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".
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.
Data-driven society, big data, data structure, database, Python, representative values, correlation, variance, probability distribution, normal distribution, random number, linear regression, least-squares method
|✔ Specialist skills||✔ Intercultural skills||Communication skills||✔ Critical thinking skills||✔ Practical and/or problem-solving skills|
To check students’ understanding, students are assigned exercises at every class.
|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.|
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.
Lecture materials will be found on T2SCHOLA in advance and shared in Zoom lecture.
Grading is based on exercises and term-end report.
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.
KOBAYASHI, Takao (lecture_ba_2023[at]dsai.titech.ac.jp)
Students can ask questions on T2SCHOLA forum or by e-mail.