AY 2026 Undergraduate School Course Catalog AY 2026 Undergraduate School Course Catalog AY 2026 Undergraduate School Course Catalog
| 2026/02/19 |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2026年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
3rd year |
| 単位数 ／Credits |
3.0 |
| 責任者 ／Coordinator |
YEN Neil Yuwen |
| 担当教員名 ／Instructor |
YEN Neil Yuwen, YAMADA Ryuhei |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2026/02/02 |
|---|---|
| 授業の概要 ／Course outline |
Nowadays, the Web is not only the source of information for the end users. Companies migrate more of their business activities to Web based systems. We are facing increasing demands for professionals who can design large Web systems. Web engineering is a relatively new term in computer science. It can be defined as a discipline of systematic development of Web applications. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
[Corresponding Learning Outcomes] (A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society. The aim of this course is to study current concepts and methods for Web application engineering. |
| 授業スケジュール ／Class schedule |
Topics to be discussed include, but not limited to, the followings. Topic 1: Introduction Topic 2: A Web engineering process Topic 3: Communication and planning Topic 4: Web App Architectures Topic 5: Introduction to WordPress Topic 6: Responsive Web Design Topic 7: Universal Design for Web Topic 8: About the Future Topic 9: Presentation and Discussions Notes: ** Some topics may need more than 1 time of lectures/exercises to complete. Any updates will be posted on LMS when the class begins. ** Schedule may be adjusted according to actual conditions. Any updates will be posted on LMS when the class begins. |
| 教科書 ／Textbook(s) |
No designated textbook is required but students are encouraged to follow the content of The Modern Web: Multi-Device Web Development with HTML5, CSS3, and JavaScript by Peter Gasston, No Starch Press, 2013. Dart 1 for Everyone Fast, Flexible, Structured Code for the Modern Web by Chris Strom, The Pragmatic Programmers, LLC., 2014. Anatomy of a web application using node.js, ExpressJS, MongoDB & Backbone.js by Jason Crol, 2015. Knockout.js: Building Dynamic Client-Side Web Applications by Jamine Munro, O’Reilly Media, 2015. |
| 成績評価の方法・基準 ／Grading method/criteria |
The final grade will be calculated based on the following contributions: Exercises - 55% Presentations/Reports - 15% Quiz - 20% Active Participation during lectures - 10% Please note that the proportion to each part may be adjusted according to actual conditions. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2026年度／Academic Year 4学期 ／Fourth Quarter |
|---|---|
| 対象学年 ／Course for; |
3rd year |
| 単位数 ／Credits |
3.0 |
| 責任者 ／Coordinator |
WATANOBE Yutaka |
| 担当教員名 ／Instructor |
WATANOBE Yutaka, SAXENA Deepika |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2026/02/06 |
|---|---|
| 授業の概要 ／Course outline |
This course covers many current topics of interest in software engineering. Some of the topics covered are formal methods to specify requirements of software systems, software reuse, software maintenance, software maintenance models, and evaluation of processes, products, and resources. It includes Advanced Treatment of Selected Software Engineering issues: Software Maintenance, Software Configuration Management, Software Re-engineering, Managing People, Critical Systems Development, User Interface Design and Evaluation, Emerging Technologies like Visual Programming, Security Engineering, and other advanced topics including student engineering topic. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
[Corresponding Learning Outcomes] (A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society. [Competency Codes] C-HI-003, C-SD-005, C-SE-001, C-SE-002 The objective of the course is to impart knowledge to students about methods in software development. The methods range from how to precisely specify software requirements to how to evaluate the methods and their products and required resources. This is achieved in two parts: first, lectures are given on several current topics of interest; second, students are given a programming project to design and implement a system using the Object-Oriented programming paradigm and the version management with a team. |
| 授業スケジュール ／Class schedule |
