Data Sience

Course Information

College	Anan College	Year	2024
Course Title	Data Sience
Course Code	1793103	Course Category	Specialized / Compulsory
Class Format	Lecture	Credits	School Credit: 2
Department	Course of Information Engineering	Student Grade	3rd
Term	Year-round	Classes per Week	前期:2 後期:0
Textbook and/or Teaching Materials	"Learning Data Science with Python" by Masahiro Yoshida, published by Gijutsu-Hyoron Co., Ltd.
Instructor	Ota Kengo

Course Objectives

1. Can explain the overview of data science and AI technology.
2. Can acquire and process the data needed for utilizing data science and AI technology.
3. Can visualize and analyze the data necessary for the application of data science and AI technology.

Rubric

	Ideal Level	Standard Level	Unacceptable Level
Achievement 1	Can explain the overview and applications of data science and AI technology.	Can explain the overview of data science and AI technology.	Can understand the overview of data science and AI technology.
Achievement 2	Can acquire and process the data necessary for the utilization of data science and AI technology.	Can acquire or process the data necessary for the utilization of data science and AI technology.	Can understand the acquisition or processing of data necessary for the utilization of data science and AI technology.
Achievement 3	Can perform the visualization and analysis of data necessary for the utilization of data science and AI technology.	Can perform the visualization or analysis of data necessary for the utilization of data science and AI technology.	Can understand the visualization or analysis of data necessary for the utilization of data science and AI technology.

Assigned Department Objectives

学習・教育到達度目標 B-4 See

Teaching Method

Outline:

Learn the fundamentals of data science in a practical manner, with exercises using Python.

Style:

In the first semester, follow the textbook, and in the second semester, proceed according to the distributed materials.
As needed, deepen understanding through a combination of lecture-style explanations and exercises with Python programming.

Notice:

Characteristics of Class / Division in Learning

Active Learning	Aided by ICT	Applicable to Remote Class	Instructor Professionally Experienced

Course Plan

			Theme	Goals
1st Semester
	1st Quarter
		1st	Introduction to Data Science	Can explain the overview of data science.
		2nd	Python Programming for Data Science	Can perform basic Python programming for data science.
		3rd	Data Collection for Data Science	Can acquire data necessary for data science.
		4th	Data Preprocessing for Data Science	Can preprocess data necessary for data science.
		5th	Probability and Statistics for Data Science	Can explain probability and statistics for data science.
		6th	Data Science with Statistical Testing	Can explain statistical testing for data science.
		7th	Data Science with A/B Testing	Can explain A/B testing for data science.
		8th	Algorithms for Data Science	Can explain basic algorithms for data science.
	2nd Quarter
		9th	【Midterm Exam for the First Semester】
		10th	Data Science with Regression AI	Can perform data prediction using regression AI.
		11th	Data Science with Classification AI	Can perform data analysis using classification AI.
		12th	Data Science with Clustering AI	Can perform data analysis using clustering AI.
		13th	Data Science with Recommendation AI	Can explain recommendation AI.
		14th	Data Science with Time Series Analysis AI and Natural Language Processing AI	Can explain analysis methods for time series and textual data.
		15th	Data Science with Image Analysis AI	Can explain analysis methods for image data.
		16th	【Final Exam for the First Semester, Return of Answer Sheets】
2nd Semester
	3rd Quarter
		1st	Python Programming in the JupyterLab Environment	Can perform Python programming necessary for data science in a JupyterLab environment.
		2nd	Python Programming in the JupyterLab Environment	Can perform Python programming necessary for data science in a JupyterLab environment.
		3rd	Data Processing with NumPy	Can perform various data processing tasks using NumPy.
		4th	Data Processing with NumPy	Can perform various data processing tasks using NumPy.
		5th	Data Processing with NumPy	Can perform various data processing tasks using NumPy.
		6th	Data Processing and Analysis with pandas	Can process and analyze data using pandas.
		7th	Data Processing and Analysis with pandas	Can process and analyze data using pandas.
		8th	Data Processing and Analysis with pandas	Can process and analyze data using pandas.
	4th Quarter
		9th	【Midterm Exam for the Second Semester】
		10th	Data Visualization with Matplotlib	Can visualize data using Matplotlib.
		11th	Data Visualization with Matplotlib	Can visualize data using Matplotlib.
		12th	Data Visualization with Matplotlib	Can visualize data using Matplotlib.
		13th	Applied Data Science	Can explain applications of data science.
		14th	Applied Data Science	Can explain applications of data science.
		15th	Applied Data Science	Can explain applications of data science.
		16th	【Final Exam for the Academic Year, Return of Answer Sheets】

Evaluation Method and Weight (%)

	Examination	Presentation	Mutual Evaluations between students	Behavior	Portfolio	Other	Total
Subtotal	60	0	0	0	40	0	100
Basic Proficiency	30	0	0	0	20	0	50
Specialized Proficiency	30	0	0	0	20	0	50
Cross Area Proficiency	0	0	0	0	0	0	0