2021 Methodology of Mathematical and Computational Analysis I

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Academic unit or major
Graduate major in Technology and Innovation Management
Mejia Caballero Cristian Andres  Sasahara Kazutoshi 
Course component(s)
Lecture / Exercise    (ZOOM)
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

Students will learn data science to utilize vast and diverse data for business, and acquire basic skills in data analysis. In particular, we will lecture on the characteristics of structured data and their analysis methods, keeping in mind its application to technology management, and acquire basic skills in data analysis through programming exercises.

Student learning outcomes

The goals of this course are as follows:
- To understand the basics of data visualization, statistical analysis, and machine learning
- To be able to use these methods to structured data for understanding and solving business problems


Descriptive statistics, hypothesis testing, data visualization, regression, classification, prediction, clustering, association analysis

Competencies that will be developed

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

Class flow

We will lecture on the basics of statistics and machine learning for structured data, and through programming exercises, students will solidify their understanding and develop practical skills for data analysis (using Python and R). In addition, we will invite a corporate data scientist to lecture on the cutting-edge applications of data science in business.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to data science Understand the overview of data science in business
Class 2 Data visualization and statistical analysis Understand theories and methods for data visualization and statistical analysis
Class 3 Programming exercise (1) Acquire programming skills for data visualization and statistical analysis
Class 4 Supervised learning Understand typical supervised learning methods, such as regression, prediction, classification.
Class 5 Unsupervised learning Understand typical unsupervised learning methods, such as clustering and association
Class 6 Programming exercise (2) Acquire programming skills for supervised and unsupervised learning
Class 7 Guest lecture Gain knowledge about cutting-edge data science applications in business

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

After the lecture, it is recommended to read and review the relevant sections of the reference books.


Slides will be provided.

Reference books, course materials, etc.

Foster Provost Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking O'Reilly Media (2013)

Assessment criteria and methods

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

Related courses

  • TIM.B412 : Strategic Management for Research and Development I
  • TIM.B413 : Strategic Management for Research and Development II
  • TIM.A414 : Introduction to Models and Experiments in Social Science
  • TIM.B535 : Digital Marketing
  • TIM.A406 : Methodology of Mathematical and Computational Analysis II

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


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