Lecture 1: The Nature of Software Engineering Topics to study: - How did software engineering become a term? - Is there a good technical solution to software development problems? - How and why are agile methods considered more people-affirming? - Compare software engineering with other professions. Lecture 2: Revisioning Software Topics to study: - Collaborative development problems, - Revision control, - VCS terminology, - Collaborative development and conflict resolution. Lecture 3: The Human Factors in Software Engineering Topics to study: - Human Diversity; - Limits to Thinking; - Knowledge Modeling; - Personality Types; - Human Factors Engineering. Lecture 4: The Managing People and Team Work Topics to study: - Selecting and Motivating Staff - Ego-less Programming - Managing Groups - The People Capability Maturity Model Lecture 5-6: User Interface Design and Evaluation Topics to study: - User Interface Design Principles - User Interaction Styles - Information Presentation - GUI Features - Message System Features - System Documentation - User Interface Design Process - Interface Evaluation Lecture 7: Visual Programming Systems Topics to study: - Terminology - Classification and Theory - A Review of Visual Programming Systems Lecture 8: Software change: Maintenance and Architectural Evolution Topics to study: -Program evolution dynamics -Software maintenance -Architectural evolution Lecture 9: Software re-engineering Topics to study: -Source code translation -Reverse engineering -Program structure improvement -Program modularization -Data re-engineering Lecture 10: Critical systems development Topics to study: -Dependable processes -Dependable programming -Fault tolerance -Fault tolerant architectures Lecture 11: Software Security Engineering Topics to study: -Security concepts -Security risk management -Design for security -System survivability The first four periods of each week are conducted in a lecture format, while the remaining two periods are dedicated to programming exercises. These exercises are structured as team-based development projects. [Preparation and Review] To effectively engage in the project, students must proactively acquire the necessary development knowledge and technical skills through self-directed study. The estimated time for learning outside of scheduled class hours is approximately 6 hours per week. |
| 教科書 ／Textbook(s) |
1.Software Engineering, 5-9th editions by Ian Sommerville, publisher: Addision-Wesley 2.Human Aspects of Software Engineering by J.E. Tomayko and O. Hazzan, Charles River Media Inc., 2004 3. User Interface Design and Evaluation by D. Stone, C. Jarrett, M.Woodroffe, Sh. Mincha 4. Lecture notes distributed by the instructor will be developed from materials collected from books, journals and proceedings papers. |
| 成績評価の方法・基準 ／Grading method/criteria |
Your final grade includes the following parts 1). Quiz: 10 points 2). Exam: 40 points 3). Project: 50 points |
| 履修上の留意点 ／Note for course registration |
It would be good if students have knowledge about basics of Software Engineering, Programming in Java and C. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
The course coordinator Yutaka Watanobe has practical work experience. He collaborated with Givery Inc. to develop the programming skill check tool and materials which can be useful for personnel assessment, training and education. He has provided a number of problems and related test data as one of main contents. He also has experience in developing large scale software in practical use, such as development environments and educational support systems. Based on his experience, he can teach the basics of Software Engineering. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2026年度／Academic Year 前期 ／First Semester |
|---|---|
| 対象学年 ／Course for; |
4th year |
| 単位数 ／Credits |
3.0 |
| 責任者 ／Coordinator |
YOSHIOKA Rentaro |
| 担当教員名 ／Instructor |
YOSHIOKA Rentaro, KAWAGUCHI Tatsuki |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
Courses preferred to be learned before this course (This course assumes an understanding of the entire or partial content of the following courses) FU14 Introduction to Software Engineering |
| 更新日／Last updated on | 2026/01/22 |
|---|---|
| 授業の概要 ／Course outline |
Students will develop software for real-world customers in teams and deepen their understanding of software engineering. In this course, each student team will receive a development request from a real customer. On average, each team will consist of 3-10 students. Teams will start by listening to customers' requests and defining the software requirements. This will be followed by design, development, and test phases. In Week 14, each team will present its project activity and the completed software and deliver it to the customer. Professional software engineers will support each team as coaches, advising on practical development tasks. Each team will report its progress to the coaches and lecturers every week. Meetings with customers will be held as required. The phases of the development will be controlled by specific deliverables that must be submitted to advance to the next phase. Teams will present at two review meetings (interim and final), where they must quantitatively analyze project work hours, accomplishments, and quality. To deliver software that best satisfies customer requirements, including functional and quality requirements, utmost creativity and effort are imperative. Proactive, self-motivated involvement by all team members is expected for successful delivery. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
