2025 (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
- Class
- -
- Course Code
- TIM.A415
- Number of credits
- 0.50.50
- Course offered
- 2025
- Offered quarter
- 2Q
- Syllabus updated
- Mar 19, 2025
- 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 | Introduction | 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 | Programming Fundamentals (1) | Learn Python's basic syntax, conditional branching, loops, and how to define and use functions. Additionally, acquire skills in manipulating lists and dictionaries, and methods for basic file input/output. |
| Class 3 | Programming Fundamentals (2) | Learn the concepts of basic algorithms and data structures, and acquire methods for implementing them in Python. Also study the fundamentals of object-oriented programming. |
| Class 4 | Data Summarization and Visualization (1) | Learn how to manipulate dataframes and calculate basic statistical measures. Create histograms, box plots, and scatter plots using visualization libraries, and acquire the fundamentals of correlation analysis. |
| Class 5 | Data Summarization and Visualization (2) | Deepen the understanding of data visualization techniques for time series and other data, as well as effective methods of data representation. |
| Class 6 | Business Data Processing and Analysis | Learn data classification techniques (binary classification, multi-class classification) using libraries, clustering techniques, and methods for processing unstructured data such as text data. Acquire applications of these methods through exercises using real data. |
| Class 7 | Business Applications | Acquire the fundamentals of application development useful in business settings, such as automation of data collection and analysis through programming, integration with databases, and utilization of APIs. |
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