AI and Big Data

General Information
Curriculum

▣ OVERVIEW

As the world continues to get smaller, the need for technology-based jobs has increased exponentially. Our AI and Big Data program boasts a curriculum that provides a solid foundation for current technological developments and beyond in the ever-changing world of technology. Our AI and Big Data program provides students with the knowledge and skills necessary for a deep understanding of the latest and most innovative technologies, with a specific emphasis on Industry 4.0.

Students will be fully versed in the analysis, design, and implementation of today’s most recent advanced technological knowledge to prepare them for the explosive job market.

Our program is one of the only all English IT programs within Korean, one of the most advanced IT countries in the world. With our new 4LAB Research Institute, students will participate in progressive research, focused on Data Science and Artificial Intelligence. Students will be well-prepared for job placement after they graduate.

Our Bachelors of Engineering in AI and Big Data offers 2 track specializations, Software Innovation or Data Sciences, selected in your Sophomore year. Each major gives our students the potential to gain employment in a variety of fields; from software developer, robotics, and blockchain to big data, finance, banking analyst, and database manager in the IT department.

▣ Program Highlights:

Specialization in Software Innovation or Data Science
Guest speakers provide real world insights to application and technology trends
4LAB Research Institute (global research center focused on data science and AI)
International faculty with specializations in AI, big data analytics, and Industry 4.0
Korea’s advanced IT industry gives extraordinary chance to study modern applications and technology

▣ Specialized courses:

Problem solving	Introduction to Industry 4.0, Freshmen Capstone Project; Research Methodology
IT infrastructure	Introduction to Computing Systems, Data Structure, and Data Bases; Operating Systems; Cloud Platform; Cloud Solution Practice; Blockchain Technology
Logical thinking and SW programming	Logical Thinking and Basic Mathematics; Mathematics for SW Engineering; Algorithms; Object-Oriented Programming; SW Engineering
Data analytics	Introduction to Data Analytics; Principles of Data Science; Big Data Platforms; Advanced Big Data Tools; Introduction To Web Analytics; Advanced Web Analytics
Artificial Intelligence Applications	Machine Learning; Deep Learning; Reinforcement Learning; Computer Vision; Natural Language Processing; 3D Modeling and Simulation; Robotics and 3D Printing

▣ Career Prospects:

Software Innovation Specialization

Software Developer
Web Developer
DevOps Engineer
Game Developer
IOT Architect
Robotics Engineer
Blockchain Engineer
IOS/Android Developer
Full Stack Developer
Computer programmer

Data Science Specialization

Business Intelligence Developer
Data Architect
Applications Architect
Enterprise Architect
Data Scientist
Data Analyst
Data Engineer
Machine Learning Scientist
Machine Learning Engineer

AI AND BIG DATA CURRICULUM

Required Courses

Study Area	Courses	Credits
Major Required	Introduction to Computing System ▼ Course Description This course provides the student with knowledge about hardware, software and data management systems. The student is provided experience with an operating system environment, application software including productivity tools, and the use of the internet to communicate and search for information.	2
	AI in Industry 4.0	2
	Algorithm ▼ Course Description In this course, we will take an in depth look at programming concepts and techniques. We will examine theoretical concepts that make the world of programming unique. We will explore problem solving strategies, and apply these techniques to solving moderately complex problems. We will create pseudocode, flowcharts, and programs to supplement the theoretical foundations.	3
	Introduction to Data Analytics ▼ Course Description Introduction to Data Analytics introduces you to the basics of data science and data analytics for handling massive databases. The course covers concepts of data mining for big data analytics, and introduces you to the practicalities of map-reduce while adopting the big data management life cycle.	3
	Data Visualization	3
	Introduction to Machine Learning ▼ Course Description In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.	3
	Deep Learning ▼ Course Description This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task.	3
	Operating Systems ▼ Course Description This course is an introduction to personal computer operating systems including installation, configuration, file management, memory and storage management, control of peripheral devices, and use of utilities.	3
	Autonomous Vehicle	3
	Field Practice x 4	-