[Corresponding Learning Outcomes] (A) Graduates are aware of their professional and ethical responsibilities as engineers and are able to analyze societal requirements and set, solve, and evaluate technical problems using information science technologies in society. [Competency Codes] C-GV-001, C-HI-003, C-SD-005, C-SE-001, C-SE-002, C-SP-002, C-SP-004, C-SP-006, C-SP-009, C-SP-0011-1, C-SP-012, C-SP-013, C-SP-014 1. The student understands the challenges of developing software with specific functional and quality requirements and the basic problem-solving methods. 2. The student understands the necessity of project management to develop software with limited resources (people, time, equipment) and experiences the various methods related to each stage of development (requirements definition, design, development, test). 3. The student understands the need to act quickly in an uncertain real-world environment, such as identifying multi-disciplinary problems and accurately communicating information, and will learn basic methods. Overall, the student will understand the difficulties (and rewards!) of developing practical, respectable-sized software. |
| 授業スケジュール ／Class schedule |
In each lecture (3 periods), 1. Progress report by each team (10 minutes) 2. Feedback on the reported content by instructors 3. Short lecture on the week’s activity by instructors 4. A meeting between the team and the customer will be performed. Week 1: Project Kickoff and Planning Divide roles among team members, prepare a project management system, receive an overview of the project background and requirements from the client, and develop a project plan. Week 2: Field Work Visit the client site to understand the problem and environment of the required solution. (The schedule is subject to the client’s circumstances) Week 3: Requirements Definition Analyze the problem carefully, derive clear and precise requirements, and obtain consent on the scope of the system from the customer. Week 4: Requirements Definition Analyze the problem carefully, derive clear and precise requirements, and obtain consent on the scope of the system from the customer. Week 5: Requirements Definition Analyze the problem carefully, derive clear and precise requirements, and obtain consent on the scope of the system from the customer. Week 6: Interim Review The customer will examine the progress and decide whether the team will proceed to the following phases. Week 7: Analysis Based on the requirements definition phase results, logically analyze the requirements and plan a practical solution. Week 8: Analysis Based on the requirements definition phase results, logically analyze the requirements and plan a practical solution. Week 9: Design Describe the structure and behavior of functions/objects of the system while considering programming. Week 10: Design Describe the structure and behavior of functions/objects of the system while considering programming. Week 11: Development Create source code of the software. Week 12: Development Create the source code of the software and begin testing. Week 13: Testing Test the developed software. Week 14: Final review The customer will evaluate the test results and other deliverables from the design phase and decide whether the software is valid for acceptance. *Schedule management is performed by the teams so that progress may vary, but the dates of the interim and final reviews cannot be changed. Teams are requested to prototype actively. [Preparation/Review] Between each class session and the next, carry out the project activities planned by your team (such as meetings, survey/analysis, idea generation, development, and material preparation) either individually or as a team. The standard time for preparation and review for each session is 6 to 7 hours. |
| 教科書 ／Textbook(s) |
Handouts will be provided as necessary. |
| 成績評価の方法・基準 ／Grading method/criteria |
There is no exam. The following items will assess performance. 1. Presentation at interim and final review 25% 2. Quality of deliverables (Technical documents and software) 25% 3. Individual report 40% 4. Participation in class activities and attitude 10% * Each student's contribution will be checked in weekly progress reports and the project management system. * Insights of customers and coaches will be taken into consideration for the assessment * In the individual report, each student is requested to describe their activities and contribution to the project and answer questions related to the knowledge of software engineering and project management involved. |
