G8354000 Special Lectures(Programming)

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Economics Economics Evening Course

Lecture Code G8354000 Subject Classification Specialized Education

Subject Name 特別講義（プログラミング）

Subject Name
（Katakana）トクベツコウギ（プログラミング）

Subject Name in
English Special Lectures(Programming)

Instructor WAKUDA YUKI

Instructor
(Katakana) ワクダ　ユウキ

Campus Higashi-Senda Semester/Term 2nd-Year,  Second Semester,  Second Semester

Days, Periods, and Classrooms (2nd) Tues13-14：Higashi-Senda Lecture Rm M303

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

Credits 2.0 Class Hours/Week 2 Language of Instruction J : Japanese

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 09 : Information Management

Eligible Students

Keywords Programming, Python, Economic Data Science, Machine Learning, Artificial Intelligence (AI)

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline <Objectives>
  This course aims to develop, through programming, the basic data-utilization skills required of students in economics-related fields. The goal is not to be able to write every line of code (software program) by hand, but to acquire the ability to build and run an end-to-end process of loading, transforming, aggregating, visualizing, and analyzing data while making appropriate use of AI. In doing so, students learn to critically verify AI-generated code, correctly understand the functionality they need, and confirm the correctness of the results themselves.

<Outline>
  Using a Python environment on Google Colaboratory, this course starts from the basics of programming and systematically covers data manipulation, visualization, statistical processing, and simulation over 15 sessions. Sessions 1-5 cover the basic syntax of Python; from session 6 onward, students carry out practical data processing while actively using generative AI. Each session includes hands-on exercises matched to its content so that students can feel the effect of what they learn. The exercise materials use data connected to other specialized subjects such as economics, business administration, and statistics, so that students experience programming as a tool that deepens their other studies.

