Course title
117501401
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 of the data science 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 methods
  4. To be able to visualize the data and analysis results
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.
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
Prepare to take this course in advance 50minutes
Review the material related to "Basics of Data Science" 50minutes
Submit assignment about "Basics of Data Science" 100minutes
2. Basics of Python Programming
A programming environment for Python
Variable
Assignment and arithmetic operators
Watch the video about "Basics of Python Programming" in advance 50minutes
Review the material related to "Basics of Python Programming" 50minutes
Submit programming assignment about "Basics of Python Programming" 100minutes
3. Control Statement 1
Conditional branch ( if, elif, and else statements )
Comparison and logical operators
Watch the video about "Condition branch" in advance 50minutes
Review the material related to "Condition branch" 50minutes
Submit programming assignment about "Condition branch" 100minutes
4. Control Statement 2
Iteration statements( for and while )
List and tuple
Watch the video about "Iteration statements" in advance 50minutes
Review the material related to "Iteration statements" 50minutes
Submit programming assignment about "Iteration statements" 100minutes
5. Function
Usage of module
Create user functions
Watch the video about "Function" in advance 50minutes
Review the material related to "Function" 50minutes
Submit programming assignment about "Function" 100minutes
6. Class and Object
Create your own classes and objects
Classes and objects of external modules
Watch the video about "Class" in advance 50minutes
Review the material related to "Class" 50minutes
Submit programming assignment about "Class" 100minutes
7. Data Expression
Various data representation
Control data in a program
Watch the video about "Data Expression" in advance 50minutes
Review the material related to "Data Expression" 50minutes
Submit programming assignment about "Data Expression" 100minutes
8. Basics of AI
AI history
AI ethics
Review the material related to "AI" 50minutes
Submit assignment about "AI" 100minutes
9. Machine Learning
Basics of machine learning
Nearest neighbor method
Watch the video about "Machine Learning" in advance 50minutes
Review the material related to "Machine Learning" 50minutes
Submit programming assignment about "Machine Learning" 100minutes
10. Regression Analysis
Single regression analysis
Multiple regression analysis
Watch the video about "Regression Analysis" in advance 50minutes
Review the material related to "Regression Analysis" 50minutes
Submit programming assignment about "Regression Analysis" 100minutes
11. Cluster Analysis
k-means method
Watch the video about "Cluster Analysis" in advance 50minutes
Review the material related to "Cluster Analysis" 50minutes
Submit programming assignment about "Cluster Analysis" 100minutes
12. Neural Network
Artificial neural network
Backpropagation method
Watch the video about "Neural Network" in advance 50minutes
Review the material related to "Neural Network" 50minutes
Submit programming assignment about "Neural Network" 100minutes
13. Deep Learning
Deep neural network
Convolutional neural network
Watch the video about "Deep Learning" in advance 50minutes
Review the material related to "Deep Learning" 50minutes
Submit programming assignment about "Deep Learning" 100minutes
14. Final examination and explanation Review of final examination 150minutes
Total. - - 2700minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
Textbooks are instructed by each class teacher
Reference materials are distributed by each class teacher
Prerequisites
Prepare your student number and password to log into a university computer
Students must acquire the minimum skills to input characters, such as the alphabet and symbols, into a computer in advance.
Prepare the programming environment on your computer. (Each teacher will give the instructions via the ScombZ in advance.)
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 17 18:07:00 JST 2024