| Course title | |||||
| Arts of Intercultural Communication [Arts of Intercultural Communication] | |||||
| Course category | Requirement | Credit | 1 | ||
| Department | Year | ～ | Semester | YearLong | |
| Course type | YearLong | Course code | WISE4003 | ||
| Instructor(s) | |||||
| 篠原 俊二郎, 西田 浩之 [SHINOHARA Shunjiro, NISHIDA Hiroyuki] | |||||
| Facility affiliation | University Research Administration Center | Office | Email address | ||
| Course description |
|
Classcode: keqa7ek This is an exercise class linked with Outline of Data Science. The Python programming language is used to carry out practical exercises relating to processing, analysis, and visualization of the data which forms the foundation of data science. In addition, students learn and develop an understanding of basic methods of machine learning (e.g., support vector machines, neural networks). |
| Expected Learning |
|
- Learn the fundamentals of python. - Ability to process and analyze data using Python, NumPy, SciPy, and Pandas. - Ability to visualize data using matplotlib. - Ability to practically implement basic machine learning methods using scikit-learn. |
| Course schedule |
|
The course will be offered almost every other Saturday afternoon from October 28 to December 23. (held on alternate weeks with the "Outline of Data Science") Classroom: Koganei L1342, 4th floor, Build.13 Session 1-2: Saturday, October 28 (13:00-14:30, 14:45-16:15) 1st session) Orientation, setup of programming exercise environment 2nd session) Fundamentals of Python (variables, data types, control structures) Session 3-4 Saturday, November 18 (13:00-14:30, 14:45-16:15) 3rd session) Fundamentals of NumPy (arrays, matrix operations) 4th session) Fundamentals of Pandas (DataFrame construction, data visualization) Sessions 5-6 Saturday, December 9 (13:00-14:30, 14:45-16:15) 5th session) Data visualization (matplotlib) 6th session) Supervised learning using scikit-learn (support vector machines) Sessions 7-8 Saturday, December 23 (13:00-14:30, 14:45-16:15) 7th session) Fundamentals of deep learning 8th session) Summary |
| Prerequisites |
|
Must take “Outline of Data Science” course before or in the same semester. Good to have programming experience |
| Required Text(s) and Materials |
| Handed out as appropriate. |
| References |
| Books on Python programming |
| Assessment/Grading |
|
In-class activities (contribution, mini-quiz): 20% Exercise assignments to check level of understanding (40%) Final assignment (40%) |
| Message from instructor(s) |
| It is hoped that students will master the practical techniques of data science, and use those skills in their own research. |
| Course keywords |
| Python, NumPy, SciPy, Pandas, scikit-learn |
| Office hours |
| Questions will be received at any time by email. |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| English |
| Language Subject |
| Last update |
| 8/18/2023 3:38:56 PM |