| 履修上の留意点 ／Note for course registration |
• Knowledge and skills acquired in the following courses are assumed. FU14 Introduction to Software Engineering IE03 Integrated Software Exercise I IE04 Integrated Software Exercise II • Students must attend an individual interview with the lecturers before the first lecture, usually scheduled for the last week of March. • Necessary knowledge and skills vary depending on the nature of each project. • Students are requested to self-study (individually or in teams, outside of classroom hours) any knowledge or skills as required to complete the project. • Participation in all lectures and meetings is mandatory. Being absent, late, or leaving early without the lecturers' prior approval is subject to a penalty in the final assessment. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
https://www.u-aizu.ac.jp/enpit/record/2025_software.html https://www.u-aizu.ac.jp/enpit/record/2024_software.html https://www.u-aizu.ac.jp/enpit/record/2023_software.html https://www.u-aizu.ac.jp/enpit/record/2022_software.html https://www.u-aizu.ac.jp/enpit/record/2021_software.html https://www.u-aizu.ac.jp/enpit/record/2020_software.html https://www.u-aizu.ac.jp/enpit/record/2019_software.html https://www.u-aizu.ac.jp/enpit/record/2018_software.html https://www.u-aizu.ac.jp/enpit/record/2017_software.html The course instructor has work experience: Company employees working on development projects will review, evaluate, and provide guidance on students' development activities throughout the course. Currently, active software engineers (with more than 30 years of experience) and faculty members with prior software development experience will jointly deliver lectures and exercises. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2026年度／Academic Year 4学期 ／Fourth Quarter |
|---|---|
| 対象学年 ／Course for; |
3rd year |
| 単位数 ／Credits |
3.0 |
| 責任者 ／Coordinator |
MOZGOVOY Maxim |
| 担当教員名 ／Instructor |
MOZGOVOY Maxim |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
Courses preferred to be learned prior to this course (This course assumes understanding of entire or partial content of the following courses) PL03 JAVA Programming I |
| 更新日／Last updated on | 2026/01/08 |
|---|---|
| 授業の概要 ／Course outline |
Concurrent programs take advantage of modern multicore and multiprocessor machines to implement algorithms that run concurrently (in parallel) to achieve higher performance and better user experience. Distributed computing brings this idea to the next level, dealing with the systems made up of independent computers, linked by a network. At the present time, both concurrent and distributed systems are widespread, due to high popularity of multicore machines and computer networks. However, the design and implementation of such systems and corresponding software remains a challenging task. We have to know how to coordinate independent processes to achieve high performance and avoid common pitfalls. The goal of this course is to introduce the basics of concurrent and distributed systems design and implementation. We will cover a number of classical and modern approaches to this problem, paying special attention to the practical aspects of implementation using Java language. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
[Corresponding Learning Outcomes] (A)Graduates are aware of their professional and ethical responsibilities as an engineer, and are able to analyze societal requirements, and set, solve, and evaluate technical problems using information science technologies in society. At the end of the course the student should be able to: - Understand the key advantages of concurrent and distributed systems and common problems the developers may encounter. - Know different approaches to concurrent and distributed systems design, their advantages and disadvantages. - Be able to implement simple concurrent and distributed systems using modern tools. - Be aware of the historical perspective of the developments in this area, understand modern trends and technologies. |
| 授業スケジュール ／Class schedule |
