Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY
Brandon MacKenzie

Brandon MacKenzie
Worldwide IM Technical Sales: Large-Scale Analytics, IBM

Brandon MacKenzie is the Data Science on Hadoop leader on IBM’s Worldwide Technical Sales team for Information Management Software. He is an expert on statistical processing in Hadoop and HPC environments. Brandon earned his master’s degree from The University of Edinburgh.

Sessions

9:00am–5:00pm Tuesday, 09/29/2015
SOLD OUT
Training
Location: 1B 03
Brandon MacKenzie (IBM), John Rollins (IBM), Jacques Roy (IBM), Chris Fregly (PipelineAI), Mokhtar Kandil (IBM)
Average rating: **...
(2.50, 12 ratings)
In this three-day course, you will: * Learn how to use machine learning, text analysis, and real-time analytics to solve frequently encountered, high-value business problems, * Understand data science methodology and end-to-end work flow of problem solution including data preparation, model building and validation, and model deployment, * Use Apache Spark and other tools for analytics. Read more.
9:00am–5:00pm Wednesday, 09/30/2015
SOLD OUT
Training
Location: 1B 03
Brandon MacKenzie (IBM), John Rollins (IBM), Jacques Roy (IBM), Chris Fregly (PipelineAI), Mokhtar Kandil (IBM)
Average rating: **...
(2.60, 0 ratings)
In this three-day course, you will: * Learn how to use machine learning, text analysis, and real-time analytics to solve frequently encountered, high-value business problems, * Understand data science methodology and end-to-end work flow of problem solution including data preparation, model building and validation, and model deployment, * Use Apache Spark and other tools for analytics. Read more.
9:00am–5:00pm Thursday, 10/01/2015
SOLD OUT
Training
Location: 1B 03
Brandon MacKenzie (IBM), John Rollins (IBM), Jacques Roy (IBM), Chris Fregly (PipelineAI), Mokhtar Kandil (IBM)
Average rating: *****
(5.00, 0 ratings)
In this three-day course, you will: * Learn how to use machine learning, text analysis, and real-time analytics to solve frequently encountered, high-value business problems, * Understand data science methodology and end-to-end work flow of problem solution including data preparation, model building and validation, and model deployment, * Use Apache Spark and other tools for analytics. Read more.