Curriculum

Data Science Literacy Program Curriculum

The University of Tsukuba is a large-scale institution that offers education in a wide range of disciplines such as the humanities, life sciences, science and engineering, information science, medicine, physical education, and arts.
Each educational institution offers subjects for studying the basics of data science, namely probability and statistics. Because data science skills are necessary regardless of one’s academic discipline, and because of the ever-increasing need for objective judgments and decision-making based on data in government and industrial domains after students graduate, Data Science (two credits) is a compulsory subject for first-year students in all departments at the university.

The Data Science Literacy Program consists of two subjects totaling three credits, including Data Science and Information Literacy (lecture) (one credit, compulsory for first-year students), which covers the fundamentals of computer science required to complete Data Science.

Students at the University of Tsukuba study a wide variety of majors in various educational organizations, and the background knowledge and skills of the students differ.
In consideration of this, the Data Science class is designed with the following points in mind.

  1. By explaining the importance and necessity of data science in relation to the student’s background knowledge, they are more motivated to learn about data science.
  2. Depending on the department, statistical education as a form of mathematics, which could be called the core of data science, is kept to a minimum on the premise that students will complete it from the second year or later in their major, however, incorporating constant exercises using computers and giving students opportunities to encounter data and processed data results in an ongoing fashion helps grant an understanding of the effects data science produces.
  3. By using data from various fields and data collected by University of Tsukuba students as course materials, they view the data analysis results as their own, rousing their interest in analysis results obtained in data science endeavors.
  4. Instead of simply learning about statistical analysis, which is considered a central skill in data science, students undergo education in the fundamentals of handling data, such as data science techniques including data collection and management, as well as a sense of ethics required for handling human-related data, including human rights, legal compliance, and protection of privacy in data science.

Data Science Education Details


For detailed information on the content of the Data Science program, see the list of open subjects (Japanese only)

During the first week, students learn about the positioning and significance of data science in society.
During this period, they view video lectures with exercises provided by researchers involved in data science in a variety of disciplines at the University of Tsukuba, such as the humanities, life sciences, science and engineering, information science, medicine, physical education, and arts.

Furthermore, the courses cover legal fundamentals that should be known when handling data related to humans, case studies of human rights and privacy infringements in data usage, as well as the thinking and procedures behind the ethics required for research handling human-related data.

In the second and third weeks, students learn about the overall data science image and life cycle (data collection, management, and analysis), and data types as an introduction, data collection, preprocessing, and reuse.

Studies in the fourth and fifth weeks cover the significance and purpose of data management, designing data collection items, the separation of information structures and expressions, and the fundamentals of data engineering.
Advanced content such as high-end data management including IoT and CPS, as well as advanced usage of big data is covered in video lectures with exercises by faculty that specialize in data engineering at the university.

In the sixth week, students learn the significance and purpose behind data visualization, as well as its methods and how to choose visualization expressions.

In the seventh to ninth weeks, they learn data analysis methods.
Specifically, this includes understanding discrete variables and discrete variable statistics, understanding and statistics of quantitative variables, cause and correlation, and analysis of complex data sets (time-series data, network data).
Depending on the discipline, discrete variable statistical testing (chi-squared test), quantitative variable statistical testing (z-test, t-test), and linear regression are also used as necessary.

In the tenth week, students study machine learning and artificial intelligence as advanced data analysis techniques through video lectures with exercises by faculty that specialize in artificial intelligence at the university.

Data Science Implementation Methods

Embodiment

There are a total of 50 classes offered in Japanese and 1 in English.
Data Science consists of two 75-minute slots over 10 weeks for 2 credits, and are held in a computer room where each student may use a computer.
Classes are 150 minutes long with 60 minutes dedicated to lectures, 15 minutes to quizzes using the class management system, and 75-minute exercises using computers.
Exercises are also given after class as assignments.
For 75 minutes of each 150-minute class, graduate students are assigned as teaching assistants, helping students with their exercises.

Course materials

Keeping in mind that this is a compulsory subject for students in a variety of academic disciplines, the standard course materials (slides), exercises, and quizzes are created with all bachelor programs in mind, not only science and engineering tracks, but also in literature, physical education, and the arts.
Various exercises are available from entry level to advanced assignments, allowing for flexible class content that matches the mathematical skills of the students.
Faculty specializing in the application of data science to individual disciplines, advanced data management, and data analysis cover such subjects in video lectures and exercises.
To support the English program at the University of Tsukuba, standard course materials and videos are also created in English.
The video lectures are available to the general public as OpenCourseWare.
University of Tsukuba OpenCourseWare Data Science Lectures

The course materials are provided to the Consortium for Strengthening Mathematics and Data Science Education with plans to promote mathematics and data science education.

© 2023 University of Tsukuba Data Science Literacy Program.