This course follows flipped learning format. Face to face lecture sessions are actually dedicated to the discussion of exercises and homeworks to make sure the students understand solutions even if they didn't manage to solve exercises themselves. Conversely, homework time is reserved for watching lectures in video format. There are 12 lectures covering the following topics: 1. Introduction 2. Basics of Concurrency 3. Synchronizing Processes 4. Introduction to Model Checking and Promela Language 5. Model Checking with SPIN and Linear Temporal Logic 6. From Shared-memory Model to Message Passing 7. Distributed Programming with MPI & Tuple Space Model 8. OpenMP Technology 9. Client-Server Programming 10. RMI: Distributed Objects in Java 11. Modern Concurrent Programming in Java 12. Actor Model of Concurrent Computations The learning process is organized as follows: - the students watch video course material covering a certain topic and solve exercises; - we discuss unclear points of the topic material and exercise solutions during the next face-to-face meeting. This approach presumes certain level of autonomy and self-learning abilities of the students, but it gives more options to discuss course material after the exercises, when the students already gained certain familiarity and experience with the topic. In addition to the regular lecture/exercise classes, a student is expected to devote around 2-3 hours per topic to achieve a minimum passing grade, and around 4-5 hours for a very high score if they wish to solve additional exercises. |
| 教科書 ／Textbook(s) |
- Distributed Systems: Principles and Paradigms by Andrew S. Tanenbaum and Maarten van Steen, Prentice Hall, 2007. - M. Ben-Ari. Principles of Concurrent and Distributed Programming, 2nd Ed. Addison-Wesley, 2006. - G. R. Andrews. Foundations of Multithreaded, Parallel, and Distributed Programming. Addision-Wesley, 2000. - M. L. Liu, Distributed Computing: Principles and Applications, Addison-Wesley, 2004. |
| 成績評価の方法・基準 ／Grading method/criteria |
The final grade is based on the following parts: - Exercises (50% of the final score). - Two exams (30% of the final score). - Quizzes on lecture content (20% of the final score). We keep strict deadlines in this course. |
| 履修上の留意点 ／Note for course registration |
The presented course is not an introductory subject. It is intended for students who already have basic experience in programming such as Java Programming, Algorithms and Data Structures, Object-Oriented Programming, Operating Systems. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2026年度／Academic Year 2学期 ／Second Quarter |
|---|---|
| 対象学年 ／Course for; |
3rd year |
| 単位数 ／Credits |
3.0 |
| 責任者 ／Coordinator |
RAGE Uday Kiran |
| 担当教員名 ／Instructor |
RAGE Uday Kiran, YAMADA Ryuhei |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2026/02/10 |
|---|---|
| 授業の概要 ／Course outline |
Python is a widely used programming language for developing large-scale industrial, scientific, and data-driven applications. It is closely integrated with Database Management Systems (DBMS) and data-frame-based processing frameworks, which form the foundation of modern AI and Big Data analytics. This course introduces students to Python programming, object-oriented concepts, database design and querying, and data processing using data frames. Through a combination of lectures and hands-on exercises, students will learn how to design data models, store and retrieve large volumes of data, and perform data analysis tasks relevant to AI and Big Data applications. The course covers: (i) fundamentals of Python programming, (ii) object-oriented programming and documentation in Python, (iii) database concepts including ER modeling and DBMS architecture, (iv) Structured Query Language (SQL), (v) object–database connectivity, (vi) data frames and data processing, and (vii) introductory knowledge discovery and pattern mining. Each class consists of one lecture period followed by two exercise periods, emphasizing learning by doing. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
Objectives This course aims to provide students with practical and conceptual foundations in data science using Python. Students will understand how programming, databases, and data-frame-based processing work together in real-world data-intensive applications. Attainment Goals By the end of the course, students will be able to: Develop Python programs using procedural and object-oriented paradigms Design conceptual and logical data models using ER diagrams Create and manage relational databases using SQL Connect Python applications with databases for data access Store, manipulate, and analyze data using data frames Apply basic data processing and pattern discovery techniques to real datasets |
| 授業スケジュール ／Class schedule |
