Practical Data Science 4

Course title

33032400

Middle-level Diploma Policy (mDP)

Program / Major	mDP	Goals
Data Science Course	DP-3・1	技術文書の作成、口頭発表、討論等のコミュニケーションができる。
Data Science Course	DP-4d・2	キャリアを見据えた高度な専門知識多様なデータを収集・分析・予測する技術を駆使し、社会に存在する実際の課題に対してエビデンスを基に解決法を考え提案できる。

Purpose of class

The purpose of this course is for students to acquire management skills for smoothly advancing projects while guiding second-year students, as well as the ability to analyze the essence of practical issues through dialogue with problem proposers and to conduct advanced analysis and proposal making by using data science, data engineering, and AI through Project Based Learning (PBL).

Course description

Project Based Learning (PBL) is effective for acquiring practical skills in data science, data engineering, and AI. In this exercise course, students work on real social issues and conduct analysis and proposal making based on requests from problem proposers engaged in practice by using data science, data engineering, and AI. Through group work conducted jointly with students from other years, students experience the full process of understanding the problem, preprocessing data, conducting exploratory analysis, selecting methods, developing and validating models, interpreting and reporting analytical results, preparing proposals, and giving presentations. Students in this course acquire the ability to take charge of project management, guidance for junior students, and core analytical work as group leaders, subleaders, or core members.

Goals and objectives

Understand the basics of project management and demonstrate leadership.
Analyze the essence of practical issues through dialogue with problem proposers and propose appropriate solutions.
Plan and conduct data preprocessing and exploratory analysis, and select appropriate methods.
Implement advanced machine learning models and evaluate and improve them to increase accuracy.
Appropriately visualize analytical results and make effective proposals to companies and stakeholders.
Acquire management skills to smoothly advance projects while guiding second-year students.

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	Progress explanation materials for each class	Comprehensive assignment	Total.
1.	5%	5%	10%
2.	10%	10%	20%
3.	10%	10%	20%
4.	10%	10%	20%
5.	10%	10%	20%
6.	5%	5%	10%
Total.	50%	50%	-

Evaluation method and criteria

Students who earn 60 points or higher in the overall evaluation will pass. Students will be evaluated on a 100-point scale based on progress explanation materials for each class (50%) and the comprehensive assignment (50%). The progress explanation materials will assess PBL progress, dialogue with problem proposers, project management, guidance for second-year students, planning and implementation of data preprocessing and exploratory analysis, explanation of analytical results, and fulfillment of roles within the group. The comprehensive assignment will assess understanding of the essence of practical issues, proposal of appropriate solutions, selection of analytical methods, implementation, evaluation, and improvement of advanced machine learning models, visualization of analytical results, and the completeness of evidence-based proposals and reports to companies and stakeholders. A score of 60 indicates the level at which the minimum necessary knowledge and skills for this course have been acquired.

Language

Japanese

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	PBL procedures, team formation, and project management	Review PBL procedures, team formation, and the basics of project management in advance.	180minutes
1.	PBL procedures, team formation, and project management	Organize the team structure, role assignments, and project management policy.	200minutes
2.	Sharing issues with problem proposers and developing a project plan	Research the background of the problem proposer, the problem area, and possible data in advance.	180minutes
2.		Organize the contents of issue sharing, and summarize the project plan and analysis policy.	200minutes
3.	Advanced data collection and preprocessing, and guidance	Review advanced data collection and preprocessing methods and how to explain them to second-year students.	180minutes
3.	Advanced data collection and preprocessing, and guidance	Check preprocessing results and organize guidance content and improvement points for second-year students.	200minutes
4.	Design and practice of exploratory data analysis (EDA) (1): basic aggregation	Review how to design exploratory data analysis and proceed with basic aggregation.	180minutes
4.		Conduct basic aggregation based on the EDA design and organize the results.	200minutes
5.	Design and practice of exploratory data analysis (EDA) (2): basic analysis	Review basic analysis and visualization methods in exploratory data analysis.	180minutes
5.		Conduct basic analysis and summarize features and trends related to problem solving.	200minutes
6.	Selecting analytical methods suited to the problem and providing guidance	Research candidate analytical methods suited to the problem, selection criteria, and guidance methods.	180minutes
6.		Prepare explanatory materials on the reasons for selecting analytical methods and guidance for second-year students.	200minutes
7.	Advanced implementation and evaluation of machine learning models	Review methods for implementing advanced machine learning models and viewpoints for evaluation.	180minutes
7.		Implement and evaluate a machine learning model, and organize the results and improvement issues.	200minutes
8.	Practice in model evaluation and improvement (1): conducting evaluation	Review model evaluation indicators and how to interpret evaluation results.	180minutes
8.		Conduct model evaluation and summarize the evaluation results and improvement policy.	200minutes
9.	Practice in model evaluation and improvement (2): improvement based on evaluation	Research methods for model improvement and accuracy enhancement based on evaluation results.	180minutes
9.		Improve the model, compare the results before and after improvement, and discuss the findings.	200minutes
10.	Visualization of analytical results and document preparation	Review methods for visualizing analytical results and structuring documents.	180minutes
10.		Visualize analytical results and prepare an outline of the document.	200minutes
11.	Creating data-based problem-solving proposals (1): examining proposal structure	Review proposal structures based on data and methods for explaining them to stakeholders.	180minutes
11.		Examine the structure of the problem-solving proposal and organize the analytical results that support it.	200minutes
12.	Creating data-based problem-solving proposals (2): finalizing the proposal	Check the validity of the proposal, missing analyses, and points for improving presentation materials.	180minutes
12.		Finalize the proposal materials and improve the explanation for the presentation.	200minutes
13.	Final presentation: presentation of proposals	Review the presentation materials and prepare to explain the proposal and analytical evidence.	160minutes
13.	Final presentation: presentation of proposals	Organize questions and comments received during the presentation and reflect them in the comprehensive assignment.	210minutes
14.	Project reflection and feedback to junior students	Organize the overall outcomes and issues of the project and feedback for second-year students.	160minutes
14.	Project reflection and feedback to junior students	Based on the reflection, summarize improvement measures for the next academic year and issues in guidance.	210minutes
Total.	-	-	5300minutes