Class Schedule Session 1  Introduction: Social Trends and the Significance of Programming
  Consider why programming is needed in the age of AI and data science. Set up a programming environment and run your first code.
  - Background of the AI/DS era and the significance of learning Python
  - Setting up the working environment (Google Colab / Notebook operations)
  - Markdown cell syntax / Notebook section structure / writing comments
  - [Exercise] Creating a Notebook / writing text in Markdown / running simple Python code
Session 2  Loading Files and Open Data
  Learn how to load tabular data into a Notebook. Learn how to find and obtain open data.
  - Loading Excel files
  - [Exercise] Load Excel data and check its contents.
Session 3  Variables, Basic Data Types, and Collection Types
  Learn the basics of handling data in Python: variables, types, and operations.
  - The concept of variables; numeric types (int/float), strings (str), Booleans (bool)
  - Arithmetic operations / string operations
  - Collection types (list/dict/tuple/set)
  - [Exercise] Using data types and collection types
Session 4  Conditional Branching and Loops
  Learn if statements and for loops, and express repeated calculations and conditional processing in code.
  - Conditional branching (if/elif/else) and logical operators
  - for loops / while loops / comprehensions
  - [Exercise] Loop control examples (e.g., compound-interest investment simulation)
Session 5  Logical Thinking, Functions, and Modules
  Design program processing with flowcharts.
  - Input/process/output-oriented design and flowchart representation
  - Defining functions (def / arguments / return values) and using modules (import)
  - [Exercise] Creating functions / pipeline processing
Session 6  Introduction to AI-assisted Coding and Debugging Practices
  Programming with AI. Learn how to verify and debug AI-generated code.
  - How to use Gemini on Colab and design prompts
  - Examples of reading and verifying AI-generated code
  - [Exercise] Creating a data-processing function with AI (build and verify a function that scores free-text survey responses)
Session 7  Pandas Basics: DataFrame and Basic Operations
  Master the basic operations on tabular data.
  - Structure of a DataFrame
  - Column operations (selecting variables, etc.)
  - Row operations (removing rows with missing values, etc.)
  - Pandas tips (column-name restrictions and workarounds, etc.)
  - [Exercise] (AI-assisted) Loading Excel data / column operations / filtering
Session 8  Pandas in Practice (1): Joining Data
  Learn how to join multiple datasets and compute new indicators.
  - Creating variables by calculating new columns
  - Joining multiple tables (left/inner/outer join) and how their behavior differs
  - Inspecting row counts and contents after joining
  - [Exercise] (AI-assisted) Integrating multiple datasets / checking the behavior
Session 9  Pandas in Practice (2): Aggregation and Data Cleansing
  Learn to reshape data through aggregation and to handle missing values in real data.
  - Group aggregation (groupby) of long-format data and pivot tables
  - Policies for handling missing values (fillna/dropna) and their pitfalls
  - Data type conversion
  - [Exercise] (AI-assisted) Aggregating journal entries and creating a trial balance
Session 10  AI-assisted Coding in Practice and Tips
  Systematically acquire the practical conventions needed to collaborate with AI.
  - Coding conventions (use functions wherever possible / settings at the top / file-naming rules for saved files / example folder structures, etc.)
  - Handling large-scale processing: adding progress indicators
  - Using AI with an understanding of its limitations and characteristics
  - Reviewing AI-generated code and spotting what is wrong
  - [Exercise] (AI-assisted) Build-review-improve cycle for a data-processing pipeline
Session 11  Matplotlib: Creating Charts and Chart Quality
  Learn to create charts and the quality standards for charts that communicate.
  - Creating line charts, scatter plots, and bar charts
  - Setting axis labels, units, legends, and titles
  - Chart quality standards (color-vision accessibility / font size / aspect ratio)
  - Arranging multiple charts (subplot)
  - [Exercise] (AI-assisted) Time-series chart of GDP growth rates
Session 12  Using Libraries: A Hands-on Experience with Statistics
  Experience the convenience of libraries through coding statistical processing.
  - Calculating correlation coefficients and exploring relationships between variables
  - Benefits of importing libraries (scipy, etc.)
  - Hands-on coding of statistical tests (t-test / U-test)
  - [Exercise] (AI-assisted) Exploring correlations between variables and running statistical tests
Session 13  Processing and Visualizing Survey Data
  Aggregate and visualize survey data in collaboration with AI.
  - Workflow for loading, aggregating, and visualizing survey data
  - Interactive iteration to improve charts (colors, legends, fonts, titles)
  - [Exercise] (AI-assisted) Aggregating survey data / designing a survey
Session 14  Simulating Economic Models
  Reproduce well-known economic theories through programming and experience models of human behavior.
  - Price-competition simulation
  - [Exercise] (AI-assisted) Market simulation
Session 15  Dynamic Charts and Dashboards; Overall Summary
  Integrate the techniques learned so far to build an interactive dashboard.
  - Creating dynamic charts with Plotly
  - Exporting to HTML files (as distributable deliverables)
  - Abstraction through descriptive statistics and the importance of checking the raw data
  - [Exercise] (AI-assisted) Building a dashboard by prefecture
  - Review; the relationship between programming and other courses, etc.

Text/Reference
Books,etc. We instruct the textbook at the first lesson.

PC or AV used in
Class,etc. Handouts, Zoom, Other (see [More Details])

(More Details) PC

Learning techniques to be incorporated PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report

Suggestions on
Preparation and
Review For review: if you could not complete the exercises during class time, finish them before the next session.

Requirements The number of students is restricted to the number of PC in the classroom.

Grading Method Grades will be based on students' participation in class and on assignment reports.
- Per-session reports / class participation / active engagement in lectures and exercises: 60%
- Assignment reports, etc.: 40%

Practical Experience Experienced

Summary of Practical Experience and Class Contents based on it Drawing on the instructor's practical experience in data science, machine learning applications, and AI applications in private companies, the course covers programming techniques that are useful in practice.

Message   Understanding what is happening in society through the power of data is a central question that economics has long engaged with. On the other hand, the skills of processing and analyzing data themselves have tended to be regarded as the job of information scientists and engineers. Now that AI has become so capable, however, the techniques of data processing and analysis have become very accessible. For those interested in economics and sociology, practicing data science through data analysis has become a realistic and significant undertaking.
  With this in mind, this course does not aim at "mastering every corner of programming." Instead, it aims to give you the ability to run, with your own hands, an end-to-end process of loading, transforming, aggregating, visualizing, and analyzing data while making appropriate use of AI. You will grasp the techniques of the Python language and learn to use it efficiently. When using AI, we also value the perspective of not taking AI-generated software code at face value but verifying it yourself.
  The exercises in each session are designed to link with what you learn in other courses. I hope to create many moments of "what came up in that class, I could actually run here." Programming will become an important tool for deepening your understanding of economics, and I hope that when this course ends, you will be left with practical foundational skills to build and run your own data processing.

