This course introduces the core concept of data science and its applications in the domain of human behavior analysis. Human
behavior data is the data obtained from several sources of human body. Since the human behavior data is very complicated and
noisy, it is difficult to make use of the data only by simple statistical analysis techniques. Therefore, this course introduces
data science techniques, such as machine learning, that can be applied to better understand human behavior data. The course
provides several exercises using Python tools for hands-on experience with data handling and analysis as well as data interpretation
in a domain-oriented manner.
The course will be conducted in face-to-face and interactive style. There will be several individual and group projects throughout the course. All lectures and group activities will be conducted in English only.
The course will be conducted in face-to-face and interactive style. There will be several individual and group projects throughout the course. All lectures and group activities will be conducted in English only.
You are expected to obtain knowledge, understanding, and technical skills related to data science as well as able to apply
it to the analysis of human behavior data.
- The students can explain the core concept of data science pipeline.
- The students can analyze and interpret data related to human behavior.
- The students can explain and discuss about the effective use of data science in the domain of human behavior analysis.
| Assignments | In-class activities | Examinations | Total. | |
|---|---|---|---|---|
| 1. | 5% | 10% | 20% | 35% |
| 2. | 10% | 5% | 20% | 35% |
| 3. | 5% | 5% | 20% | 30% |
| Total. | 20% | 20% | 60% | - |
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1． | Introduction to data science and human behavior | Review after class and do assignment | 190minutes |
| 2． | Data science pipeline and tools | Review after class and do assignment | 190minutes |
| 3． | Design and analysis of human experiment (1) | Review after class and do assignment | 190minutes |
| 4． | Design and analysis of human experiment (2) | Review after class and do assignment | 190minutes |
| 5． | Explanatory data analysis and visualization (1) | Review after class and do assignment | 190minutes |
| 6． | Explanatory data analysis and visualization (2) | Review after class and do assignment | 190minutes |
| 7． | Mid-term examination & Discussion on solutions | Review of all previous classes and preparation for examination | 220minutes |
| 8． | Data preprocessing and cleaning / Feature engineering for human behavior data | Review after class and do assignment | 190minutes |
| 9． | Introduction to Machine Learning | Review after class and do assignment | 190minutes |
| 10． | Machine learning tools and techniques / Machine learning modeling and evaluation (1) | Review after class and do assignment | 190minutes |
| 11． | Machine learning modeling and evaluation (2) | Review after class and do assignment | 190minutes |
| 12． | Machine learning modeling and evaluation (3) | Review after class and do assignment | 190minutes |
| 13． | Model interpretation and applications | Review after class and do assignment | 190minutes |
| 14． | Final examination & Discussion on solutions | Review of previous classes and preparation for examination | 220minutes |
| Total. | - | - | 2720minutes |
The scores for assignments and in-class activities are given based on the quality of the contents of reports/presentations,
the remarks in the discussions, and the degree of interaction and participation in class activities.
Assignments and in-class activities will contribute 40% of your grade.
Mid-term and final examinations will contribute 60% of your grade.
(*Those who get at least 60% of the total score will receive the credits of this course.)
Assignments and in-class activities will contribute 40% of your grade.
Mid-term and final examinations will contribute 60% of your grade.
(*Those who get at least 60% of the total score will receive the credits of this course.)
| ways of feedback | specific contents about "Other" |
|---|---|
| The Others | The lecturer will give feedback in class. |
Experience in basic Python programming is required, as in-class exercises will involve advanced Python libraries.
(*There will be no review of the basic Python programming.)
(*There will be no review of the basic Python programming.)
- By appointment.
Please contact me by e-mail: tipporn@shibaura-it.ac.jp
- Course that cultivates a basic problem-solving skills
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | N/A |
Last modified : Tue Aug 27 13:57:33 JST 2024