The Data Science landscape: foundations, tools, and practical applications
Dr. Oge Marques - CEECS, Florida Atlantic University

TUTORIAL

Goal
The goal of the proposed tutorial is to provide an overview of data science principles, tools, technologies, models, applications, and best practices.

Learning objectives
At the end of the proposed training program participants will:
1. Understand what is driving the widespread adoption of Data Science across all industries.
2. Understand commonalities and practical differences between various Data Science methods.
3. Appreciate when and why models are needed, the limitations of models, and how models can be used to support conclusions.
4. Become resourceful and capable of navigating the web of online resources for Data Science.
5. Become more discriminating in their assessment of published results in the field of Data Science.

Duration
The proposed duration is 4 hours, with a break.

Topics

1. What is Data Science and why is it so popular now?
2. Data Science terminology and fundamental concepts
3. The Data Science workflow and ecosystem
4. Exploratory data analysis (EDA)
5. Statistics and Data Science
6. Machine Learning and Data Science
7. Standard Data Science tasks
    a. Clustering (segmentation)
    b. Anomaly (outlier) detection
    c. Association-rule mining
    d. Prediction (classification and regression)
8. Data Science tools, languages and libraries
9. Social and legal challenges in Data Science
10. Data Science: latest developments and future trends


BIO


Oge Marques is a Professor of Computer Science and Engineering in the College of Engineering and Computer Science and, by courtesy, a Professor of Information Technology in the College of Business at Florida Atlantic University (FAU) (Boca Raton, FL). He received his PhD in Computer Engineering from FAU in 2001 and a Master in Electronic Engineering from Philips International Institute (the Netherlands).
He is a world-renowned expert in the area of intelligent processing of visual information, which encompasses the fields of image processing, computer vision, human vision, artificial intelligence (AI) and machine learning, with more than 120 publications in these areas, including 10 books. His research has been funded by the National Science Foundation (NSF), Office of Naval Research (ONR), and the Department of Defense (DoD), as well as private foundations and companies. His current research focuses on the intersection of AI and medicine, particularly the use of deep learning for medical image analysis in radiology, pathology, and dermatology.
Dr. Marques is a Senior Member of both the IEEE (Institute of Electrical and Electronics Engineers) and the ACM (Association for Computing Machinery). He is also an ACM Distinguished Speaker, a Fellow of the Leshner Leadership Institute of the American Association for the Advancement of Science (AAAS), Tau Beta Pi Eminent Engineer, and a member of the honor societies of Sigma Xi, Phi Kappa Phi and Upsilon Pi Epsilon.
Prof. Marques has more than 30 years of teaching experience in different countries (USA, Austria, Brazil, Netherlands, Spain, France, and India) and has won several teaching awards, including: