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Miguel Godinho Matos, Program Director, details the contents of the program:

 

INTRODUCTION

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

STATISTICAL FOUNDATIONS

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 *

IN CLASS GROUP WORK

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

EXAM PART I

2h Machine Learning Exam

CAUSAL ANALYTICS

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

GUIDED DATA SCIENCE CASE

2h Predicting Client Churn
4h Designing an Experiment to Evaluate Proactive Churn Management

DATA SCIENCE PROJECT 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

EXAM PART II

2h Causal Analytics & Data Science Project Management Exam

Contacts

Executive Education - Open Programs

Mafalda Gato
E-mail: mgato@ucp.pt
Tel: (+351) 215 906 007

ALTA DIGITAL