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	School of Economics Economics Evening Course
Lecture Code	G8354000	Subject Classification	Specialized Education
Subject Name	特別講義（プログラミング）
Subject Name （Katakana）	トクベツコウギ（プログラミング）
Subject Name in English	Special Lectures(Programming)
Instructor	WAKUDA YUKI
Instructor (Katakana)	ワクダ　ユウキ
Campus	Higashi-Senda	Semester/Term	2nd-Year, Second Semester, Second Semester
Days, Periods, and Classrooms	(2nd) Tues13-14：Higashi-Senda Lecture Rm M303
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)

Credits	2.0	Class Hours/Week	2	Language of Instruction	J : Japanese
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	09 : Information Management
Eligible Students
Keywords	Programming, Python, Economic Data Science, Machine Learning, Artificial Intelligence (AI)
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	<Objectives> This course aims to develop, through programming, the basic data-utilization skills required of students in economics-related fields. The goal is not to be able to write every line of code (software program) by hand, but to acquire the ability to build and run an end-to-end process of loading, transforming, aggregating, visualizing, and analyzing data while making appropriate use of AI. In doing so, students learn to critically verify AI-generated code, correctly understand the functionality they need, and confirm the correctness of the results themselves. <Outline> Using a Python environment on Google Colaboratory, this course starts from the basics of programming and systematically covers data manipulation, visualization, statistical processing, and simulation over 15 sessions. Sessions 1-5 cover the basic syntax of Python; from session 6 onward, students carry out practical data processing while actively using generative AI. Each session includes hands-on exercises matched to its content so that students can feel the effect of what they learn. The exercise materials use data connected to other specialized subjects such as economics, business administration, and statistics, so that students experience programming as a tool that deepens their other studies.
Class Schedule	Session 1 Introduction: Social Trends and the Significance of Programming Consider why programming is needed in the age of AI and data science. Set up a programming environment and run your first code. - Background of the AI/DS era and the significance of learning Python - Setting up the working environment (Google Colab / Notebook operations) - Markdown cell syntax / Notebook section structure / writing comments - [Exercise] Creating a Notebook / writing text in Markdown / running simple Python code Session 2 Loading Files and Open Data Learn how to load tabular data into a Notebook. Learn how to find and obtain open data. - Loading Excel files - [Exercise] Load Excel data and check its contents. Session 3 Variables, Basic Data Types, and Collection Types Learn the basics of handling data in Python: variables, types, and operations. - The concept of variables; numeric types (int/float), strings (str), Booleans (bool) - Arithmetic operations / string operations - Collection types (list/dict/tuple/set) - [Exercise] Using data types and collection types Session 4 Conditional Branching and Loops Learn if statements and for loops, and express repeated calculations and conditional processing in code. - Conditional branching (if/elif/else) and logical operators - for loops / while loops / comprehensions - [Exercise] Loop control examples (e.g., compound-interest investment simulation) Session 5 Logical Thinking, Functions, and Modules Design program processing with flowcharts. - Input/process/output-oriented design and flowchart representation - Defining functions (def / arguments / return values) and using modules (import) - [Exercise] Creating functions / pipeline processing Session 6 Introduction to AI-assisted Coding and Debugging Practices Programming with AI. Learn how to verify and debug AI-generated code. - How to use Gemini on Colab and design prompts - Examples of reading and verifying AI-generated code - [Exercise] Creating a data-processing function with AI (build and verify a function that scores free-text survey responses) Session 7 Pandas Basics: DataFrame and Basic Operations Master the basic operations on tabular data. - Structure of a DataFrame - Column operations (selecting variables, etc.) - Row operations (removing rows with missing values, etc.) - Pandas tips (column-name restrictions and workarounds, etc.) - [Exercise] (AI-assisted) Loading Excel data / column operations / filtering Session 8 Pandas in Practice (1): Joining Data Learn how to join multiple datasets and compute new indicators. - Creating variables by calculating new columns - Joining multiple tables (left/inner/outer join) and how their behavior differs - Inspecting row counts and contents after joining - [Exercise] (AI-assisted) Integrating multiple datasets / checking the behavior Session 9 Pandas in Practice (2): Aggregation and Data Cleansing Learn to reshape data through aggregation and to handle missing values in real data. - Group aggregation (groupby) of long-format