massive number of attendees, sometimes you couldn't find room to attend interesting talks. Coffee breaks are poorly organized.
 



Customer Analytics innovation Summit

Talks Quality: Low-High. The keynotes and Databricks talks were high quality and very informative, however many talks in the other sessions were low to medium quality.
Track: Single Track

Focus: Data Science

Num of Sales Pitches: Medium

Pros: Single track so you don't miss any interesting talk, fancy meals (breakfast, lunch), high quality coffee breaks, well organized, good panels.

Cons: High ticket price. Not many interesting talks. Few attendees, so not good for networking.

 


The Data Science Conference

 

Talks Quality: Medium - High

Track: Single Track

Focus: Data Science

Num of Sales Pitches: None

Pros: Single track so you don't miss any interesting talk, fancy meals (breakfast, lunch), high quality coffee breaks, well organized, good panels, no sales pitches, good for networking.

Cons: High ticket price, Due to the "No Recruiters" policy this is not suitable for students who are looking for job, many talks are not accessible after the conference, no swags due to the "No Sponsors" policy.

 


 

Chief Data Science Forum, USA

 

Talks Quality: High

Track: Multi Tracks

Focus: Data Science

Num of Sales Pitches: Low

Pros: top speakers, unique format where attendees set in a room with the invited speakers to discuss a selected topic via question/answer theme. Upscale meals, upscale coffee breaks, well organized.

Cons: High ticket price, few attendees, so not optimal for networking.


 


MLConf, Atlanta (Attendee)

 

Talks Quality: Medium - High

Track: Single Track

Focus: Machine Learning

Num of Sales Pitches: Low

Pros: top speakers, low ticket price, decent meals and coffee breaks

Cons: The venue used for MLConf is not good enough to host such event. The topics are restricted to machine learning. Attendees had to eat lunch standing or setting on stairs due to the lack of a dining tables.
 

 

IEEE Big Data (Academia)

 

Talks Quality: Low - Medium

Track: Multi Tracks

Focus: Big Data

Num of Sales Pitches: Low

Pros: bring the researchers on Big Data in one venue, decent meals and coffee breaks

Cons: Not well organized, multi tracks so you may miss some interesting talks, massive number of attendees makes it difficult to find space in some talks, more theory than practical. Many boring talks.


 

 

IEEE ICMLA (Academia)

 

Talks Quality: Low - Medium

Track: Multi Tracks

Focus: Machine Learning

Num of Sales Pitches: Low

Pros: bring the researchers on machine learning in one venue, decent meals and coffee breaks

Cons: Not well organized, multi tracks so you may miss some interesting talks, more theory than practical. Many boring talks.

 

 

 

Southern Data Science Conference

 

In this conference we strive to bring all the pros of the different conferences while avoiding the cons. Since this is the first year of the Southern Data Science Conference, I will talk about what we have today and what we plan to do in order to make sure this is the conference which the data science community deserve.
 

Talks Quality: To ensure high quality, we chose top speakers carefully based on their contribution to the data science community. As an example of the quality of our speakers, we have authors of three important books "Data Science from Scratch", "Data Science with Java", and "Solr in Action".

Track: Single Track

Focus: Data Science, A.I, Big Data

Num of Sales Pitches: None

Pros (Here is our plan to give you all the possible Pros): low ticket price, we will limit the number of attendees to 300 so we don't face the problem of managing massive number of attendees. We will make sure to offer fancy meals and coffee breaks. We will allow recruitment networking. We will offer nice swags. We will provide access to all the talks of the conference.

Please share your experience on these or any other data science conferences by commenting on this post.

 

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