Course Content and Methods Each session consists of a lecture (theory and examples) followed by hands-on exercises. Exercises are conducted individually or in small groups and include programming tasks, database design, SQL execution, and data analysis using real datasets. Discussion and feedback are incorporated regularly to reinforce understanding. Schedule (14 Sessions) Session 1 Lecture: Introduction to Python and data-driven applications Exercise: Input/output operations in Python Session 2 Lecture: Python syntax, variables, control structures Exercise: Solving the Tower of Hanoi problem in Python Session 3 Lecture: Object-Oriented Programming in Python Exercise: File handling, dictionaries, and searching Session 4 Lecture: Python packages and documentation Exercise: Web crawling and scraping application Session 5 Lecture: Introduction to Database Management Systems Exercise: ER modeling for air-pollution data Session 6 Lecture: Entity–Relationship model and schema design Exercise: Database schema implementation Session 7 Lecture: SQL-1 (DDL: CREATE, ALTER, DROP) Exercise: Creating tables and schemas Session 8 Lecture: SQL-2 (DML: INSERT, UPDATE, DELETE) Exercise: Inserting air-pollution data Session 9 Lecture: SQL-3 (SELECT queries) Exercise: Querying and filtering data Session 10 Lecture: Keys and indexes Exercise: Index creation and performance discussion Session 11 Lecture: Object Database Connectivity (ODBC) Exercise: Connecting Python programs to databases Session 12 Lecture: Introduction to data frames Exercise: Generating data frames from SQL results Session 13 Lecture: Managing and processing data frames Exercise: Processing air-pollution data in data frames Session 14 Lecture: Knowledge discovery and pattern mining Exercise: Pattern mining on data frames and discussion Pre-class and Post-class Learning Before each class, students are expected to review lecture materials and relevant textbook sections. After class, students should complete assigned programming or database exercises and review key concepts covered in the session. Typical out-of-class study time per session: 3–5 hours, including preparation, review, and exercise completion. |
| 教科書 ／Textbook(s) |
Database Systems: The Complete Book, Hector Garcia-Molina, Jeffrey Ullman, Jennifer Widom Stanford University Python course materials: http://web.stanford.edu/class/archive/cs/cs106a/cs106a.1212/lectures/4-Variables/4-IntroPython.pdf |
| 成績評価の方法・基準 ／Grading method/criteria |
Student performance will be evaluated based on: Quizzes: 30% Exercises and assignments: 40% Final examination: 30% Exercises do not carry negative marks. Quizzes and the final examination may include negative marking where applicable. Attendance is not included in the grading criteria. Active participation during exercises and discussions may be reflected within the exercise evaluation. Grades are determined on a percentile basis. |
| 履修上の留意点 ／Note for course registration |
Students are strongly advised to have prior knowledge equivalent to: Operating Systems Structured Programming Languages Assignments must be submitted by the specified deadlines. Late submissions may result in score deductions. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
Stanford University course resources: https://web.stanford.edu/class/cs245/ Office hours will be announced during the first week of the course. |
| Open Competency Codes Table Back |
| 開講学期 ／Semester |
2026年度／Academic Year 3学期 ／Third Quarter |
|---|---|
| 対象学年 ／Course for; |
3rd year |
| 単位数 ／Credits |
3.0 |
| 責任者 ／Coordinator |
RAGE Uday Kiran |
| 担当教員名 ／Instructor |
RAGE Uday Kiran, DANG Nam Khanh |
| 推奨トラック ／Recommended track |
－ |
| 先修科目 ／Essential courses |
－ |
| 更新日／Last updated on | 2026/02/10 |
|---|---|
| 授業の概要 ／Course outline |
Course Outline This course introduces students to the principles and practices of Big Data Analytics, with a strong emphasis on data science and data mining techniques used to extract meaningful knowledge from large-scale datasets. The course addresses the challenge of transforming massive volumes of raw data into actionable insights for decision-making and socio-economic development. The course integrates three major aspects of big data analytics: (i) data storage and preprocessing, (ii) data mining and machine learning, and (iii) predictive and time-series analytics. Students will learn how data is collected, stored, cleaned, analyzed, and transformed into knowledge using real-world datasets. Core analytical techniques such as pattern mining, clustering, classification, prediction, and forecasting are covered through hands-on exercises. A “Learn by Doing” approach is adopted throughout the course. Each topic is introduced through conceptual explanations and demonstrations, followed by extensive practical exercises that allow students to implement and experiment with analytics techniques on real datasets. Each class consists of three periods: the first period focuses on explanation and demonstration by the instructor, while the remaining two periods are dedicated to student exercises and guided practice. |
| 授業の目的と到達目標 ／Objectives and attainment goals |
