Data Science (DATA)

Our world is driven by data. To navigate this world and understand the influence data and its science has on modern life, students will learn the core concepts of inference, data analysis, and computing. Students will work with real data sets from a variety of fields such as economics, geography, and sociology. Topics will include basic programming in Python, basic statistical concepts such as Bayes' Theorem and inference, and the pitfalls of bias inherent in data sets. Prerequisites: Precalculus or permission of instructor.

This course equips students with the knowledge, skills, and tools needed to analyze and interpret data, solve complex problems, and make data-driven decisions. This course is designed to give students a solid foundation in key principles and techniques of Data Science. In this course, we explore key areas of data science including question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making. The course also explores machine learning methods including regression and classification. This class provides a bridge between the introductory Data Science course (DATA-101 / DS-101) to more advanced topics.

Examine the ethical issues related to method validity and reproducibility, and social impact and responsibility. Study survival analysis models for incomplete data, experimental design and time series.

An introduction to topics in the statistical analysis of data and machine learning. Collecting, coding, validating data; exploratory data analysis; effective quantitative displays; classification and regression models; unsupervised learning.

This course will be an introduction to deep learning with artificial neural networks. The course will focus on applications and computations with software such as Python, R, and other business intelligence platforms while having significant mathematical content. Issues of appropriate uses, un/interpretability, and ethics in data will be considered in the context of employing neural network models. Topics will include artificial neural networks and training, a variety of architectures, and some applications to typical problems.