Course title
045100031
Exercise in Data Science

 YASUMURA Yoshiaki

NAKAMURA Shingo
Course description
Students will learn the basics of the programming language Python and typical data science analysis methods. First, students will understand how data science techniques are used in the real world. Next, students will learn Python's basic syntax as a data science tool by actually creating programs. In the second half, students will learn more practical data science techniques with a focus on AI. Specifically, students will create data analysis programs in Python and master the basics of statistical processing, machine learning, and data visualization methods.
Purpose of class
This course's purpose is to provide students with a basic level of competence in data science. Students will acquire basic Python programming skills and create basic statistical and machine learning programs.
Goals and objectives
  1. To be able to explain the case studies in the real world
  2. To be able to understand the basic grammar of Python and make a program
  3. To be able to make a program using basic analysis procedure
  4. To be able to visualize the data and analysis results
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Basics of Data Science
Case study of data science
Big data and data science engineering
Data analysis process 		Review of case studies of data science 100minutes
Review about data analysis process 100minutes
2. Basics of Python Programming
A programming environment for Python
Variable
Assignment and arithmetic operators 		Review of variable, assignment, and arithmetic operators 100minutes
Programming assignment about variable, assignment, and arithmetic operators 100minutes
3. Control Statement 1
Conditional branch ( if, elif, and else statements )
Comparison and logical operators 		Review of conditional branch statements 100minutes
Programming assignment about conditional branch 100minutes
4. Control Statement 2
Iteration statements（ for and while ）
List and tuple 		Review of iteration statements 100minutes
Programming assignment about iteration astatements 100minutes
5. Function
Usage of module
Create user functions 		Review of function 100minutes
Programming assignment about function 100minutes
6. Class and Object
Create your own classes and objects
Classes and objects of external modules 		Review of class 100minutes
Programming assignment using classes 100minutes
7. Data Expression
Various data representation
Control data in a program 		Review of data expression 100minutes
Programming assignment controlling data 100minutes
8. Basics of AI
AI history
AI ethics 		Review of AI 100minutes
9. Machine Learning
Basics of machine learning
Nearest neighbor method 		Review of machine learning 100minutes
Programming assignment using the nearest neighbor method 100minutes
10. Regression Analysis
Single regression analysis
Multiple regression analysis 		Review of regression analysis 100minutes
Programming assignment of regression analysis 100minutes
11. Cluster Analysis
k-means method 		Review of cluster analysis 100minutes
Programming assignment using the k-means method 100minutes
12. Neural Network
Artificial neural network
Backpropagation method 		Review of neural network 100minutes
Programming assignment using the backpropagation method 100minutes
13. Deep Learning
Deep neural network
Convolutional neural network 		Review of deep learning 100minutes
Programming assignment of deep learning 100minutes
14. Final examination and explanation Review of final examination 150minutes
Total. - - 2650minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 課題 期末試験 Total.
1. 10% 10% 20%
2. 10% 10% 20%
3. 15% 15% 30%
4. 15% 15% 30%
Total. 50% 50% -
Evaluation method and criteria
Programming assignments 50%
Final examinations 50%
A score higher than 60 points passes
The 60 points is the level at which basic problems can be solved.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
The Others 各教員が指示する
Textbooks and reference materials
Textbooks are instructed by each class teacher
Reference materials are introduced in each class teacher
Prerequisites
Prepare your student number and password to login into a university computer
Office hours and How to contact professors for questions
  • Office hour is given by each teacher
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic problem-solving skills
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
About half of the classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
  • 12.RESPONSIBLE CONSUMPTION & PRODUCTION
Last modified : Tue Sep 12 04:05:14 JST 2023