Practical Fundamentals of Data Science

In this course, students shall build on and develop the contents of the first year's compulsory course "Data Science Literacy," and systematically learn practical knowledge and techniques for using mathematics, data science, and AI in their fields of expertise as a bridge between literacy and specialization.
They shall learn "Programming Basics," "Mathematical Basics (Statistical Mathematics, Linear Algebra, and Calculus)," "Algorithms,", which are the foundational elements of data science; and machine learning, which is the core of artificial intelligence (AI) and the rapidly developing generative AI. They shall also use Python, a language that is frequently used in data engineering, in various settings to learn these fundamentals through exercises.
In particular, the course incorporates a lot of real data from open data sources and familiar datasets to help students view challenges and issues as more relatable and immediate.
Based on the model curriculum of the Japan Inter-University Consortium for Mathematics, Data Science, and AI Education, each learning module uses accessible data to teach practical skills, including hands-on coding.

Data Science, Analytical Design, Regression Analysis, Data visualization, Probability/Statistics, Vectors and Matrices, Fundamentals of Calculus, Algorithm Basics, Data Engineering, Data Representation, Programming, Python, Neural Networks, Machine Learning, Deep Learning, Natural Language Processing, Generative AI, Image Recognition

The numbers in parentheses following each session title represent the corresponding number in the model curriculum.

1. Introduction to Data Science (1-1. Data-Driven Society and Data Science, 2-1. Big Data and Data Engineering)
2. Programming Basics (2-7. Programming Basics)
3. Data Representation and Formats (2-2. Data Representation)
4. Data Observation and Visualization (1-3. Data Observation, 1-4. Analytical Design, 1-5. Data Visualization)
5. Problem-solving Methods (1-7. Algorithms)
6. Introduction to Mathematics for Data Science (1-6. Basics of Mathematics)
7. Univariate Statistics (1-2. Analytical Design, 1-4. Data Analysis, 1-6. Basics of Mathematics, 2-5. Data Processing)
8. Multivariate Statistics (1-2. Analytical Design, 1-4. Data Analysis, 1-6. Basics of Mathematics, 2-5. Data Processing)
9. Introduction to AI - Society, History, and Ethics (3-1. History and Application of Fields of AI, 3-2. AI and Society)
10. Basic Concepts of Machine Learning (3-3. Fundamentals and Prospects of Machine Learning)
11. Neural Networks and Deep Learning (3-4. Fundamentals and Prospects of Deep Learning)
12. Construction and Operation of AI (3-10. Construction and Operation of AI)
13. Image Recognition and Deep Learning (3-4. Fundamentals and Prospects of Deep Learning, 3-6. Recognition)
14. Normal Distribution and Diffusion Model (1-6. Basics of Mathematics, 3-5. Fundamentals and Prospects of Generative AI)
15. Natural Language Processing and Large-scale Language Models (3-5. Fundamentals and Prospects of Generative AI, 3-8. Language and knowledge), Class Questionnaire

Syllabus (Japanese site only)