社会学応用研究

令和元年度入学者	社会学応用研究
教員名	菅野剛
単位数		課程	後期課程	開講区分	文理学部
科目群	社会学専攻
学期	通年	履修区分	選択必修

指導可能な研究領域	- Quantitative Social Science - Data Science
目標	- We are facing the Fourth Industrial Revolution (4IR). The times have changed outside the class. Worth learning is the STEAM not in the class. - Learn Data Science the hard way. You will need several other courses on social survey, statistics, data analysis, programming, machine learning, and AI elsewhere. - Deep dive into one’s own research with data science: statistics, programming, and data analysis. - Submitting research papers to academic journals, making presentations at the conferences, writing a doctoral dissertation. - Report of the Central Council for Education http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo4/houkoku/1412988.htm - Coucil for Science, Technology and Innovation https://www.kantei.go.jp/jp/singi/tougou-innovation/dai2/siryo1.pdf https://www.kantei.go.jp/jp/singi/tougou-innovation/dai4/siryo1-1.pdf
方法	https://sites.google.com/a/nihon-u.ac.jp/sugano-lab/home/google-classroom - Occasional meeting. - Conduct own project and research, analyse data by oneself, discuss with others. - Study and research outside the class: Academic conferences, meetings on data science, etc. - NU-MailG accounts and joining to Google Classroom are required. - Prerequisites: Deep understanding in statistics, good programming skill in Google Colaboratory and Python, rich experience in social survey and multivariate data analysis with various types of data, various skills in Linux operations. - BYOD: Bring your own device. - We do not provide support for the Windows operating system due to shortage of human resource. - Courses are to be closed upon no registration.

その他
評価方法	- Presentations at academic associations, writing papers at academic journals will be considered for grades. - Self-directedness and Intellectual flexibility.
オフィスアワー	- Appointment times will generally be available after the class. Ask any questions at any time on Google Classroom.