Course details:

Vinicius Brei

Vinicius Brei is a Visiting Scholar and Connection Science Fellow at the Massachusetts Institute of Technology (MIT) Media Lab, Chair Tramontina Eletrik Associate Professor of Marketing at the Federal University of Rio Grande do Sul, Brazil.

Day One

Intro

  • Personal presentation and get-to-know-each-other
  • Course introduction and framework

Session One – Intro to Data Science

  • What is Data Science
  • Data Science, Machine Learning, Statistics, and Business Intelligence
  • Data Science Roadmap
  • Data Science in an Organization
  • Job roles


Session Two - Telling a story using data

  • Types and applications of data
  • Summarizing and visualizing data

Day Two


Session One – Overview of statistics for data science

  • Statistics: intro to inference
  • Hypothesis testing in business applications


Session Two – Intro to Machine Learning and Data Science Tools

  • Intro to Machine Learning
  • Tools for different data science tasks for business
  • Big data and cloud computing
Vinicius Brei

Vinicius Brei is a Visiting Scholar and Connection Science Fellow at the Massachusetts Institute of Technology (MIT) Media Lab, Chair Tramontina Eletrik Associate Professor of Marketing at the Federal University of Rio Grande do Sul, Brazil.

Big data and cloud computing concepts are not easy. To make it applicable for managers it's even harder. Luckily, Vinicius Brei, visiting scholar at MIT, is here to guide you through meanders of data science to show you the actionable implementation roadmap.

He is a former visiting scholar/post-doc of psychology/Machine Learning at Harvard University and has a dual Ph.D. degree in Sciences de Gestion from HEC Paris and in Marketing from UFRGS, Brazil. He has also been a visiting professor in different universities in Europe and in the USA. Besides his activities in academia, he has also worked for more than a decade in different marketing positions at the largest Brazilian retail bank. He researches the intersection of consumer/business analytics, judgment and decision-making, and market dynamics, with special attention to individual and organizational behavior prediction, market potential, and demand forecasting estimation. His research uses a variety of quantitative methods from behavioral experiments to machine learning. His articles have been published in many marketing and management prestigious journals, as well as in the most important international marketing conferences.