B.S. in Data Science and Artificial Intelligence < University of Miami
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B.S. in Data Science and Artificial Intelligence
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B.S. in Data Science and Artificial Intelligence
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Overview
Data science (DS) is an interdisciplinary field focused on extracting knowledge from large data sets and applying that knowledge to solve problems. Artificial intelligence (AI) is the study of systems that perceive their environment and take actions that maximize their chance of achieving their goals. The two fields are interwoven, with DS systems using AI techniques for knowledge extraction and representation, and AI systems improving by examination of existing performance data. The proposed new major in Data Science and Artificial Intelligence gives students critical skills in both DS and AI, and teaches them about the interplay between the two fields. This knowledge is based on a foundational underpinning of computer science and mathematics, provides a range of electives to develop skills in subareas, and exposes the application of DS and AI in various domains.
Curriculum Requirements for B.S. in Data Science and Artificial Intelligence
Course List
Code
Title
Credit Hours
MAJOR REQUIREMENTS
Core Computer Science Courses
CSC 113
Data Science for the World (New course: Data Science for Everyone)
CSC 120
Computer Programming I
CSC 220
Computer Programming II
CSC 315
Introduction to Python for Scientists
CSC 317
Data Structures and Algorithm Analysis
CSC 545
Advanced Artificial Intelligence
CSC 546
Machine Learning
Core Mathematics Courses
MTH 161
Calculus I (Also fulfills Quantitative Proficiency Skills Requirement)
MTH 162
Calculus II
MTH 210
Introduction to Linear Algebra
MTH 224
Introduction to Probability and Statistics
MTH 309
Discrete Mathematics I
or
MTH 230
Introduction to Abstract Mathematics
Techniques
CSC 115
Python Programming for Everyone (only if taken before
CSC 120
CSC 322
System Programming
CSC 423
Database Systems
CSC 506
Logic and Automated Reasoning
CSC 542
Statistical Learning with Applications
CSC 543
Computer Vision with Deep Learning
CIM 563
Design with AI
ECE 553
Neural Networks
ECE 574
Agent Technology
EPS 351
Intro to Statistics for the Social, Behavioral, and Educational Sciences
EPS 401
Applied Regression in the Social and Behavioral Sciences
EPS 402
Statistical Programming: R, Python, and SQL for Social and Behavioral Data
JMM 331
Introduction to Infographics and Data Visualization
JMM 429
Advanced Infographics and Data Visualization
MTH 524
Introduction to Probability
MTH 525
Introduction to Mathematical Statistics
MTH 542
Statistical Analysis
PHI 330
Ethics
PSY 292
Introduction to Biobehavioral Statistics Section B (not permitted with
MTH 524
MTH 525
, or
MTH 542
Applications
CSC 210
Computing for Scientists
CSC 329
Introduction to Game Programming
CSC 410
Computer Science Project Planning (project must be approved as DS&AI related)
CSC 411
Computer Science Project Implementation (project must be approved as DS&AI related)
CSC 412
Computer Science Internship (project must be approved as DS&AI related)
CSC 549
Biomedical Data Science
CSC 550
Computational Neuroscience
APY 313
Data science of culture and language
GEG 305
Spatial Data Analysis I
GEG 310
Geographic Information Systems I
GEG 405
Spatial Data Analysis II
GEG 410
Geographic Information Systems II
PSY 110
Introduction to Psychology
PSY 290
Introduction to Research Methods
Additional Required Course for the Major
PHI 115
Social and Ethical Issues in Computing
GENERAL EDUCATION REQUIREMENTS
Written Communication Skills:
WRS 105
First-Year Writing I
WRS 106
First-Year Writing II
or
WRS 107
First-Year Writing II: STEM
or
ENG 106
Writing About Literature and Culture
Quantitative Skills (3 credits) (fulfilled through
MTH 161
Areas of Knowledge:
Arts & Humanities Cognate
People & Society Cognate
STEM Cognate (9 credits) (fulfilled through the major)
ADDITIONAL REQUIREMENTS FOR THE B.S. DEGREE
At least 3 credit hours in Natural Science
Language Requirement
Advanced Writing and Communication Requirement
Electives
28
Total Credit Hours
120
To fulfill the Advanced Writing and Communication Skills requirement, students must complete 4 "W" courses including one of the following;
CSC 405
Computer Science Seminars Reports
CSC 410
Computer Science Project Planning
CSC 431
Introduction to Software Engineering
or
WRS 233
Advanced Writing for STEM
Plan of Study
Plan of Study Grid
Freshman Year
Fall
Credit Hours
CSC 115
Python Programming for Everyone
CSC 113
Data Science for Everyone
MTH 161
Calculus I
WRS 105
First-Year Writing I
2nd Language
Credit Hours
17
Spring
CSC 120
Computer Programming I
MTH 162
Calculus II
WRS 106
First-Year Writing II
2nd Language
Credit Hours
14
Sophomore Year
Fall
CSC 220
Computer Programming II
MTH 309
Discrete Mathematics I
PHI 115
Social and Ethical Issues in Computing
P&S Cognate
2nd Language
Credit Hours
16
Spring
CSC 317
Data Structures and Algorithm Analysis
MTH 224
Introduction to Probability and Statistics
P&S Cognate
Natural Science
Elective
Credit Hours
15
Junior Year
Fall
CSC 315
Introduction to Python for Scientists
MTH 210
Introduction to Linear Algebra
P&S Cognate
Elective
Elective
Credit Hours
15
Spring
CSC 546
Machine Learning
Application
A&H Cognate
Elective
Elective
Credit Hours
15
Senior Year
Fall
CSC 545
Advanced Artificial Intelligence
Application
A&H Cognate
Elective
Elective
Credit Hours
15
Spring
Application
A&H Cognate
Elective
Elective
Elective
Credit Hours
15
Total Credit Hours
122
Mission
The program aims to prepare students for professional and research careers in DS and AI, by giving them an understanding of both the principles and the practice of the two areas. The core courses will provide common knowledge that is necessary for all aspects of DS and AI; the elective courses will provide advanced knowledge in chosen subareas, and the application courses will illustrate how techniques in DS and AI can be applied in a range of domains. Additionally, the mathematics and statistics courses provide a formal basis for DS and AI techniques, and the ethics courses teach how DS and AI should be used in modern society. Students with this major in DS and AI will find employment in a range of industries, or to continue into academic or industrial research.
Learning Outcomes
Students will be able to:
Write efficient computer programs in several programming languages (minimally Python and Java), using appropriate data structures, to solve application problems.
Use data analysis languages and libraries for the analysis of large data sets.
Apply basic and advanced techniques of AI.
Relate mathematical concepts and techniques to programming, data analysis, and AI algorithms.
Use specialized tools and techniques from DS and AI, for data repositories, statistical analysis, data visualization, machine learning, etc.
Translate their DS and AI skills to solve problems in application domains beyond computer science and mathematics.
Use DS and AI in an ethical way.
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