Spring 2024

Course Code Title/Instructor Meets
DATA 180-01 Introduction to Data Science
Instructor: Mark D'Arcy
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. Offered every semester.
09:00 AM-10:15 AM, TR
TOME 120
DATA 180-02 Introduction to Data Science
Instructor: Mark D'Arcy
Course Description:
Cross-listed with COMP 180-02 and MATH 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. Offered every semester.
10:30 AM-11:45 AM, TR
TOME 120
DATA 200-01 Data Systems for Data Analytics
Instructor: Dick Forrester
Course Description:
Cross-listed with COMP 200-01. A comprehensive introduction to the access, structure, storage, and representation of data as it applies to data analytics. The tabular data model, relational data model, and hierarchical data model are studied. Topics include the use of structured query language (SQL) to extract and manipulate data from a relational database, APIs to extract information from web services, and methodologies for processing unstructured data. The primary programming language used in the course is Python.Prerequisite: COMP 130 or 132, and DATA/COMP/MATH 180. Cross-listed with COMP 200. Offered every semester.
01:30 PM-02:45 PM, TF
TOME 121
DATA 300-01 Statistical and Machine Learning
Instructor: Mohammad Naderi Dehkordi
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.
03:00 PM-04:15 PM, TF
TOME 121
DATA 400-01 Data Analytics Capstone
Instructor: Eren Bilen
Course Description:
Permission of Instructor Required. A capstone course that provides students with an opportunity to apply the data analytics knowledge they have acquired to independent research projects. At least one of the projects must be derived from the chosen discipline specific electives. Students will get experience in all aspects of solving real-world problems, including project planning, consideration of legal and ethical issues, collecting and processing data, analyzing and interpreting results, writing reports, and giving presentations.Prerequisites: DATA 300, completion of ECON 298 or concurrent registration in MATH 325, DATA 198/PHIL 258 and the three-course disciplinary sequence. Offered every spring.
01:30 PM-02:45 PM, TF
ALTHSE 07
DATA 400-02 Data Analytics Capstone
Instructor: Eren Bilen
Course Description:
Permission of Instructor Required. A capstone course that provides students with an opportunity to apply the data analytics knowledge they have acquired to independent research projects. At least one of the projects must be derived from the chosen discipline specific electives. Students will get experience in all aspects of solving real-world problems, including project planning, consideration of legal and ethical issues, collecting and processing data, analyzing and interpreting results, writing reports, and giving presentations.Prerequisites: DATA 300, completion of ECON 298 or concurrent registration in MATH 325, DATA 198/PHIL 258 and the three-course disciplinary sequence. Offered every spring.
03:00 PM-04:15 PM, TF
ALTHSE 07
DATA 560-01 Mutational Profile as a Predictor of Survival in Acute Myeloid Leukemia
Instructor: Michael Roberts
Course Description:

DATA 560-02 Bioinformatic Analysis of Leukemia Transcriptomes
Instructor: Jeffrey Forrester
Course Description:

DATA 560-03 Using Natural Language Processing to Enhance Google Reviews
Instructor: Eren Bilen
Course Description:

DATA 560-04 Turning Point: A Statistical Analysis of the 1964 Dice Game of Strategy and Choice
Instructor: Eren Bilen
Course Description: