요약
과목명 | Course Title | 빈도 | 참고 커리큘럼 |
선형대수 | Linear Algebra | 5 | (Berkely): Linear Algebra (필수)
(Drexel): Linear Algebra
(Charleston): Linear Algebra (필수)
(Iowa): Matrices and Linear Algebra (필수)
(Rochester): Linear Algebra with Differential Equations |
미적분학 | Calculus | 8 | (Adams 2020): Calculus I, II
(Berkeley): Calculus I, II (필수)
(Charleston): Introductory Calculus (필수)
(Drexel): Calculus I, II
(Iowa): Calculus I, II, III (필수)
(Rochester): Calculus I, II (필수)
(UNH): Calculus I, II, Multidimensional Calculus |
증명수학 | Mathematical Proof | 1 | (UNH): Mathematical Proof |
통계학개론 | Intro. to Statistics | 5 | (Adams 2020): Intro. to Statistics
(Berkeley): Concepts of Statistics
(Charleston): Statistical Methods I, II (필수)
(Iowa): Intermediate Statistical Concepts and Methods (필수)
(Purdue): Statistical Theory (필수) |
확률과통계 | Probability and Statistics | 6 | (Adams 2020): Computational Bayesian Statistics
(Berkeley): Concepts of Probability, Stochastic Processes
(Michigan): Intro. to Probability and Statistics (필수)
(Purdue): Probability (필수)
(Rochester): Probability (필수)
(UNH): Probability with Applications, rinciples of Statistical Inference |
수리통계학 | Mathematical Statistics | 2 | (Adams 2020): Mathematical Statistics
(Rochester): Mathematical Statistics (필수) |
수치계산 | Numerical Methods | 1 | (Purdue): Numerical Methods |
다변량데이터분석 | Multivariate Data Analysis | 1 | (Iowa): Intro. to Multivariate Data Analysis |
범주형데이터분석 | Categorical Data Analysis | 1 | (Iowa): Intro. to Categorical Data Analysis (필수) |
응용통계학 | Applied Statistics | 2 | (Adams 2020): Applied Statistics
(Michigan): Applied Regression Analysis (필수) |
전산통계 | Computational Statistics | 2 | (Rochester): Intro. to Computational Statistics (필수)
(UNH): Statistics in Computing and Engineering |
실험설계 | Experimental Design | 1 | (Iowa): Intro. to Experimental Design |
통계적학습 | Statistical Learning | 1 | (Charleston): Statistical Learning I, II (필수) |
컴퓨팅개론 | Intro. to Computing | 4 | (Adams 2020): Intro. to Computing
(Purdue): Foundations of Computer Science (필수)
(Rochester): Intro. to Computer Science (필수)
(UNH): Applied Computing 1: Foundations of Programming |
프로그래밍기초 | Basics of Programming | 3 | (Charleston): Computer Programming I, II (필수)
(Drexel): Computer Programming I, II
(Purdue): Python Programming (필수) |
구조적프로그래밍 | Structural Programming | 2 | (Berkeley): Program Structures (필수)
(Charleston): Functional and Logic Programming |
계산이론 | Theory of Computation | 5 | (Charleston): Automata and Formal Languages
(Drexel): Mathematical Foundations of Computer Science
(Iowa): Theoretical Foundations of Computer Engineering (필수)
(Purdue): Intro. to Theory of Computation
(UNH): Intro. to the Theory of Computation |
자료구조 | Data Structures | 9 | (Adams 2020): Intro. to Data Structures
(Berkeley): Data Structures (필수)
(Charleston): Data Structures and Algorithms (필수)
(Drexel): Data Structures (필수)
(Iowa): Intro. to Data Structures (필수)
(Michigan): Programming and Elementary Data Structures (필수)
(Purdue): Data Structures & Algorithms (필수)
(Rochester): Data Structures and Algorithms (필수)
(UNH): Data Structures Fundamentals |
알고리즘 | Algorithms | 8 | (Adams 2020): Adv. Data Structures & Algorithms
(Berkeley): Efficient Algorithms and Intractable Problems
(Charleston): Advanced Algorithms (필수)
(Iowa): Intro. to the Design and Analysis of Algorithms (필수)
(Michigan): Data Structures and Algorithms (필수)
