Study Abroad in Rome

Data Science and Engineering for Discovery and Diversity

Summer I 2022 Featured Program

Led by an initiative from Florida Agricultural and Mechanical University (Florida A & M University, FAMU) one of the historically black colleges and universities (HBCU) in the U.S. and ranked by US News and World Report as the #1 Public HBCU in the U.S., John Cabot University is offering a new summer course on data science and engineering and diversity.  

The course in data science and engineering provides students with a learning experience in carrying out a tangible data science analysis with a focus on the student’s choice of a real-world problem using pre-existing secondary data. The course includes a critique of the inherent biases of Data Science itself and their societal implications. The full course description is below.

The collaboration between Florida A & M and JCU is the foundation in building a vibrant international summer program that brings together data science and engineering, diversity, and the humanistic traditions of the liberal arts. This summer program will serve as a focal point for highlighting and discussing the many societal and ethical issues that arise from data science and engineering and artificial intelligence. These topics are already being discussed within the John Cabot University Institute of Future and Innovation Studies.

In support of Florida A & M University’s international education diversity initiative, John Cabot University has established automatic scholarships for students from HBCUs who enroll in any of JCU’s summer (or semester) sessions. 

We look forward to seeing you in Rome this summer!

Apply for the Summer Course 

COURSE CODE: CS 212
COURSE NAME: Data Science and Engineering for Discovery and Diversity
PREREQUISITES: Recommended CS 160, MA 100, or MA 101
SYLLABUS

COURSE DESCRIPTION 

This course introduces students to the main concepts of data science. It combines statistical and computational theories to create and implement Machine Learning and Deep Learning models for classification and prediction. Such models may have a significant impact on society, as they can be used to automate procedures and extract relevant information from large amounts of data. Also, the quality, objectivity, and preparation of training data are addressed to examine the cognitive bias that may affect the machine learning model, thus resulting in poor performance and inaccurate predictions. Students will learn how to detect and correct implicit/explicit bias often found in A.I. and Machine Learning algorithms, which is important to determining validity /veracity of information (such as found in social media) and in threat analysis (as in cybersecurity). The course includes a critique of the inherent biases of data science itself and their societal implications. Students will be guided through the process of formulating and carrying out data science analyses with real-world data, with a focus on open, pre-existing secondary data. Using popular languages such as Python, students will learn how to transform and manipulate structured and unstructured data and manage complex computational pipelines.

Faculty

Professor Yohn J. Parra Bautista

Yohn Jairo Parra Bautista is an Assistant Professor, Computer and Information Sciences Department, College of Science and Technology at Florida Agricultural & Mechanical University. He is a team member initiating research and teaching in data science and engineering and creating and promoting Artificial Intelligence and data science across the curriculum. He is instrumental in developing a project-activated data science and AI REU. His Big Data research interests are in text analysis, Data behavior and its implications in AI and ethics. In 2021 he received a Microsoft USA Data Science Fellowship. He received a PhD in Computational and Data Enabled Science and Engineering from Jackson State University in 2020. Learn more about Prof. Parra Bautista.

Professor Carlos Andres Theran-Suarez

Carlos Andres Theran-Suarez is an Instructor, Computer and Information Sciences Department, College of Science and Technology at Florida Agricultural & Mechanical University. He is a team member in data science and engineering. He is developing a data science and AI curriculum for REU programs and, as part of his research, a different machine learning model to categorize Alzheimer’s disease in patients. His main research is developing cloud computing infrastructures for big data analysis using Hadoop, Hive, and Spark. Also, He has developed different methods for hyperspectral images analysis (enhancement and classification problems) using deep learning techniques. He is a Ph.D. candidate in Computer and Information Science and Engineering at the University of Puerto Rico-Mayaguez.