Course title
117501502
Data Science

 YASUMURA Yoshiaki
Course description
This course will provide an understanding of techniques for extracting meaning from data by studying examples of data science being applied in the real world. Students will learn basic programming techniques in the Python programming language. Students will learn basic techniques of statistics and machine learning, and deepen their understanding by creating practical programs.
Purpose of class
The objective of this course is to provide applied basic skills in data sciences. Students will learn programming techniques in Python and acquire skills in implementing statistical and machine learning methods.
Goals and objectives
  1. To be able to explain how data science can be used in the real world.
  2. To be able to program in Python with an understanding of basic algorithms.
  3. To be able to program the statistics methods.
  4. To be able to program the machine learning methods.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Assignments Examinations Total.
1. 10% 10% 20%
2. 15% 15% 30%
3. 10% 10% 20%
4. 15% 15% 30%
5. 0% 0% 0%
Total. 50% 50% -
Evaluation method and criteria
The course grade will be determined according to exams and classwork assignments.
Relative weights assigned for full credit are 50% for exams and 50% for assignment.
Over 60% scale awards a credit.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Introduction
Overview of Data science
Basic Python programming 		Programming assignment about basic grammer 240minutes
2. Basic Program
Iteration 		Programming assignment about iteration 240minutes
3. Programming
Function 		Programming assignment about function 240minutes
4. Programming

Object oriented programming 		Programming assignment about object oriented program 240minutes
5. Data representation and Visualization
Data representation
Data visualization 		Programming assignment about data visualization 240minutes
6. Computing Statistics

Hypothesis tests 		Programming assignment about statistics 240minutes
7. Midterm examination and its explanation Review about midterm examination 200minutes
8. What is Machine Learning?
　　　Fundamentals of Machine Learning
　　　Nearest Neighbor Method
　　　Evaluation of Learning Results 		Programming assignment about machine learning 240minutes
9. Regression Analysis
　　　Linear Regression　　　Overfitting 		Programming assignment about regression 240minutes
10. Clustering Analysis
　　　What is Clustering?
　　　k-means method 		Programming assignment about clustering 240minutes
11. Text Analysis
　　　Natural Language Processing
　　　Spam Classification 		Programming assignment about natural language processing 240minutes
12. Neural Networks
　　　Fundamentals of Neural Networks
　　　Back Propagation Method 		Programming assignment about neural network 240minutes
13. Deep Learning
　　　
Convolutional Neural Networks (CNN)
Image Classification 		Programming assignment about deep learning 240minutes
14. Final examination and its explanation Review about final examination 200minutes
Total. - - 3280minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
Instruction will be given in class.
Prerequisites
Build an integrated development environment for python programs on your own computer
Office hours and How to contact professors for questions
  • Wednesday lunch break
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
Most 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
Last modified : Sat Jun 29 04:09:02 JST 2024