(Purdue): Intro. to the Analysis of Algorithms
(Rochester): Design and Analysis of Efficient Algorithms
(UNH): Data Structures and Algorithms |
이산수학 | Discrete Mathematics | 5 | (Charleston): Discrete Structures I (필수)
(Drexel): Discrete Computational Structures
(Iowa): Discrete Computational Strcutures (필수)
(Michigan): Discrete Mathematics
(Rochester): Discrete Mathematics (필수) |
디지털신호처리 | Digital Signal Processing | 2 | (Berkeley): Digital Signal Processing
(Iowa): Machine Learning: A Signal Processing Perspective |
컴퓨터구조 | Computer Organization | 2 | (Charleston): Architecture of Advanced Computer Systems
(Rochester): Computer Organization |
임베디드 시스템 | Embedded Systems | 1 | (Iowa): Embedded Systems II: Mobile Platforms |
객체지향프로그래밍 | Object-oriented Programming | 2 | (Iowa): Object-oriented Programming (필수)
(Purdue): Problem Solving and Object-Oriented Programming (필수) |
운영체제 | Operating Systems | 1 | (Berkeley): Operating Systems and Systems Programming |
데이터베이스 | Database | 9 | (Adams 2020): Database Management Systems
(Berkeley): Intro. to Database Systems
(Charleston): Dataset Organization and Management (필수)
(Drexel): Database Management Systems (필수)
(Iowa): Intro. to Database Management Systems (필수)
(Michigan): Databases and Applications (필수)
(Purdue): Intro. to Relational Database
(Rochester): Intro. to Databases (필수)
(UNH): Database Systems and Technologies |
데이터베이스시스템프로그래밍 | Database System Programming | 2 | (Charleston): Concepts of Database Implementation
(Iowa): Principles and Internals of Database Systems |
정보검색및관리 | Information Retrieval and Management | 3 | (Drexel): Data Curation (필수), Information Retrieval Systems
(Purdue): Web Information Search and Management
(Michigan): Data Management and Applications |
컴퓨터네트워크 | Computer Networks | 2 | (Berkely): Intro. to the Internet: Architecture and Protocols
(Charleston): Computer Networks |
소프트웨어공학 | Software Engineering | 3 | (Drexel): Systems Analysis I, II
(Iowa): Foundations and Applications of Program Analysis
(Purdue): Software Engineering I |
정보시스템 | Information Systems | 3 | (Adams 2020): Information Systems & Data Mgmt
(Drexel): Intro. to Information Systems (필수)
(Purdue): Information Systems |
퍼스널컴퓨팅 | Personal Computing | 1 | (Drexel): Social Aspects of Information Systems (필수)
(Iowa): Privacy Preserving Algorithms and Data Secuirty |
웹서비스 | Web Services | 1 | (Charleston): Service-Oriented Computing |
인공지능 | Artificial Intelligence | 5 | (Adams 2020): Artificial Intelligence
(Charleston): Principles of Artificial Intelligence (필수)
(Drexel): Artificial Intelligence
(Purdue): Intro. to Artificial Intelligence
(Rochester): Intro. to Artificial Intelligence (필수) |
데이터마이닝 | Data Mining and Analytics | 4 | (Berkeley): Data Mining and Analytics
(Charleston): Data Mining (필수)
(Iowa): Data Mining
(Rochester): Data Mining (필수) |
지식표현및추론 | Knowledge Representation and Reasoning in AI | 1 | (Rochester): Knowledge Representation and Reasoning in AI |
머신러닝 | Machine Learning | 8 | (Berkeley): Intro. to Machine Learning
(Drexel): Machine Learning
(Iowa): Concepts and Applications of Machine Learning (필수)
(Michigan): Machine Learning and Data Mining (필수)
(Purdue): Data Mining and Machine Learning (필수)
(Rochester): Machine Learning
(UNH): Machine Learning Applications and Tools |
딥러닝 | Deep Learning | 3 | (Berkeley): Designing, Visualizaing, and Understanding Deep Networks