Elective Courses

Study Area	Courses	Credits
Major Elective	Linear Algebra	2
	Statistics	2
	Data Structure ▼ Course Description Explores methods of design, write, and analyze the performance of programming language source code that handle structured data and perform more complex tasks, typical of larger software projects development. Students acquire skills in using generic principles for data representation and manipulation with view for efficiency, maintainability, and code reuse.	3
	Freshman Capstone Project ▼ Course Description Students who take this course will be expected to plan, research and carry a term project on a topic of their own choice. Students will not be required to attend a regularly scheduled class, but must meet with a faculty advisor who will supervise and grade the student's work.	1
	Essential Mathematics for AI* ▼ Course Description The course introduces students to the basics of Linear Algebra, Analytic Geometry, Differential Calculus, Vector Calculus, Matrix decompositions, Probability and Statistics, and Continuous optimization.	3
	SW Engineering	3
	Computer Networks* ▼ Course Description This course provides an introduction to computer networks, with a special focus on the Internet architecture and protocols. Topics include layered network architectures, addressing, naming, forwarding, routing, communication reliability, the client-server model, web and email protocols. Besides the theoretical foundations, students acquire practical experience by programming reduced versions of real Internet protocols.	3
	Database ▼ Course Description The course, Introduction to Database, provides an introduction to the management of database systems. The course emphasizes the understanding of the fundamentals of relational systems including data models, database architectures, and database manipulations. The course also provides an understanding of new developments and trends such as Internet database environment and data warehousing. The course uses a problem-based approach to learning.	3
	Big Data Platform ▼ Course Description HDFS, the Hadoop Distributed File System, is a distributed file system designed to hold very large amounts of data (terabytes or even petabytes), and provide high-throughput access to this information. Files are stored in a redundant fashion across multiple machines to ensure their durability to failure and high availability to very parallel applications. This module introduces the design of this distributed file system and instructions on how to operate it.	3
	Web Analytics* ▼ Course Description This course explores the impending revolution in digital analytics, one that has the potential to change both the Web analytics and business intelligence fields. Students will study Web Analytics (Adobe Analytics and Google Analytics), Audience Intelligence (ComScore MyMetrix, PlanMetrix). Additional platforms and subject areas are included that explore customer intelligence. The class will also examine newer Ad-Tech such as Programmatic Trading, The Internet of Things, Various Social Medias, Viral Marketing, Geolocation tracking, iBeacons and Convergence Analytics.	3
	Principles of Blockchain Technology ▼ Course Description This course covers in detail the technical principles & concepts behind blockchain. In addition, it seeks to provide students with the insights and deep understanding of the various components of blockchain technology and enables them to determine how to best leverage and exploit blockchain for future technology.	3
	Advanced Big Data Tools ▼ Course Description Hadoop is an open source distributed processing framework which is at the center of a growing big data ecosystem. Used to support advanced analytics initiatives, including predictive analytics, data mining and machine learning applications, Hadoop manages data processing and storage for big data applications and can handle various forms of structured and unstructured data.	3
	Cloud Platform ▼ Course Description The course presents a top-down view of cloud computing, from applications and administration to programming and infrastructure. Its main focus is on parallel programming techniques for cloud computing and large-scale distributed systems which form the cloud infrastructure. The topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems.	3
	Natural Language Processing ▼ Course Description This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Word level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.	3
	Computer Vision ▼ Course Description This course will cover methods in image processing and computer vision, with an emphasis on the state-of-the-art techniques currently used in academia and industry. Topics will include image filtering, edge detection, corner detection, segmentation, object\image\face classification, object detection, morphological operators, object tracking, camera calibration, image registration, and activity classification. Students are expected to have some familiarity with college level calculus, linear algebra, and basic probability and statistics (conditional probability, mean, variance, etc.). There will be an extensive amount of computer programming in the course.	3
	Capstone Project 1	3
	Capstone Project 2	3
	Information Security in Industry 4.0 ▼ Course Description Students will explore the full spectrum of theoretical concepts of information and computer technologies security, methodologies of hardware and software tools for disrupting technologies in the fourth industrial revolution era. Students will learn applying information security practice using software tools to solve real life information and computer security related problems.	3
	Cognitive Science	3
	AI in Digital Healthcare	3
	Internship x 4	4+
General Elective	Any General Education or Major course x 2	10

Minors

There is one minor for the AI and Big Data Department. In order to complete the minor students must take the following courses.

Minor:		18 Total Credits
Track/Minor	Essential Mathematics for AI ▼ Course Description The course introduces students to the basics of Linear Algebra, Analytic Geometry, Differential Calculus, Vector Calculus, Matrix Decompositions, Probability and Statistics, and Continuous Optimization.	3
	Computer Networks ▼ Course Description This course provides an introduction to computer networks, with a special focus on the Internet architecture and protocols. Topics include layered network architectures, addressing, naming, forwarding, routing, communication reliability, the client-server model, web and email protocols. Besides the theoretical foundations, students acquire practical experience by programming reduced versions of real Internet protocols.	3
	Web Analytics ▼ Course Description This course explores the impending revolution in digital analytics, one that has the potential to change both the Web analytics and business intelligence fields. Students will study Web Analytics (Adobe Analytics and Google Analytics), Audience Intelligence (ComScore MyMetrix, PlanMetrix). Additional platforms and subject areas are included that explore customer intelligence. The class will also examine newer Ad-Tech such as Programmatic Trading, The Internet of Things, Various Social Medias, Viral Marketing, Geolocation tracking, iBeacons and Convergence Analytics.	3
	Common Track Course x 3	12

General Electives

All students are required to take 36 credits in General Education

Study Area	Courses	Credits
Gen Edu	General Education	36

AI and Big Data Curriculum

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