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Data Science

 

The field of Data Science deals with the theories, methodologies and tools of applying statistical concepts and computational techniques to various data analysis problems related to science, engineering, medicine, business, etc. The objective is to inspect, clean, transform and model data in order to discover useful information, suggest conclusions and support decision-making. It is an emerging topic that plays a critical role in almost every discipline of today’s science and technology and has become an indispensable component of any business, industry, enterprise, etc.

 

Data science is a highly interdisciplinary field. Data Science methodologies are mostly derived from statistics theories. The computational algorithms for implementing these statistical methodologies are based upon numerical computation and optimization, and are often executed on a large-scale hardware platform composed of massive computing units and storage devices. These kinds of data analyses can be applied to a wide range of specific problems across the natural and social sciences and serve as the foundation for artificial intelligence. Data Science can be extensively applied to economics, biology, health care, quantitative social science including global health and environmental science, and humanities (e.g., digital media). Numerous new applications are being discovered, and established techniques are being applied in new ways to solve emerging problems. Meanwhile, a variety of career opportunities are open to students with appropriate training in interdisciplinary data science.

 

Major Requirements

(Not every course listed is offered every term, and the course list will be updated periodically. Please refer to the online Course Catalog for Courses offered in 2023-2024.)

Data Science

Divisional Foundation Courses

Course Code

Course Name

Course Credit

Choose one from the following two calculus courses

MATH 101

Introductory Calculus

4

MATH 105

Calculus

4

And choose two of the following courses (PHYS 121 is strongly recommended)

BIOL 110

Integrated Science – Biology

4

CHEM 110

Integrated Science – Chemistry

4

PHYS 121

Integrated Science – Physics

4

INTGSCI 205

Integrated Science — Research Methods and Science Communication

4

Interdisciplinary Courses

Course Code

Course Name

Course Credit

COMPSCI 201

Introduction to Programming and Data Structures

4

STATS 302

Principles of Machine Learning

4

STATS 303

Statistical Machine Learning

4

STATS 401

Data Acquisition and Visualization

4

STATS 402

Interdisciplinary Data Analysis

4

Disciplinary Courses

Course Code

Course Name

Course Credit

MATH 201

Multivariable Calculus

4

MATH 202

Linear Algebra

4

MATH 206

Probability and Statistics

4

STATS 211

Introduction to Stochastic Processes

4

COMPSCI 301

Algorithms and Databases

4

MATH 304

Numerical Analysis and Optimization

4

MATH 305

Advanced Linear Algebra

4

Electives

Courses listed in the table below are recommended electives for the major. The course list reflects the most recent intellectual organization of major electives. Depending on the academic year in which you matriculated, some of the courses below may be requirements for your major. To verify required courses, always consult the requirements for the relevant class year in the bulletin of the year in which you matriculated unless you have been approved to complete the major requirements of a subsequent year.  (See Ability to Meet Major Requirements Published in Years Subsequent to Year of Matriculation.)

Programming and Software Engineering

COMPSCI 101

Introduction to Computer Science

4

COMPSCI 203

Discrete Math for Computer Science

4

COMPSCI 205

Computer Organization and Programming

4

COMPSCI 303

Search Engines

4

COMPSCI 306

Introduction to Operating Systems

4

COMPSCI 308

Design and Analysis of Algorithms

4

COMPSCI 310

Introduction to Databases

4

COMPSCI 311

Computer Network Architecture

4

COMPSCI 320

Software Reliability

4

COMPSCI 401

Cloud Computing

4

Machine Learning and AI

STATS 102

Introduction to Data Science

4

STATS 304

Bayesian and Modern Statistics

4

COMPSCI 402

Artificial Intelligence

4

STATS 403

Deep Learning

4

STATS 404

Probabilistic Graphical Models

4

Signal Processing

COMPSCI 207

Image Data Science

4

COMPSCI 302

Computer Vision

4

COMPSCI 304

Speech Recognition

4

Interdisciplinary Data Analytics

 

ECON 211

Intelligent Economics: An Explainable AI approach

4

SOSC 320

Data in the World: Applied Social Statistics

4