While the use of data is highly expected in business, correct understanding about methodology of data analysis is required for appropriate decision-making.
In this lecture, students learn basic knowledge for quantitative analysis, especially statistical analysis, in Management of Technology.
The goals of this course are as follows:
- To understand the basics of data analysis so that students can use them to solve business problems.
- To be able to program for elementary data analysis, especially statistical analysis.
Statistics, data science, quantitative analysis, descriptive statistics, hypothesis testing, regression analysis
✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Lectures provide the knowledge on elementary data analysis and programming exercises is scheduled after each topic.
In the programming exercises, students overview the flow of data analysis using Python and develop the ability applying data analysis to business problems.
Course schedule | Required learning | |
---|---|---|
Class 1 | Guidance | Understand the overview of data science and quantitative analysis, especially statistical analysis, in business |
Class 2 | Descriptive statistics | Understand the basic summary statistics and data visualization methods |
Class 3 | Programming exercise (1) | Acquire programming skills how to use Python and for descriptive statistics through exercises |
Class 4 | Hypothesis testing | Understand the procedures for hypothesis testing and what to pay attention to when applying it |
Class 5 | Programming exercise (2) | Acquire programming skills for data visualization and hypothesis testing through exercises |
Class 6 | Regression analysis | Understand what kind of problems to be suit for regression analysis and its assumptions behind performing |
Class 7 | Programming exercise (3) | Acquire programming skills for regression analysis through exercises |
It is recommended to read and review the relevant sections of the references after the lecture.
In order to master programming, learning by writing code on outside of class is also recommended.
None required.
Lecture materials will be distributed.
In addition to those listed below, references will be introduced in the lecture.
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%
No prerequisites.