Objectives In modern data-driven environments, organizations collect massive volumes of data, yet valuable knowledge often remains hidden due to the limitations of traditional analysis methods. This phenomenon is commonly referred to as the “Data-Rich but Information-Poor” problem. Big Data Analytics and Data Mining provide systematic approaches to overcome this challenge. This course aims to equip students with both theoretical understanding and practical skills necessary to analyze large datasets and extract useful patterns, models, and predictions. Attainment Goals By the end of the course, students will be able to: Design and manage databases for large-scale data storage Construct analytical data repositories for big data processing Apply data preprocessing and missing-value imputation techniques Perform pattern mining, clustering, and classification tasks Apply regression and time-series forecasting techniques Build ensemble models to improve analytical accuracy Interpret analytical results to support data-driven decision-making |
| 授業スケジュール ／Class schedule |
Course Content and Methods Each session combines conceptual instruction, live demonstrations, and hands-on exercises. Exercises are conducted individually or in small groups using real-world datasets, such as air-pollution and sensor data. Discussion and feedback sessions are incorporated to reinforce analytical thinking and interpretation skills. Session 1 Lecture: Introduction to Big Data Analytics and data science workflow Exercise: Understanding datasets and basic data exploration Session 2 Lecture: Data storage concepts and relational databases Exercise: ER modeling and database schema design Session 3 Lecture: Data warehousing and analytical data organization Exercise: Constructing a data warehouse from transactional data Session 4 Lecture: Data preprocessing and data quality issues Exercise: Handling missing values and data normalization Session 5 Lecture: Knowledge discovery process Exercise: Exploratory analysis of air-pollution data Session 6 Lecture: Pattern mining fundamentals Exercise: Mining frequent patterns from transactional data Session 7 Lecture: Advanced pattern mining and fuzzy concepts Exercise: Fuzzy frequent pattern extraction Session 8 Lecture: Clustering concepts and k-Means algorithm Exercise: k-Means clustering on pollution data Session 9 Lecture: Density-based clustering and DBSCAN Exercise: DBSCAN clustering and comparison with k-Means Session 10 Lecture: Classification concepts and evaluation metrics Exercise: Naïve Bayes classification Session 11 Lecture: Decision tree classification Exercise: Building and evaluating decision tree models Session 12 Lecture: Prediction and regression techniques Exercise: Linear regression and autoregression Session 13 Lecture: Time-series analytics and forecasting Exercise: ARIMA-based forecasting Session 14 Lecture: Ensemble models and integrated analytics Exercise: Ensemble modeling and case study discussion Pre-class and Post-class Learning Students are expected to review lecture materials before each session and complete unfinished exercises after class. Typical out-of-class study time per session: 3–5 hours, including preparation, review, and assignment work. |
| 教科書 ／Textbook(s) |
Core / Data Mining & Big Data Mining of Massive Datasets — Anand Rajaraman, Jeff Ullman, Jure Leskovec Used in many big data analytics courses; excellent for large-scale data mining and pattern discovery. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data — EMC Education Services Comprehensive overview of practical analytics workflows used in industry. Big Data Analytics: From Strategic Planning to Enterprise Integration — David Loshin Focus on strategic, architectural, and integration aspects of big data systems. |
| 成績評価の方法・基準 ／Grading method/criteria |
Student performance is evaluated based on: Quizzes: 30% Exercises and assignments: 40% Final examination: 30% Exercises do not carry negative marks. Quizzes and the final examination may include negative marking where applicable. Attendance is not included in grading criteria. However, students must have 75% of attendence. |
| 履修上の留意点 ／Note for course registration |
Attendance is mandatory. Students must maintain at least 75% attendance to be eligible for course completion. Failure to meet the attendance requirement will result in failure of the course, regardless of academic performance. Prior knowledge of Operating Systems, Database Systems, and a Programming Language is strongly recommended. |
| 参考(授業ホームページ、図書など) ／Reference (course website, literature, etc.) |
Course website: https://u-aizu.ac.jp/~udayrage/intro2BigData.html Stanford University (CS246): https://web.stanford.edu/class/cs246/ The University of Tokyo OCW: https://ocwx.ocw.u-tokyo.ac.jp/course_11414/ |