Curriculum

Annual Schedule

e-Learning courses will be offered sequentially from April with supplementary lessons for follow up and special lectures for leveling up the skills of students (not compulsory). Joint research and employment training (internship) are carried out after consultation with the host institution, as the contents of these programs differs for each student.
Annual Schedule

 

Completion Requirements

For completion requirements, please check the courses and items required for each position. Those who are interested in receiving training incentives are required to take at least one course, in addition to the “Data Entrepreneur Practical Theory” and “Advanced Data Scientist”, in the Management Engineering System. Please note that the number of designated classes may change depending on the academic year.

 

Completion Requirements

 

Attached Table: Example of Designated Classes (The University of Electro-Communications)

Mathematical and Statistical GroupData Engineering GroupArtificial Intelligence GroupManagement Engineering Group
Fundamentals of AnalysisAdvanced Topics on Discrete Information StructureAdvanced Study for Theoretical Computer ScienceManagement Planning
Advanced Topics in AnalysisFoundation of Discrete OptimizationFundamentals of Algorithm Theory Management Principles and Practices
Fundamentals of Applied AnalysisFoundation of Continuous OptimizationApplied AlgorithmsAdvanced Management Systems Engineering
Mathematical AnalysisTopics on Theory of ComputationAdvanced Topics on Algorithmic EngineeringManagement Information Systems
Fundamentals of Information TheoryFundamentals of Computer ArchitectureAdvanced Topics in Machine LearningProject-based Learning of Practical Information System
Advanced Information TheoryAdvanced Computer ScienceAdvanced Intelligent Information SystemsAdvanced Intellectual Property
Advanced Theory on Information Data AnalysisParallel Processing IGame theoryAdvanced Engineering Development
Fundamentals of Mathematical StatisticsParallel Processing II Advanced Theory of Software SciencesAdvanced Lectures on Venture Business
Data MiningAdvanced Theory of Software SciencesAdvanced Topics on Intelligent Soft Computing SystemsAdvanced Service Science
Advanced Theory on Bayesian Artificial IntelligenceAdvanced Progamming Languages: Concepts and ImplementationArtificial Intelligence and Complex NetworkSoftware Quality
Advanced Statistical Machine LearningAdvanced Topics on System SoftwareAdvanced Image Recognition SystemsIntroduction to Information Technologies
Advanced Topics in Data Analysis OptimizationFundamentals of Practical Software DevelopmentAdvanced Cognitive SciencesAdvanced Financial Engineering
Fundamental of GeometryPractical Software Development IILearning InformaticsFundamentals of Accounting Information Systems
Advanced Topics of GeometryPractical Software Development IIITopics on Cognitive processingAdvanced Theory of Systems Reliability
Advanced Topics on Information GeometryPrinciples of Data Engineering 1Advanced Topics in Language and Cognitive SystemsRisk management
Fundamentals of AlgebraPrinciples of Data Engineering 2Advanced Topics on Intelligent RoboticsAdvanced Manufacturing Systems Engineering
Advanced Topics in AlgebraAdvanced System Design 2Advanced topics in perceptual systemAdvanced Intelligent Production System
Modern Algebra Advanced System Design 1Advanced Data and Knowledge EngineeringData Entrepreneur Practical Theory
Advanced Data Scientist

 

Basics learning

e-Learning

Probability Theory and Statistics

Probability Theory and Statistics are essential to understanding the methods used to analyze big data for machine learning, AI, and data science. In this course, students learn four sessions of Probability Theory and six sessions of Statistics. In the first half of the course, the 1st session reviews the basic concepts of Probability Theory, followed by the next three sessions for the basic knowledge of expected values, major probability distributions, and stochastic processes, which are essential for understanding methods. In the latter half of the course, we explain hypothesis testing, one session with a one-sample test and a two-sample test, and one session with a multi-sample test. Next, as the main estimation methods, we explain point estimation and interval estimation based on the maximum likelihood method in one session, and Bayesian estimation in one session. As the origin of many data analysis methods, from the multivariate analysis methods, we introduce regression analysis in one session, principal component analysis and factor analysis in one session, and discriminant analysis and cluster analysis in one session.

