Week 1 Guidance / Course Overview / University E-mail Setup

Week 2 On-campus ICT Services for Learning / Basic File and Folder Management

Week 3 Data Science in Modern Society

Week 4 Internet Basics / Big Data / Artificial Intelligence (AI)

Week 5 Information Ethics

Week 6 Information Security

Week 7 Midterm Examination

Week 8 Fundamentals of Data Analysis and Visualization

Week 9 Basics of Excel and Fundamental Statistics

Week 10 Data Visualization Using Excel

Week 11 Histograms
Excel Exercise Assignment

Week 12 Basics of Python

Week 13 Basic Statistical Analysis Using Python

Week 14 Data Visualization Using Python
Python Exercise Assignment

Week 15 Histograms Using Python
Final Assignment

Masahiko Sasajima (ed.), Business Data Science with Python 1: Introduction to Data Science
Genshiro Kitagawa et al. (eds.), Introduction to Data Science Series: Data Science as Liberal Arts

Additional references will be announced during class if necessary.

Regarding preparation and review, the approximate amount of time is as follows. Details will be explained by the instructor as needed.

Preparation (Pre-study): Review and confirm understanding of all content covered up to the previous class, including re-review as necessary (15 sessions, total of 15 hours)

Review: Review of materials distributed by the instructor, as well as review of in-class exercises and quizzes for each session (15 sessions, total of 30 hours), Completion of assignments (3 assignments, total of 7.5 hours)

Grading Criteria

Unexcused absences without valid reasons such as illness are not permitted. The course is conducted on the assumption that students attend every class session.
Students who correctly understand the fundamental concepts of data science (including statistics, approaches to data analysis, and ethics) and who are able to appropriately process and analyze data using Excel and Python will be evaluated based on the level of achievement of the abilities (knowledge and skills, thinking ability, judgment, and expressive ability, etc.) described in the course objectives and learning outcomes.
Grades will be assigned as follows: S (90 points or above), A (80 points or above), B (70 points or above), and C (60 points or above). Credits will be awarded accordingly.

Methods of Evaluation

Grades will be determined comprehensively based on the following components, including class participation:

• Quizzes: 30% (5 quizzes in total)

• Midterm Test: 30% (conducted in Week 7)

• Assignments: 40%

  • Excel assignments: 15%

  • Python assignments: 15%

  • Final assignment: 10%

Students who are absent four times or more will receive a failing grade.
Absences due to unavoidable reasons such as illness will be considered only if the prescribed procedures are completed.
Note: No final examination will be administered.

Since this course includes hands-on exercises, students who are unable to fully understand the content during class time or who cannot complete assignments within the allotted time must review the material on their own and submit the assignments, for example by asking questions to the instructor. Students with inappropriate attitudes toward the class (such as leaving or entering the classroom without valid reasons, having frequent unexcused absences, or engaging in private conversations during class) may receive warnings. If no improvement is observed after a warning, the student may not be permitted to attend subsequent classes. For detailed information regarding course requirements and rules, students must review the explanations provided during the orientation and the Week 1 class before enrolling in this course.