Data Science & AI in Pharma & Healthcare

TRAINING: March 10th - 13th, 2020 (Amsterdam, Netherlands)

This 4-day course is intended to expose participants to data science and AI best practices in the Pharmaceutical & Healthcare industry and introduce the big data ecosystem and how it can benefit from AI. It doesn’t limit itself to analytics, but to all disciplines to which modern data relates as well. 

In the course, you will work through several case studies and examples using healthcare data and Machine Learning tools. By the end of this course, participants will become connoisseurs of all techniques and technologies related to Pharma/Healthcare and Medical research that will allow them to get the best knowledge from their data.

Meet the Training Leader: Walid Semaan

Walid Semaan is the founder and president of Matrix Training Research & Consulting, a company specialized in Data Science training programs and Research. He graduated in engineering from Ecole Supérieure d’Ingénieurs à Beyrouth. He holds a degree in finance and marketing from the Ecole Supérieure de Commerce de Paris (ESCP) and an MBA from the University ParisDauphine-Sorbonne in Paris. He is the creator and architect of the automated artificial intelligence behind “Triple One Analytics,“ winner of the Best Innovative ICT Project at the 2011 Arab Golden Chip Award. He is also the founder of Matrix the TRC “Data Science Academy” (partners with the SAS Data Science program) in which he wrote on and teaches research methodologies, data visualization, data analytics, machine and deep learning, quality control, forecasting and epidemiology as well as the big data ecosystem. Mr. Semaan is an expert trainer delivering for SAS, PWC Academy and MEIRC-PLUS Training, UAE Academy, Formatech and holds thousands of hours in training Masters University students as well as local and international companies: to name few from dozens: Central Bank of Lebanon (Lebanon), PWC (Dubai and Jordan), IPSOS (Lebanon), SCAD (Abu Dhabi), OXY Petroleum (Oman), Obeikan Group (KSA), Dallah Hospital (KSA), Smart Dubai (Dubai), DarkMatter (Dubai), Indevco Industries (Lebanon and Egypt) and Algorithm Pharma (Lebanon).

Training audience

  • Data Science
  • Big Data
  • Data Analytics and Advanced Analytics
  • Artificial Intelligence
  • Computational Chemistry
  • Statisticians
  • Scientific Computing
  • IT project leaders
  • Modeling Platforms
  • Computational Biology
  • Machine Learning
  • Computer-Aided Drug Design
  • Deep Learning 
  • AI Tools
  • Bioinformatics
  • Cheminformatics

Key take-aways

  • Understand and design data for efficient analytical use
  • Organize variables by importance
  • Distinguish valuable data from “silent” destructive data
  • Visualize and analyze data correctly and informatively
  • Compare data analysis with machine learning
  • Explore the most important predictive models
  • Find patterns for market profiling and segmentation
  • Explore the role of the internet and the big data ecosystem
  • Compare big data information flow with tradition BI systems
  • Demystify all big data components and their interactions
  • Decide between “proprietary” and “open source” technologies
  • Understand the reality behind AI and its real-world application

Benefits of attending

  • Understand and design data for efficient analytical use
  • Organize variables by importance
  • Distinguish valuable data from "silent" destructive data
  • Visualize and analyze data correctly and informatively
  • Compare data analysis with machine learning
  • Explore the most important predictive models
  • Find the pattern for market profiling and segmentation
  • Demystify all big data components and their interactions
  • Decide between "proprietary" and "open source" technologies
  • Understand the reality behind AI and it's real-world application

Course methodologies
To make things more than comprehensible, the famous Titanic case study will be analyzed under all analytics and ML algorithms and participants will translate it into Pharma/Healthcare and Medical business models. Two real medical cases from the Pharma/Healthcare and Medical research industries, Pulse and Breast Cancer, will also be overviewed all along the workshop with dozens of generic case studies in parallel to cover all the steps necessary for running: • Data visualization and statistical tests • Regressions models and Supervised Machine Learning • Neural networks • Big data architecture design • AI architecture design All analytical methods and solutions are elaborated with step-by-step case studies with different examples to consolidate the comprehension of participants. Exhaustive documentation will support some topics with an exclusive face-to-face comparison between open source and proprietary technologies like SAS - SPSS – STATISTICA - Excel – R and Python.

For details and to register, visit the website at

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