2026 (Current Year) Faculty Courses School of Environment and Society Department of Technology and Innovation Management Graduate major in Technology and Innovation Management
Business Data Science
- Academic unit or major
- Graduate major in Technology and Innovation Management
- Instructor(s)
- Kazutoshi Sasahara
- Class Format
- Lecture/Exercise (HyFlex)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Sat (CIC)
- Class
- -
- Course Code
- TIM.A415
- Number of credits
- 0.50.50
- Course offered
- 2026
- Offered quarter
- 2Q
- Syllabus updated
- Mar 5, 2026
- Language
- Japanese
Syllabus
Course overview and goals
In this lecture, students will learn the fundamentals of programming and data science using Python (with supplementary use of R). By understanding basic programming concepts and syntax, and acquiring practical skills such as business data collection, summarization, analysis, and visualization, students will understand the importance of data-driven decision-making processes and develop the ability to utilize these skills for solving business challenges.
Course description and aims
The learning objectives of this lecture are the following three:
- Understand programming concepts and syntax, and be able to create basic programs
- Execute the complete workflow of business data collection, processing, analysis, and visualization
- Integrate these skills and be able to apply data science methods to business problems
Keywords
Python, R, preprocessing, data processing, data visualization, data science, business applications
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
The course consists of lectures and practical exercises. In the first half of each session, concepts and methods will be explained, followed by programming exercises in the second half. Students should bring their own laptops and will use cloud environments such as Google Colaboratory for programming.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Fundamentals of Business Data Science (1) |
Learn the overview of business data science and the basic concepts of data-driven decision making. Study the fundamental elements of Python, including data types, variables, and basic operations, to acquire the foundations of programming. |
| Class 2 | Fundamentals of Business Data Science (2) |
Learn control structures and functions in Python, as well as how to use standard functions and standard libraries. Additionally, understand the basics of object-oriented programming. |
| Class 3 | Processing of Business Data (1) |
Learn the basics of data processing in business and acquire the skills to implement them using Python. Also, learn how to use libraries for data processing and handle file input and output. |
| Class 4 | Processing of Business Data (2) |
Learn the basics of business data wrangling. Additionally, acquire skills in data cleaning and preprocessing, data shaping, and data merging and transformation using Python. |
| Class 5 | Business Data Analysis (1) |
Learn the basics of exploratory data analysis and visualization in business, and acquire the skills to perform them using Python libraries. |
| Class 6 | Business Data Analysis(2) |
Acquire fundamental knowledge and practical skills in business data collection and advanced visualization techniques. |
| Class 7 | Business Datathon |
Conduct exercises and presentations related to business data analysis. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to references and other course material.
Textbook(s)
Slides will be provided.
Reference books, course materials, etc.
- Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Jupyter
Evaluation methods and criteria
Class participation 10%, Exercises 30%, Assignment reports 60%
Related courses
- TIM.A405 ： Methodology of Mathematical and Computational Analysis I
- TIM.A406 ： Methodology of Mathematical and Computational Analysis II
- TIM.B536 ： Computational Social Science
Prerequisites
None