Fernando Bação

Professor

At the moment, Fernando Baço holds the position of Full Professor at the NOVA Information Management School (NOVA IMS). He graduated from Lisbon's NOVA University with a doctorate in information management. He is currently the director of the Ph.D. program in statistics and information management and the president of the pedagogical council. Over the course of his scientific career, he has published more than 60 papers in journals and conferences both domestically and abroad. He has also won honors for the caliber of his work. His studies concentrate on business analytics, information systems management, decision support systems, and information management.

Courses By Speaker

Certified Executive Program in Data Science

Given the massive amounts of data that are produced these days, it has become essential that industries adopt data science an integral part of their effort to grow their business, increase customer satisfaction and make better business decisions. Data science deals with vast volumes of data using modern tools and techniques and complex machine-learning algorithms to identify unseen patterns, derive meaningful information, and inform data-driven business decisions. Data Science depend on several technical concepts such as Machine Learning, Modeling, Databases Statistics and Programming. This certification program will discuss the foundations of machine learning and data science and will cover data science methodology and explorations and several important topics such as supervised and unsupervised learning models as well as data pre-processing techniques and data visualization.

Course detail:

High-level overview of data science and machine learning
Data science methodology and data exploration for leaders and managers
Working with data pre-processing and data visualization - data pre-processing and error estimates, metrics for numeric and categorical data, technical standards, the problem with missing values, estimates of error of regression and classification systems, and techniques for feature extraction and projections
Unsupervised learning models - market basket analysis, recency-frequency-monetary (RFM) analysis, clustering algorithms (K means, self-organizing maps (SOMs), additional topics on clustering)
Supervised learning models - decision theory and Bayesian learning systems, learning and classification based on instances, induction of decision trees (general principles, discrete-diffraction-transform (DDT) algorithm, others), ensemble classifiers, neural networks (single perceptron, multi-layer perceptron (MLP), introduction to deep learning neural networks), and support vector machines