(Drexel): Applied Deep Learning (필수)
(Rochester): Deep Learning and Graphical Models |
컴퓨터그래픽스 | Computer Graphics | 1 | (Charleston): Principles of Computer Graphics |
분산시스템 | Distributed Systems | 1 | (Iowa): Intro. to Parallel Algorithms and Programming, Distributed Systems |
고성능컴퓨팅 | High Performance Computing | 2 | (Adams 2020): High Performance Computing
(Iowa): Intro. to High Performance Computing |
프로그래밍언어론 | Theory of Programming Languages | 3 | (Berkeley): Programming Languages and Compilers
(Charleston): Principles of Compiler Design
(Iowa): Principles of Programming Languages |
웹프로그래밍 | Web Programming | 1 | (Charleston): Server-Side Web Programming |
유저인터페이스개발 | User Interface Development | 1 | (Charleston): User Interface Development |
컴퓨터보안 | Computer Security | 4 | (Berkeley): Computer Security
(Drexel): Intro. to Computing and Security Technology (필수)
(Iowa): Basics of Information System Security
(Purdue): Intro. to Cryptography |
데이터사이언스개론 | Intro. to Data Science | 7 | (Berkeley): Foundations of Data Science (필수)
(Charleston): Intro. to Data Science (필수)
(Drexel): Intro. to Data Science (필수)
(Iowa): Intro. to Data Science (필수)
(Purdue): Intro. to Data Science (필수)
(Rochester): Tools for Data Science
(UNH): Introduction to Data Science and Analytics |
데이터사이언스통계학 | Statistics for Data Science | 2 | (Iowa): Probability and Statistical Theory for Data Science (필수)
(Purdue): Statistics for Data Science (필수) |
데이터엔지니어링 | Data Engineering | 2 | (Berkeley): Data Engineering
(Drexel): Data Science Programming I, II (필수) |
데이터모델링및시각화 | Data Modeling and Visualization | 5 | (Adams 2020): Applied Modeling and Visualization
(Berkeley): Intro. to Data Visualization
(Drexel): Information Visualization, Exploratory Data Analytics (필수)
(Iowa): Data Acquisition and Exploratory Data Analysis (필수)
(Purdue): Intro. to Data Visualization |
바이오인포매틱스 | Bioinformatics | 1 | (Iowa): Intro. to Bioinformatics and Computational Biology |
소셜미디어데이터분석 | Social Media Data Analysis | 1 | (Drexel): Social Media Data Analysis (필수) |
시계열분석및예측 | Time Series Analysis and Forecasting | 2 | (Berkeley): Intro. to Time Series
(Rochester): Time Series Analysis & Forecasting in Data Science |
자연어처리 | Natural Language Processing | 2 | (Berkeley): Natural Language Processing
(Rochester): Natural Language Processing, Statistical Speech and Language Processing |
빅데이터분석 | Big Data Analytics | 3 | (Drexel): Cloud Computing and Big Data (필수)
(Iowa): Software Tools for Large Scale Data Analysis (필수), Big Data Analytics and Optimization
(Purdue): Large Scale Data Analytics (필수) |
컴퓨터비전 | Computer Vision | 1 | (Rochester): Machine Vision |
추천시스템 | Recommender Systems | 1 | (Drexel): Recommender Systems (필수) |
비즈니스애널리틱스 | Business Analytics | 2 | (Berkeley): Advanced Business Analytics
(Drexel): Visual Analytics |
데이터사이언스캡스톤 | Data Science Capstone | 8 | (Adams 2020): Data Science for All
(Berkeley): Applied Data Science with Venture Applications: Data-X
(Charleston): Data Science Capstone (필수)
(Drexel): Data Science Projects (필수)
(Iowa): Data Science Capstone (필수)
(Michigan): Data Science Applications (필수)
(Purdue): Data Science Capstone (필수)
(Rochester): Data Science Capstone (필수) |
참고
(Adams 2020) Creating a Balanced Data Science Program, SIGGSE
(Rochester) Goergen Institute for Data Science, BA and BS Major Requirements, University of Rochester (link)
