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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Kobayashi Takao Nitta Katsumi Miyazaki Kei Tomii Norio Okumura Keiji Sakuma Jun Ono Isao Miyake Yoshihiro
- Class Format
- Lecture (Livestream)
- Media-enhanced courses
- Courses specified
- Day/Period(Room No.)
- Wed7-8(W2-401(W241))
- Group
- -
- Course number
- XCO.T281
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- 2023/3/20
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course gives basic theories, methods, and algorithms of data science, data engineering, and AI to students those who have finished a literacy-level course study of data science and AI and wish to study a higher-level course. The curriculum is designed so that it provides an intermediate-level course study of data science and AI between literacy- and expert-level ones. The course would enable students to understand theories and methods deeply and achieve practical skills in problem solving through a variety of examples and exercises. Students are strongly recommended to successfully complete both the courses "Basics and Applications of Data Science and Artificial Intelligence I and II".
Student learning outcomes
Students will be able to:
1) Understand significance of studying data science, as well as data analysis methods, and choose appropriate data analysis and visualization methods.
2) Understand roles of data engineering, representation methods of various data on a computer, and data acquisition/processing/accumulation techniques.
3) Understand history of AI, its technical background, AI ethics, machine learning and learning algorithms, neural networks and deep learning algorithms, and apply AI technology to problem solving.
Keywords
Data-driven society, big data, data structure, database, Python, representative values, correlation, variance, probability distribution, normal distribution, random number, linear regression, least-squares method
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
To check students’ understanding, students are assigned exercises at every class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction to data science and AI | Learn fundamentals of data science and AI, and also understand their histories and roles. |
| Class 2 | Fundamentals of data engineering | Learn techniques of data acquisition, data processing, and data accumulation, and also understand representations of various data on computer. |
| Class 3 | Python tools for data science and AI, part 1: Libraries | Understand basics of Python programming language and useful libraries such as NumPy, SciPy, and matplotlib. |
| Class 4 | Python tools for data science and AI, part 2 | Learn how to utilize the Python/pandas library, powerful and flexible tool for data analysis and manipulation, with using open data. |
| Class 5 | Fundamentals of mathematics for data science and AI | Learn basic knowledge of mathematics required for utilizing data science and AI. |
| Class 6 | Fundamentals of data science, part 1 | Learn probability distributions widely used in data analysis, and also learn random numbers. |
| Class 7 | Fundamentals of data science, part 2 | Understand data analysis process and also learn data analysis methods such as linear regression and multiple regression. |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None. Lecture materials will be given in the class.
Reference books, course materials, etc.
Lecture materials will be found on T2SCHOLA in advance and shared in Zoom lecture.
Assessment criteria and methods
Grading is based on exercises and term-end report.
Related courses
- LAS.I111 ： Information Literacy I
- LAS.I112 ： Information Literacy II
- LAS.I121 ： Computer Science I
- LAS.I122 ： Computer Science II
- LAS.I131 ： Basics of Data Science and Artificial Intelligence
- LAS.M101 ： Calculus I / Recitation
- LAS.M102 ： Linear Algebra I / Recitation
- LAS.M105 ： Calculus II
- LAS.M106 ： Linear Algebra II
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students are assumed to have the knowledge given in Calculus I and II, Linear Algebra I and II, and Basics of Data Science and Artificial Intelligence.
Contact information (e-mail and phone) Notice : Please replace from "[at]" to "@"(half-width character).
KOBAYASHI, Takao (lecture_ba_2023[at]dsai.titech.ac.jp)
Office hours
Students can ask questions on T2SCHOLA forum or by e-mail.