  • 10 sessions in total
  • 1. Random variables, probability distribution, probability function, cumulative distribution function, probability density function, simultaneous probability, marginal probability, conditional probability, and independence and dependence
  • 2. Expected values, means, variance, conditional expected values, and conditional variance
  • 3. Probability distribution (normal, exponential, binomial, Poisson, and hypergeometric) (t, chi-square, F, Γ)
  • 4. Stochastic processes
  • 5. Hypothesis testing #1 (simple hypothesis, one-sample, and two-sample)
  • 6. Hypothesis testing #2 (multiple testing)
  • 7. Estimation #1 (maximum likelihood method, point estimation, and confidence interval)
  • 8. Estimation #2 (Bayesian statistics and posterior distribution derivation based on MCMC or conjugate prior distribution)
  • 9. Regression analysis (correlation, single regression, and multiple regression)
  • 10. Multivariate analysis (principal component, factor, discriminant analysis, and cluster)

椿 美智子 - Michiko Tsubaki

Lecturer

TSUBAKI Michiko


A subject in collaboration with the research group of big data analysis and probability and statistics

  • Professor Michiko Tsubaki
  • Associate professor Shuichi Kawano
  • Associate professor Lu Jin
  • Lecturer Watalu Yamamoto

 

Advanced Computer Science

The course covers computer science basics, which is essential for understanding the various data science methods. After an initial introduction to computer science, students will learn about algorithms’ concepts to give an overview of the calculation algorithms for the greatest common factor, factorization, and power. After studying congruence formulas, finite fields, and Fermat’s little theorem, we explain their applications to RSA public-key cryptography and digital signatures. We discuss small-world phenomena in graph theory after lecturing on the breadth-first search, paths and connected components, and rooted trees. Finally, we lecture on the basics of computational complexity theory. Specifically, the concepts of computational time measurement, polynomial time algorithms, and explosive increases in computational time are studied, We introduce the P versus NP problem, bitcoin, and quantum computer topics after lecturing on the concepts of how to measure computation time, polynomial-time algorithms, and an explosive increase in computation time.

  • 10 sessions in total
  • 1. Algorithm #1: What is computer science? What is an algorithm? Calculation of the greatest common factor and Euclid’s algorithm
  • 2. Algorithm #2: Factoring algorithm, exponentiation calculation, power calculation time, and factoring difficulty
  • 3. Theory of integers #1: Congruence formulas, algebras of congruence formulas, and fundamental problems of algebra
  • 4. Theory of integers #2: Euclid’s algorithm, theorems about greatest common factors, finite fields, and Fermat’s little theorem
  • 5. Public-key cryptography: Public-key cryptography, RSA public-key cryptography, and electronic signatures
  • 6. Graph theory #1: Graph definition, paths and connectivity, and rooted trees
  • 7. Graph theory #2: Graph breadth-first search, connected components, and complex network analysis
  • 8. Computational complexity theory #1: How to measure computation time, polynomial-time algorithms, and an explosive increase in computation time
  • 9. Computational complexity theory #2: Clique problem, clique problem complexity, and P versus NP problem
  • 10. Computational complexity theory #3: NP-complete problems, bitcoin, and quantum computers

西野 哲朗 - Tetsuro Nishino

Lecturer

NISHINO Tetsuro

 

Advanced Programming Languages

This course lectures on the basics of the Python programming language required for data science. Students acquire the language through practice. After learning the Python programming language’s grammar, we move on to the control scripts and object-oriented concepts and notation. After the modules for data analysis, we study visualization and conduct a general exercise. Learning the Python programming language framework leads to practices in the Advanced Data Scientists in the second semester.

  • 10 sessions in total
  • 1. Building a Python experimental environment
  • 2. Try Python: Data structures of interpreter shells and arithmetic processing; variables, strings, and lists;
  • 3. Python as a script #1: From the interpreter to the script, how to write and indent control
  • 4. Python as a script #2: How to write and use functions and the scope of variables
  • 5. Python as an object-oriented language: How to write and use classes and generate instances
  • 6. Module overview and import #1: Explanation of standard modules, interfaces of the OS, string pattern matching, math, Internet access, date and time, data compression, performance measurement, and quality control
  • 7. Module overview and import #2: Introduction to the outline of NumPy, SciPy, Matplotlib, and pandas modules used for scientific and engineering calculations
  • 8. NumPy overview and visualization with Matplotlib
  • 9. SciPy overview and data analysis
  • 10. Examination

庄野 逸 - Hayaru Shouno

Lecturer

SHOUNO Hayaru

 

Python Engineer Development Association
Courses offered by the Python Engineer Development Association

  • Manabu Terada, Advisory Director of the Association, examination question supervision and community support

 

Face-to-face Learning

Data Entrepreneur Practical Theory

In this lecture, students will learn how to deploy the concepts of data science to society. We will invite people who have been involved in the social implementation of data science to share their experiences (some lecturers may require a report). At the end of the lecture, there will be a pitch competition in which participants will present how they want to integrate data science to solve a problem, whether it is a new business or a social issue.

