Syllabus Reference

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Course title Introduction to data science[A]
Responsible department Common to all Colleges
Term Spring Lecture classification Pre-registration
Credit(s) 2
The main day Saturday The main period 5th period
Campus On Demand
Numbering Code DTSC-1-JL-KGU
Lecture Style Lecture
Active Learning Type  B

Instructor
Full name
* Nakagawa Koichi

Course Theme and Description In this lecture, as an introductory subject of data science, you will learn what data science is, what kind of possibilities data science has in various fields, what kind of technology development it will lead to, and as an introduction to data science, you will learn the basics.
Course Objectives Understand the significance of studying data science by understanding the basic concepts of data science from the various information and data that overflows in the world with daily life as subjects in a data-driven society, and understanding the basic concepts related to data science. In addition, the goal is to learn appropriate data analysis methods according to the purpose of analysis, and to provide motivating learning that can lead to learning with specialized fields. Explain each analysis in data science and acquire the security and information ethics necessary for using information and data.
Method of feedback(test, report etc.) Outstanding answers will be posted on manaba with their names withheld.
Class Schedule
No.Lecture's / Class Theme and DescriptionLecture's / Class GoalHomework(Preparation)Homework(Review)
1Explain the content of the lecture based on the syllabus.
How data will change society (1) How far should business people be able to do data science?
Understand the practical level of data science required in the business field and understand the goal point we should aim for.I would like everyone to take on the challenge of data analysis, so I would like everyone to cooperate with the survey. Personal information will be completely confidential, so students are encouraged to complete this survey. Let's analyze everyone by ourselves!Find websites and articles that use data to present analyses, such as the one I showed you during the lecture. Google Images search may be useful. Then, while using the data, analyze it in your own way and formulate improvement measures (management measures, policies, improvement plans for back services) from there. The material can be anything, whether it's a game, an app, a manga, an anime, a drama, or a YouTuber!
2In Excel, find the "middle" of the dataIn Excel, you will be able to derive the average, median, and mode values, and make management measures based on them. Nothing in particular. Feel free to use "everyone's data" to show the mean, median, and mode of a few questions, and then ask yourself "what can I say from that?" For example, if you were to apply the results to your business, what would you do?
3How data will change society (2) What is AI?Learn about the brief history of AI, its technical structure, and how it is put to practical use. Nothing in particular.What would you like to do with AI? Please take into account "what can be achieved with AI" and the prerequisites required for that, and then come up with new ideas for utilizing AI.
4Know the "variance" of your data You will be able to create bar charts and pie charts. I understand that these graphs are not childish tools, but the most important methods of statistics. Nothing in particular.Freely select three questions from "Everyone's Data" and draw a bar chart or pie chart. From there, analyze what you can read. For example, if you were to use this in corporate management, what measures could you take?
5How Data Changes Society (3) What is the Fourth Industrial Revolution?Understand the concept of the Fourth Industrial Revolution and possible social changes Nothing in particularPlease introduce one of the news reports or actual products and services, and discuss what could happen in the Fourth Industrial Revolution around you.
6Know the "relationship" between two numbers Students will be able to understand the relationship between two types of variables, scatter plots and correlation, and apply them to management measures. Nothing in particularFrom "Everyone's Data", discover the relationship between two interesting variables in a scatter plot. From there, think about what you can say.
7How data changes society (4) Application example of AI: Visualization of success probability of kurafanunderstand the potential of AI Nothing in particularPlease consider "AI that is likely to be useful for earnings (acquisition of sales, customers, investment, etc.)" and "AI that is likely to be useful for reducing costs in society (reducing costs and resource consumption)."
8Fundamentals of Statistics (4) Surprisingly easy to understand regression analysis and become self-effective Be able to perform regression analysis yourself Nothing in particularUsing "everyone's data", feel free to perform regression analysis and think about what can be said from it. You can use the one you wrote the scatter plot on one more time.
9How data will change society (5) Let's use AIUsing AI services on the web, you will be able to think and execute things. Nothing in particularPlease feel free to submit the results of your analysis using one of the various AI services introduced in the lecture.
10What does "statistically significant" mean? Understand what "statistically significant" is, which is often seen in papers and is also important in today's data science practice. You will be able to do it yourself in Excel. Nothing in particularFeel free to use "everyone's data" to perform a t-test to prove the difference between the two groups. Based on that, please discuss what kind of improvement measures are effective.
11How Data Changes Society (6) DX and Data ScienceUnderstand what the "after digital" changes that are occurring at the same time as the data science and AI revolution are, and how they relate to each other. Nothing in particularPlease introduce one thing that you think "this is what after-digital is" and think about what kind of data utilization is behind it. Don't worry if it doesn't suit you (no points will be deducted). It is important to be able to imagine that behind the digital services you are using is data and its use.
12Practice: Strategic Theory of Probabilistic ThinkingAcquire the concept of conditional probability, which is effective in the probability analysis of occurrence of things, and be able to make measures using it. Nothing in particular From "everyone's data", select variables that can be analyzed as percentages and probabilities, and create a model of conditional probabilities. Then, please think about what you can do to improve the probability (management measures, policies, service improvement plans, etc.).
13How data will change society (7) Human resources needed in the Fourth Industrial RevolutionUnderstand the image of human resources required in the future data era and the organization and strategy required for DX Nothing in particularIn anticipation of the coming era, if you were to work in a job that involves data, what kind of person would you be? Think about what skills you need to acquire to achieve this.
14Sampling Be able to conduct surveys properly Nothing in particular If you have a budget of 50,000 yen and decide to conduct a questionnaire using an Internet research company about "what you want to know", please design the target of the questionnaire and specific question items. (10 yen per question per person).
Grade evaluation method and criteria (Grades are given according to the report assignments required to be submitted in each lecture.) Since the lecture will be held 14 times, the maximum score will be 7 points for each lecture. Depending on the quality of the report, it will be evaluated on a three-point scale of 3, 5, and 7 points. Points earned will be posted every time. Reports may be submitted late, but there will be a slight deduction (-1 points).
Related Courses 「Exercises in data science」
Explanation of teaching methods No mark: face-to-face courses ★: online courses
Preparation Hours 2
Review Hours 2
Practical experiences of Instructor He has been working part-time for three years (2018-2020) and in-house business for two years (2021-2022) developing systems using data analytics and machine learning. Examples of achievements: SMTB "Startup Survey", a system for estimating crowdfunding success probability and acquisition amount using machine learning, and calculation of the optimal feeding amount in aquaculture using machine learning. 
How to utilize practical experiences in class Data science is often thought of as performing advanced machine learning and complex statistical processing, but in fact, it is essential to know the field and reality well, and to properly grasp it as elementary statistics (average, variance, and correlation). Most of the work is up to the point of providing the above statistics, and if you can do this, it will be more than enough preparation for general business work, and you will have the foundation as a data scientist. Based on this point, this course thoroughly solidifies the foundation.
Rubric https://univ.kanto-gakuin.ac.jp/education/center-for-research-and-development-of-higher-education.html#6
Numbering Code Detail ・ Diploma Policy https://univ.kanto-gakuin.ac.jp/education/syllabus.html
Textbook
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Reference book(s) | materials
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