Print

2023　Methodology of Mathematical and Computational Analysis II

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Technology and Innovation Management
Instructor(s)
Sasahara Kazutoshi 
Class Format
Lecture / Exercise    (HyFlex)
Media-enhanced courses
Day/Period(Room No.)
Sat1-2(CIC)  
Group
-
Course number
TIM.A406
Credits
1
Academic year
2023
Offered quarter
4Q
Syllabus updated
2023/3/20
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

Students will learn data science to use vast and diverse data for business. In particular, the characteristics of structured and unstructured data and their analysis methods will be lectured, with application to technology management in mind, and students will acquire basic skills in business data analysis through programming exercises.

Student learning outcomes

The goals of this course are as follows:
- To understand the basics of machine learning, text analysis, network analysis
- To be able to use data analysis to understand and solve business problems

Keywords

Regression, classification, prediction, clustering, text analysis, network analysis

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills ✔ Practical and/or problem-solving skills

Class flow

The theory of data science for analyzing structured and unstructured data will be lectured, and programming exercises will be used to consolidate understanding and develop practical skills in business data analysis. Python will be used for programming exercises.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to Business Data Science Understand the big picture of business data science and learn the basics of programming
Class 2 Supervised learning Understand typical supervised learning methods, such as regression, prediction, and classification
Class 3 Unsupervised learning Understand typical unsupervised learning methods, such as clustering and dimensionality reduction
Class 4 Programming exercise 1 Develop programming skills for supervised and unsupervised learning
Class 5 Network analysis Understand the nature of network data and the principles and methods for visualizing and analyzing networks
Class 6 Text analysis Understand the nature of text data and the principles and methods of natural language processing, including morphological analysis and sentiment analysis
Class 7 Programming exercise 2 Develop programming skills related to network and text analysis

Out-of-Class Study Time (Preparation and Review)

It is recommended to read and review the relevant sections of the reference books after the lecture.

Textbook(s)

Slides will be provided.

Reference books, course materials, etc.

- Sebastian Raschka　et al. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Assessment criteria and methods

Class contribution 20%, Exercise 40%, Report 40%

Related courses

  • TIM.A414 ： Introduction to Models and Experiments in Social Science
  • TIM.B535 ： Digital Marketing
  • TIM.A405 ： Methodology of Mathematical and Computational Analysis I

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

Page Top