  • Academic Year 2020:15 sessions in total
  • 1. Orientation SELF-INTRODUCTION – TAMURA Motonori, The University of Electro-Communications
  • 2. Data science: research and hands-on practice – OKAMOTO Kazushi, The University of Electro-Communications
  • 3. Business Skills for Data Scientists – SAITO Shiro, The University of Electro-Communications
  • 4. Examples of Big-data Application – MORIYA Toshio, Hitachi, Ltd.
  • 5. Analytics to increase customer value – NISHIMAKI Yoichiro, IBM Japan, Ltd.
  • 6. Business decision-making through data analysis – WADA Yoichiro, Data4C’s K.K.
  • 7-9. Experience Japanese Watson API: Design thinking with artificial intelligence – NISHINO Tetsuro, The University of Electro-Communications
  • 10. A start-up case study of a location data analysis business  ISHIKAWA Yutaka, Nightley Inc.
  • 11. Leveraging Data for Change  – MARUYAMA Fumihiro, Fujitsu Laboratories Ltd.
  • 12. Business Model Creation in the IoT Era – SHIMADA Keiichiro, SONY Corporation
  • 13-15.Value Creation from Data (pitch competition) – TAMURA Motonori, The University of Electro-Communications

田村 元紀 - Motonori Tamura

Lecturer

TAMURA Motonori




 

Advanced Data Scientist

In this lecture, the sponsor company will provide the actual data, and a representative from that company will talk about the challenges of their business. Participants in the lecture form groups (the lecturer will determine the composition of members) and think critically about how to solve the problems they hear about based on the data, analyze the data necessary for this purpose, and finally create models and other tools to come up with solutions. Every year, we receive support from various companies, but in the past, we have used purchasing data from ASKUL Corporation and game data from DeNA Co., Ltd. to give lectures.

Past Curriculum
・Academic Year 2019: Mobile Game Data of DeNA Co., Ltd.
・Academic Year 2018: Real Estate Price Data (Open Data)
・Academic Year 2017: Sales Data of ASKUL Corporation
・Academic Year 2016: POS data of Zen-Nippon Shokuhin Co., Ltd.
・Academic Year 2015: 9 sessions


田村 元紀 - Motonori Tamura

Lecturer

TAMURA Motonori

 

斉藤 史朗 - Shiro Saito

Lecturer

SAITO Shiro

 

Practical Learning

Joint Research

This program is intended for graduate students to experience practical problem-solving methods and effects by getting involved in joint research between their laboratories and companies.
Depending on the content and scale of the assignment, the implementation format will vary and will be discussed on an individual basis. In practice, specific assignments are set through guidance and consultation meetings scheduled at the beginning and in the middle of the academic year.
The University of Electro-Communications is home to a large number of faculty and researchers with outstanding achievements in the fields of advanced science and technology, focusing on electronics, information and communications, computers, mechatronics, intelligent robotics, biotechnology, laser and optical technologies, new materials, etc. More than 300 professors from science and engineering to humanities participate in joint research in their respective fields of expertise.

The program will focus on setting up assignments in information and communication, particularly in AI, IoT, big data, and cybersecurity.
Reference: http://www.crc.uec.ac.jp/institution/


田村 元紀 - Motonori Tamura

Lecturer

TAMURA Motonori




 

Internship (Employment Training)

This program is designed for graduate students to experience practical problem-solving work by participating in an internship at a company. The University of Electro-Communications’ Center for Industry-Academia-Government Collaboration, the Internship Promotion Office of the Career Education Subcommittee, Division of General Education, and URA staff will assist the students in deciding which companies to accept and what internship program to pursue. The target year, internship period, and research/technical development topics will be determined flexibly in response to individual consultation. Students who wish to participate in the program will be required to submit a request form. Insurance coverage, company confidentiality, and travel expenses will be covered in the same manner as the conventional internship program.
Reference: http://www.uec.ac.jp/career/career/procedure.html

The internship granted for two credits by the Career Education Subcommittee’s Internship Promotion Office is, in principle, a summer internship of at least 90 hours for first-year master’s students. The report and performance evaluation may be used to apply for completion requirements for this program.
Reference: http://www.uec.ac.jp/career/career/internship.html

田村 元紀 - Motonori Tamura

Lecturer

TAMURA Motonori