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Webinar

Introduction to Data Science Within the Enterprise

By Patrick Saoud
Oct 27, 2022 10:00 (EDT)

Overview

Tech experts may disagree on several ideas, but that “Data is the new oil” is an idea there is widespread consensus on. We are witnessing an unprecedented proliferation in the amount of data generated. Yet, many organizations don't know the value of data and how to utilize its full potential. The advent of Data Science as a coveted career track has resulted in the rise of Data Scientists in organizations. Yet, enterprises fail to grasp the importance and relevance of the field and take advantage of the opportunity it presents. We bring you a chance to stand out! 

In this one-hour informative webinar, Patrick Saoud—a dynamic Data Scientist with expertise in Statistics, Artificial Intelligence, and Computer Science—will give you an overview of Data Science and guide you to a better understanding of the field and the potential associated with it. 

Key Takeaways:    

  • Understand the value of data 
  • Understand the different types of data 
  • Understand the types of data storage 
  • Understand the different roles involving data 
  • Understand the difference between a Data Engineer, Data Analyst, Data Scientist, and Machine Learning Engineer 

Presenter

Patrick Saoud

Patrick Saoud

Lebanon
About:

Patrick Saoud is a Data Scientist with expertise in Statistics, Artificial Intelligence, and Computer Science. He completed his undergraduate studies at the American University of Beirut, followed by a Masters and Ph.D. in Operations Research from Lancaster University in the United Kingdom. His professional focus lies in Service, Innovation, Teaching, and Research.

Patrick has worked as a Data Scientist and as a Machine Learning Engineer and has also assumed the roles of Data Engineer and Statistical Consultant. In addition, he delivers training in Programming, Statistical Modeling, Machine Learning, and Data Science, and is a journal referee in the fields of Operations Research, Supply Chain Management, Machine Learning, and Time Series Analysis.

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