data and pivot tables - Policies for handling missing values (fillna/dropna) and their pitfalls - Data type conversion - [Exercise] (AI-assisted) Aggregating journal entries and creating a trial balance Session 10 AI-assisted Coding in Practice and Tips Systematically acquire the practical conventions needed to collaborate with AI. - Coding conventions (use functions wherever possible / settings at the top / file-naming rules for saved files / example folder structures, etc.) - Handling large-scale processing: adding progress indicators - Using AI with an understanding of its limitations and characteristics - Reviewing AI-generated code and spotting what is wrong - [Exercise] (AI-assisted) Build-review-improve cycle for a data-processing pipeline Session 11 Matplotlib: Creating Charts and Chart Quality Learn to create charts and the quality standards for charts that communicate. - Creating line charts, scatter plots, and bar charts - Setting axis labels, units, legends, and titles - Chart quality standards (color-vision accessibility / font size / aspect ratio) - Arranging multiple charts (subplot) - [Exercise] (AI-assisted) Time-series chart of GDP growth rates Session 12 Using Libraries: A Hands-on Experience with Statistics Experience the convenience of libraries through coding statistical processing. - Calculating correlation coefficients and exploring relationships between variables - Benefits of importing libraries (scipy, etc.) - Hands-on coding of statistical tests (t-test / U-test) - [Exercise] (AI-assisted) Exploring correlations between variables and running statistical tests Session 13 Processing and Visualizing Survey Data Aggregate and visualize survey data in collaboration with AI. - Workflow for loading, aggregating, and visualizing survey data - Interactive iteration to improve charts (colors, legends, fonts, titles) - [Exercise] (AI-assisted) Aggregating survey data / designing a survey Session 14 Simulating Economic Models Reproduce well-known economic theories through programming and experience models of human behavior. - Price-competition simulation - [Exercise] (AI-assisted) Market simulation Session 15 Dynamic Charts and Dashboards; Overall Summary Integrate the techniques learned so far to build an interactive dashboard. - Creating dynamic charts with Plotly - Exporting to HTML files (as distributable deliverables) - Abstraction through descriptive statistics and the importance of checking the raw data - [Exercise] (AI-assisted) Building a dashboard by prefecture - Review; the relationship between programming and other courses, etc.
Text/Reference Books,etc.	We instruct the textbook at the first lesson.
PC or AV used in Class,etc.	Handouts, Zoom, Other (see [More Details])
(More Details)	PC
Learning techniques to be incorporated	PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report
Suggestions on Preparation and Review	For review: if you could not complete the exercises during class time, finish them before the next session.
Requirements	The number of students is restricted to the number of PC in the classroom.
Grading Method	Grades will be based on students' participation in class and on assignment reports. - Per-session reports / class participation / active engagement in lectures and exercises: 60% - Assignment reports, etc.: 40%
Practical Experience	Experienced
Summary of Practical Experience and Class Contents based on it	Drawing on the instructor's practical experience in data science, machine learning applications, and AI applications in private companies, the course covers programming techniques that are useful in practice.
Message	Understanding what is happening in society through the power of data is a central question that economics has long engaged with. On the other hand, the skills of processing and analyzing data themselves have tended to be regarded as the job of information scientists and engineers. Now that AI has become so capable, however, the techniques of data processing and analysis have become very accessible. For those interested in economics and sociology, practicing data science through data analysis has become a realistic and significant undertaking. With this in mind, this course does not aim at "mastering every corner of programming." Instead, it aims to give you the ability to run, with your own hands, an end-to-end process of loading, transforming, aggregating, visualizing, and analyzing data while making appropriate use of AI. You will grasp the techniques of the Python language and learn to use it efficiently. When using AI, we also value the perspective of not taking AI-generated software code at face value but verifying it yourself. The exercises in each session are designed to link with what you learn in other courses. I hope to create many moments of "what came up in that class, I could actually run here." Programming will become an important tool for deepening your understanding of economics, and I hope that when this course ends, you will be left with practical foundational skills to build and run your own data processing.
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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