Course Objectives
Can explain an overview and application examples of information technology, such as IoT, machine learning, and artificial intelligence.
Can explain an overview of computers and networks.
Can explain an overview of information security and examples of cyberattacks and defense.
Can execute data utilization and analysis from big data and IoT, using a data processing language (Python).
Rubric
| Ideal Level | Standard Level | Unacceptable Level |
| Achievement 1 | Can fully explain an overview and application examples of information technology, such as IoT, machine learning, and artificial intelligence | Can explain an overview and application examples of information technology, such as IoT, machine learning, and artificial intelligence | Cannot explain an overview and application examples of information technology, such as IoT, machine learning, and artificial intelligence |
| Achievement 2 | Can fully explain an overview of computers and networks | Can explain an overview of computers and networks | Cannot explain an overview of computers and networks |
| Achievement 3 | Can fully explain an overview of information security and examples of cyberattacks and defense | Can explain an overview of information security and examples of cyberattacks and defense | Cannot explain an overview of information security and examples of cyberattacks and defense |
Assigned Department Objectives
Teaching Method
Outline:
The aim is to develop the knowledge and skills for the appropriate and effective use of information and information technology, to develop the ability to use them practically, and to develop an attitude toward proactively participating in an information society. The course will be held as an early introductory education to foster human resources capable of utilizing, analyzing, and evaluating real data such as "IoT," "big data," and "AI" following their acquisition of knowledge on "mathematics/data science/AI." Students will learn about real-world issues and how to resolve them appropriately through exercises, using real data and issues, and other practical examples in society by utilizing mathematics, data science, and AI. This lecture will be conducted by a faculty member who has been engaged at a company in middleware (database) research and development.
Style:
Students will practice programming, data analytics, and analysis with examples using the Python program. Quizzes will be conducted every lesson to test students' understanding. Students will be evaluated based on quizzes and submitted work which serve as tests.
Notice:
Students who miss 1/3 or more of classes will not be eligible for a passing grade.
Characteristics of Class / Division in Learning
Course Plan
|
|
|
Theme |
Goals |
| 2nd Semester |
| 3rd Quarter |
| 1st |
Introduction to programming (1) |
Learn Python programming syntax
|
| 2nd |
Introduction to programming (2) |
Learn Python programming syntax
|
| 3rd |
Introduction to programming (3) |
Learn Python programming syntax
|
| 4th |
Deep learning |
Learn about implementing deep learning through the use of sample codes
|
| 5th |
Data science for control system |
Learn about overview of deep learning from the point of view of control system, and attention is also given to applied problems in control system
|
| 6th |
Data Visualization |
Can demonstrate data visualization using a web server
|
| 7th |
Statistical analysis (1) |
Can demonstrate a simple regression analysis
|
| 8th |
Statistical analysis (2)・Mutual Evaluations between students |
Can demonstrate simple clustering (k-means)・Mutual Evaluations between students
|
| 4th Quarter |
| 9th |
Computer configuration and programming |
Check a computer's configuration and performance by obtaining system information and creating a simple benchmark with the use of Python
|
| 10th |
Parallel processing |
Learn how to write and execute parallel processing in Python to speed up your program
|
| 11th |
Automated file processing |
Automate file processing in Python and learn how to optimize simple tasks
|
| 12th |
Automated web information retrieval |
Learn about web scraping, a method for automatically retrieving web information in Python
|
| 13th |
Network processing (1) |
Learn how to automate web-related tasks by programming
|
| 14th |
Network processing (2) |
Learn more about handling Internet communication through Python
|
| 15th |
Security and summary of studies |
Reproduce vulnerable websites in Python and learn about the need for security by verifying their behavior
Review the previous exercises and learn how they relate to each other and how they can be combined to build a system
|
| 16th |
Final exam |
None
|
Evaluation Method and Weight (%)
| Examination | Presentation | Mutual Evaluations between students | Behavior | Portfolio | Other | Total |
| Subtotal | 0 | 0 | 0 | 0 | 100 | 0 | 100 |
| Basic Proficiency | 0 | 0 | 0 | 0 | 40 | 0 | 40 |
| Specialized Proficiency | 0 | 0 | 0 | 0 | 40 | 0 | 40 |
| Cross Area Proficiency | 0 | 0 | 0 | 0 | 20 | 0 | 20 |