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.
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
|✔ Specialist skills||Intercultural skills||Communication skills||Critical thinking skills||✔ Practical and/or problem-solving skills|
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|
|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|
After the lecture, it is recommended to read and review the relevant sections of the reference books.
Slides will be provided.
Foster Provost Tom Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking O'Reilly Media (2013)
Class contribution 20%, Exercise 40%, Report 40%