Course Objectives
To learn mathematics & data science, AI, information literacy, security, etc., and to acquire basic knowledge that can be used in daily life and work in the future information society.
To be able to make appropriate human-centered judgments and to explain and utilize the knowledge and skills acquired by learning social situations and actual examples in society.
To acquire the ability to think from multiple perspectives through cooperative learning with students who have different specialties from one's own.
(1) Handling of data
(2) Data analysis
(3) Relationship between corporate activities and mathematics & data science/AI
Rubric
| Ideal Level | Standard Level | Unacceptable Level |
| Achievement 1
(Handling of data) | Appropriately handles data and explains the details of its usage | Ability to handle data | Unable to handle data |
| Achievement 2
(Data alalysis)
| Appropriately analyzes real data and correctly explains the results | Ability to analyze real data an to explain the results | Unable to analyze real data an to explain the results |
| Achievement 3
(Relationship between corporate activities and mathematics & data science/AI) | Fully investigates the company in charge, composes a report based on appropriate interviews, and considers the relationship between corporate activities and mathematic&s data science/AI from multiple perspectives | Ability to investigate the company in charge, compose a report based on interviews, and to consider the relationship between corporate activities and mathematic&s data science/AI from multiple perspectives | Unable to investigate the company in charge, compose a report based on interviews, and to consider the relationship between corporate activities and mathematic&s data science/AI from multiple perspectives |
Assigned Department Objectives
Teaching Method
Outline:
To learn through "Data Science I" and "Data Science II" the literacy of information technology, mathematical data science, AI, and security that technical college students should acquire regardless of their field of specialization.
To learn in addition to knowledge the importance of data science in society through actual examples and practice exercises using real data to acquire basic knowledge for discovering and solving problems in the real world and learning how to use data appropriately.
Style:
The class will consist mainly of lectures and exercises using actual data.
In the cooperative education, the team consists of students from all departments as much as possible, and the team investigates and interviews the company in charge, discusses the relationship with data and AI utilization, and composes a report.
Notice:
Presentation, portfolio, and others (reports, etc.) will be evaluated comprehensively. Each evaluation will consist of 20% for presentation, 10% for portfolio, and 70% for others. A grade of 50 points or more is required to receive credit.
A student whose grade is less than 50 points may take an additional examination upon request. If the student is approved for credit as a result of the additional examination, the grade will be 50.
The class plan is subject to change according to the level of understanding of the students.
Characteristics of Class / Division in Learning
Course Plan
|
|
|
Theme |
Goals |
| 2nd Semester |
| 3rd Quarter |
| 1st |
Utilization of Microsoft Teams & cooperative Education (1) |
To understand how to use Microsoft Teams
To understand how to proceed with corporate investigation activities and points to keep in mind
|
| 2nd |
Utilization of Microsoft Teams & cooperative Education (2) |
To conduct a company survey, to utilize Microsoft Teams, and to hold a meeting
|
| 3rd |
Utilization of Microsoft Teams & cooperative Education (3) |
To conduct interviews with companies and compose a report on the results and the relationship with data and AI applications
|
| 4th |
Data science (1) |
To acquire data appropriately and understand how to handle it and what to keep in mind
|
| 5th |
Data science (2) |
To understand the types of data and to be able to create appropriate graphs
|
| 6th |
Data science (3) |
To understand frequency distributions and histograms through exercises on actual data
|
| 7th |
Data science (4) |
To understand how to sort data through exercises on real data
|
| 8th |
Data science (5) |
To understand representative values (mean, median, and mode) of data through exercises on real data
|
| 4th Quarter |
| 9th |
Data science (6) |
Through exercises on real data, to understand the variability (variance, standard deviation) of data
|
| 10th |
Data science (7) |
To understand box-and-whisker plots and scatter plots through exercises on actual data
|
| 11th |
Data science (8) |
Through exercises on real data, to understand correlation and correlation coefficient
|
| 12th |
Data science (9) |
Through exercises on real data, to understand the method of least squares
|
| 13th |
Data science (10) |
Through exercises on real data, to understand the regression line
|
| 14th |
Data science (11) |
Through exercises on real data, to understand the coefficient of determination
|
| 15th |
Data science (12) |
Through exercises on real data, to understand the analysis of data and causal relationships
|
| 16th |
Class evaluation questionnaires |
|
Evaluation Method and Weight (%)
| Examination | Presentation | Mutual Evaluations between students | Behavior | Portfolio | Other | Total |
| Subtotal | 0 | 20 | 0 | 0 | 10 | 70 | 100 |
| Basic Proficiency | 0 | 10 | 0 | 0 | 0 | 40 | 50 |
| Specialized Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Cross Area Proficiency | 0 | 10 | 0 | 0 | 10 | 30 | 50 |