This course will introduce students to the
main concepts of data science, Covering the fundamental concepts and principles
of machine learning, and pattern recognition within hands-on experiences.
Topics include descriptive statistics [the measure of central tendency, the
measure of variation], nonparametric decision making [Nearest Neighbor, Support
Vector Machine, Decision Trees], Supervised and Unsupervised learning
techniques such as Nearest Neighbor, Decision Tree, Neural Networks, Kernel
Machines, Convolutional Neural Networks.

This course combines statistical and computational
theory to create and implement Machine Learning and Deep Learning models for
classification and prediction in solving societal problems. The course will
provide experience in formulating and carrying out a tangible data science
analysis with real-world data, with a focus on open, pre-existing secondary
data. Using popular languages like Python, students in the course will learn
how to transform and manipulate structured and unstructured data and manage complex
computational pipelines.