Fall 2022

Course Code Title/Instructor Meets
DATA 180-01 Introduction to Data Science
Instructor: Eren Bilen
Course Description:
Cross-listed with COMP 180-01 and MATH 180-01. An introduction to theprinciples and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervisedmachinelearning. The course will include an introduction to computational tools such as the R statistical environment. No prior programming experience is required.Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. An introduction to theprinciples and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervisedmachinelearning. The course will include an introduction to computational tools such as the R statistical environment. No prior programming experience is required. Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. Offered every semester.
09:00 AM-10:15 AM, TR
TOME 120
DATA 180-02 Introduction to Data Science
Instructor: Eren Bilen
Course Description:
Cross-listed with MATH 180-02 and COMP 180-02. An introduction to theprinciples and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervisedmachinelearning. The course will include an introduction to computational tools such as the R statistical environment. No prior programming experience is required.Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. An introduction to theprinciples and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervisedmachinelearning. The course will include an introduction to computational tools such as the R statistical environment. No prior programming experience is required. Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. Offered every semester.
10:30 AM-11:45 AM, TR
TOME 120
DATA 198-01 Philosophy of Data
Instructor: Chauncey Maher
Course Description:
Cross-listed with PHIL 258-01. This an introduction to philosophical issues arising in data science. Students will discuss, read and write about some important ethical issues that arise in the practice of data sciences, such as discrimination, privacy, consent, trust, and justice. To help clarify those issues, students will also learn about some connected issues in the epistemology and metaphysics of data science, such as the nature of statistical inference and of algorithms. Prerequisites: DATA/COMP/MATH 180. This course is cross-listed as PHIL 258. This an introduction to philosophical issues arising in data science. Students will discuss, read and write about some important ethical issues that arise in the practice of data sciences, such as discrimination, privacy, consent, trust, and justice. To help clarify those issues, students will also learn about some connected issues in the epistemology and metaphysics of data science, such as the nature of statistical inference and of algorithms. Prerequisites: DATA/COMP/MATH 180. This course is cross-listed as PHIL 258. Offered every fall.
01:30 PM-02:45 PM, MR
STERN 103
DATA 200-01 Database Systems and Data Management for Data Analytics
Instructor: Eren Bilen
Course Description:
Cross-listed with COMP 200-01. A comprehensive introduction to the management and manipulation of database systems as it applies to data analytics. Topics related to data query languages to relational databases and NoSQL data systems will be covered, as well as the access and acquisition of other structured and unstructured data repositories available across the Internet. An understanding of techniques for transforming and restructuring data representations to allow for analysis will also be addressed.Prerequisite: COMP 130 or 132, and DATA/COMP/MATH 180. Cross-listed with COMP 200. A comprehensive introduction to the management and manipulation of database systems as it applies to data analytics. Topics related to data query languages to relational databases and NoSQL data systems will be covered, as well as the access and acquisition of other structured and unstructured data repositories available across the Internet. An understanding of techniques for transforming and restructuring data representations to allow for analysis will also be addressed.Prerequisite: COMP 130 or 132, and DATA/COMP/MATH 180. Cross-listed with COMP 200. Offered every semester.
03:00 PM-04:15 PM, MR
TOME 121
DATA 300-01 Statistical and Machine Learning
Instructor: Xiexin Liu
Course Description:
An introduction to the fundamental concepts and methods for statistical and machine learning. Focus is given on providing both a theoretical foundation and the practical skills needed to apply machine learning to a variety of applications in various disciplines. Topics include supervised methods such as regression and classification, and unsupervised methods such as clustering and dimensionality reduction.Prerequisite: COMP/DATA 200 and MATH 225. Offered every semester.
10:30 AM-11:45 AM, TR
TOME 121
DATA 550-01 Bayesian Hierarchical Models and their Applications in Business
Instructor: Xiexin Liu
Course Description: