2023 Basics of Data Science and Artificial Intelligence

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Academic unit or major
Basic science and technology courses
Okumura Keiji  Tomii Norio  Miyazaki Kei  Sakuma Jun  Ono Isao  Miyake Yoshihiro  Nitta Katsumi  Kobayashi Takao 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Courses specified
Day/Period(Room No.)
Wed5-6(南4号館3階 第1演習室, 南4号館3階 第2演習室, W2-401(W241))  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

The purpose of this course is to give the fundamentals of data science and AI to students those who wish to solve various problems utilizing data science and AI approaches. The course is based on a model curriculum for literacy-level study of data science and AI, and also provides advanced topics so that students can easily proceed to study an advanced-level course. 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.

Student learning outcomes

Students will be able to:
1) Acquire independently fundamental knowledge for utilizing data science and AI in their daily lives and tasks.
2) Make human-centered smart judgements, accept a certain benefit without anxiety on their own will, explain and utilize AI, when being in the situation of an AI user.


Internet of Things, Information and Communication Technology, cloud computing, Society 5.0, personal information protection, anonymous processing information, copyright, open data, spread sheet, data cleansing, data summarization, histogram, scatter diagram, CSV (comma separated value) representation,
data input/output, Python, NumPy, matplotlib, scikit-learn, machine learning, unsupervised learning, supervised learning

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 Social utilization of data science and AI Understand the utilization of data science and AI, and learn problems newly caused due to the introduction of AI technique, direction of progress in data science, and data equity and ethics in the AI systems.
Class 2 Data reading and considerations of data handling Understand various kinds of information sources and learn fundamental data utilization by means of a table calculation software. An outline of attribute representation, summarization, and visualization of collected data is also provided.
Class 3 Basics of data processing Understand built-in data structures and the handling of a character string in Python through exercises of actual short programming.
Class 4 Handling tabular data Understand how to input / output text data and CSV data in Python. Learn a special module called NumPy which extends the ability of data representation.
Class 5 Data explaining Learn to handle CSV tables with NumPy module. Understand how to write a short Python program which summarizes, analyzes, and visualizes the given data as an example of utilization of NumPy data structure.
Class 6 Practical utilization of data (unsupervised / supervised learning) Learn the role and availability of unsupervised and supervised machine learning in problem solving through simple Python programming with scikit-learn and NumPy modules.
Class 7 Information, AI, and data ethics Learn about issues such as privacy protection in the collection and use of personal information, fairness in services using AI such as generative AI, balance between data use and copyright protection, and ethics in decisions made by AI such as generative AI, as well as the concept along with incidents that have occurred in the past.

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.


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 quizzes 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.M102 : Linear Algebra I / Recitation
  • LAS.M106 : Linear Algebra II

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None required.

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