Passar para o conteúdo principal

Miguel Godinho Matos, Diretor do Programa, enumera os conteúdos do programa:



1h Program Opening
1h Environment Setup and Configuration
2h The House of Analytics
4h Introduction to R Programming


2h Machine Learning Paradigms
6h Introduction to Unsupervised Learning
4h Introduction to Supervised Learning for Regression and for Classification
4h Building a Supervised Learning Model in R
4h Model Evaluation, Fit, Overfit and Complexity Control
4h The Expected Value Framework
4h Core Machine Learning Models
4h Ensemble Learning (Bagging, Boosting and Stacking)
9h Deep Learning *


3h Build Your Own Classification Model
3h Build Your Own Regression Model and Use the Expected Value Framework
3h Evaluate an Experiment and an Intervention in Observational Data


2h Machine Learning Exam
4h Causality, Correlation and Unobserved Effects
4h Causality in Observational Data Part 1
4h Causality in Observational Data Part 2
3h Randomized Experiments
2h Heterogenous Treatment Effects
2h Predicting Client Churn
4h Designing an Experiment to Evaluate Proactive Churn Management
2h Building an AI Strategy
2h Scoping Data Science Use Cases
2h Managing Data Science Talent
2h Life Cycle of Data Science Projects
2h Development and Implementation Infrastructure
2h Causal Analytics & Data Science Project Management Exam


Formação de Executivos - Programas de Inscrição Aberta

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