| Program / Major | mDP | Goals |
|---|---|---|
| Software Course | DP-4b・2 | キャリアを見据えた高度な専門知識 ソフトウェア開発手法、情報ネットワーク、および機械学習に関する技術を駆使し、社会的ニーズに適切に対応したソフトウェアを開発できる。 |
| Data Science Course | DP-4d・1 | 研究者・技術者としての基礎的素養 データ解析とそれに必要となる機械学習、プログラミング技術の専門知識を習得し、課題解決の鍵となる有効なエビデンスを見いだせる。 |
| Sports Engineering Course | DP-4c・2 | キャリアを見据えた高度な専門知識 運動機能・パフォーマンスを科学的に解析し、論理的に課題解決できる。 |
The purpose of this course is to systematically learn the fundamentals through applications of data analysis using Python,
and to develop the ability to properly process, analyze, and visualize real data. Data analysis consists of a wide range of
processes, including data collection and preprocessing, application of statistical methods, construction of predictive models,
and interpretation of results through visualization. This course provides hands-on experience with these processes so that
students acquire the skills needed to work with data.
This course covers the fundamentals through applications of data analysis using the Python programming language. Students
will learn data preprocessing (cleaning and handling missing values), statistical analysis, time series data analysis, multivariate
analysis, and visualization techniques through hands-on exercises using real data. In particular, the course aims to develop
the ability to understand the characteristics of data and select appropriate methods. Students will also learn to apply regression
analysis, logistic regression, and decision trees so that they can put insights gained from data analysis to practical use.
- Students will be able to use Python to read data, check data types, and perform basic data processing tasks such as extraction, merging, aggregation, handling missing values, and data transformation.
- Students will understand the basic concepts of descriptive statistics, statistical estimation and testing, and data visualization, and will be able to appropriately grasp the characteristics of data and the relationships between variables.
- Students will be able to select an appropriate method — regression analysis, classification, clustering, time series analysis, or multivariate analysis — based on the purpose of the analysis and the characteristics of the data, and to carry out the analysis using Python.
- Students will be able to evaluate the accuracy, validity, and limitations of analysis results, and to clearly explain the analysis process and the resulting insights to a third party using charts and figures.
| Pre/Post-class assignments | Comprehensive assignment | Total. | |
|---|---|---|---|
| 1. | 20% | 5% | 25% |
| 2. | 15% | 10% | 25% |
| 3. | 15% | 10% | 25% |
| 4. | 5% | 20% | 25% |
| Total. | 55% | 45% | - |
Grades are assessed on a 100-point scale based on a total of 28 pre-class and post-class assignments plus a comprehensive
assignment. The overall grade is calculated as the average score of all assignments, and a score of 60 or above is required
to pass. A score of 60 represents, at minimum, the level at which a student understands the content of each session and can
explain what is required by the assignments.
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Session 1: Fundamentals of Data Analysis | Check the syllabus | 30minutes |
| Read the handout materials | 60minutes | ||
| Work on exercise assignments | 120minutes | ||
| 2. | Session 2: Handling Data | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 3. | Session 3: Data Preprocessing | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 4. | Session 4: Statistical Methods 1 — Descriptive Statistics | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 5. | Session 5: Statistical Methods 2 — Estimation and Testing | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 6. | Session 6: Data Visualization | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 7. | Session 7: Regression Analysis 1 — Simple Regression | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 8. | Session 8: Regression Analysis 2 — Multiple Regression | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 9. | Session 9: Clustering | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 10. | Session 10: Classification Models — Logistic Regression and Decision Trees | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 11. | Session 11: Time Series Data Analysis 1 — Basic Concepts of Time Series Data | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 12. | Session 12: Time Series Data Analysis 2 — AR, MA, ARMA, and ARIMA Models | Read the handout materials | 60minutes |
| Work on exercise assignments | 180minutes | ||
| 13. | Session 13: Multivariate Analysis 1 — Principal Component Analysis | Read the handout materials | 60minutes |
| Work on exercise assignments | 120minutes | ||
| 14. | Session 14: Multivariate Analysis 2 — Hierarchical Cluster Analysis and Discriminant Analysis | Read the handout materials | 60minutes |
| Work on exercise assignments | 180minutes | ||
| Total. | - | - | 2670minutes |
| ways of feedback | specific contents about "Other" |
|---|---|
| Feedback in/outside the class. |
Textbook
Not specified. Lecture materials, exercise data, and Python sample code will be distributed via the course support system.
References
Reference materials related to data processing with Python, statistics, machine learning, and time series analysis will be introduced as appropriate according to the course content.
Not specified. Lecture materials, exercise data, and Python sample code will be distributed via the course support system.
References
Reference materials related to data processing with Python, statistics, machine learning, and time series analysis will be introduced as appropriate according to the course content.
Students are encouraged to review "Introduction to Python" or equivalent content and be able to read and write basic Python
programs, including variables, conditional branching, loops, functions, and lists.
Additionally, please prepare a laptop for use in class and confirm access to the designated Python execution environment. It is also recommended to review basic high school mathematics and fundamental statistical terms such as mean, variance, and standard deviation.
Additionally, please prepare a laptop for use in class and confirm access to the designated Python execution environment. It is also recommended to review basic high school mathematics and fundamental statistical terms such as mean, variance, and standard deviation.
- Questions are accepted at any time via Teams.
- Course that cultivates an ability for utilizing knowledge
- Course that cultivates a basic problem-solving skills
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | Not applicable. |
- 4.QUALITY EDUCATION
- 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Fri Jul 17 04:02:08 JST 2026