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Data Science: Big Data, AI and Analytics

Brochura e Calendário Candidaturas

INTRODUCTION

1h		Program Opening Miguel Godinho Matos
1h		Environment Setup and Configuration Miguel Godinho Matos
2h		The House of Analytics Miguel Godinho Matos
3h		Introduction to R Statistical Programming Miguel Godinho Matos

BIG DATA FOUNDATIONS

3h		Big Data Tools Eco System Miguel Costa
4h		Big Data Tools Hands-on Miguel Costa

MACHINE LEARNING

3h		Introduction to Unsupervised Learning Miguel Godinho Matos
4h		Introduction to Supervised Learning for Regression and for Classification Miguel Godinho Matos
4h		Model Evaluation, Fit, Overfit and Complexity Control Miguel Godinho Matos
4h		Popular Supervised Machine Learning Models in R Miguel Godinho Matos

IN CLASS GROUP WORK

3h		Build Your Own Classification Model Miguel Godinho Matos Filipa Reis

MACHINE LEARNING

4h		Detail Deep Learning * Joren Gijsbrechts * Lectured in English
4h		Ensemble Learning (Bagging, Boosting and Stacking) Miguel Godinho Matos
4h		The Expected Value Framework Miguel Godinho Matos

IN CLASS GROUP WORK

4h		Build Your Own Regression Model and Use the Expected Value Framework Miguel Godinho Matos Filipa Reis

CAUSAL ANALYTICS

4h		Statistics for Business Analytics Filipa Reis
3h		Causality, Correlation and Unobserved Effects Filipa Reis
3h		Causality in Observational Data Filipa Reis
8h		Randomized Experiments Filipa Reis

GUIDED DATA SCIENCE CASE

4h		Data Science Case of Customer Retention Miguel Godinho Matos

IN CLASS GROUP WORK

4h		Evaluate an Experiment and an Intervention in Observational Data Miguel Godinho Matos Filipa Reis

* Módulo lecionado em Inglês

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