Course title
1M9922001
Data Science for Human Behavior Analysis

LAOHAKANGVALVIT TIPPORN
Course content
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.
Purpose of class
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.
Goals and objectives
  1. The students can explain the core concept of data science pipeline.
  2. The students can analyze and interpret data related to human behavior.
  3. The students can explain and discuss about the effective use of data science in the domain of human behavior analysis.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

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% -
Language
English
Class schedule

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
Evaluation method and criteria
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.)
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
The Others The lecturer will give feedback in class.
Textbooks and reference materials
Materials will be provided in Scombz before each class.
Prerequisites
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.)
Office hours and How to contact professors for questions
  • By appointment.
    Please contact me by e-mail: tipporn@shibaura-it.ac.jp
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic problem-solving skills
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 4.QUALITY EDUCATION
Last modified : Tue Aug 27 13:57:33 JST 2024