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This course aims to cultivate handling data with appropriate methods in literacy level. Specifically, this course will introduce descriptive statistics to summarize characteristics of data.
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To operate software and computers for handling data.
To conduct data visualization with appropriate methods.
To summarize characteristics of data with calculating descriptive statistics via MS Excel.
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○ A: Basic knowledge and understanding of various sciences subjects.
◎ B: The ability to think logically and creatively.
△ C: The ability to communicate effectively.
○ D: Gained a sense of social ethics and an international perspective.○
(◎ = Very important, 〇 = Important, △ = Subject to evaluation)
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Data are stored without knowing it and something are decided by these data. The first step to data science is to learn characteristics of data and methods for processing. This course aims to acquire basic and general techniques for data science literacy though on-demand education using e-learning movies and web tests.
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This course includes lecture and exercise. Students learn them using movies and web tests on the LMS. One class is completed by means of watching the movies and answering the web tests within the designated period. For Qs and As related to classes, both Web service on LMS and in person discussion with teachers are available.
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1 Introduction to this course and settings for your environment
2 Information ethics, how to use Gunma Univ. library
3 Mechanism of computers
4 Information networks and services
5 Introduction of MS Excel I
6 Introduction of MS Excel II
7 Introduction of MS Excel III
8 Introduction to data science (organization of statistical data)
9 Visualization with graphs
10 Statistic measures of position
11 Statistic measures of dispersion
12 Relationship between data in multi-dimension through cross-tabulation
13 Causation and correlation, data preprocessing for real data
14 Distribution of the data for final exercise and its explanation
15 Submission of final exercise
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The learners can take e-learning exercises repeatedly within these time limits. The learners may take each web test only once during designated period.
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This course evaluates the learner as below;
・Web tests for each class 70% A,B, C, D
・final exercise 30% A,B.
In addition, receiving an e-learning for information ethics and getting a passing score of its web test are required. Submitting a report of the final exercise is also required.
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"Data Science" is considered be a science course and difficult. However, problem solving and decision making based on data are becoming necessary in all fields. In this course, we try to explain the concepts of applied fundamental in a simple manner, which is a further development of the "Data Science" course. This course will be useful for students in their future professional fields.
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Statistics, Descriptive Statistics, Excel, Visualization
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Mathematics in high school
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Python for data science, Data science applications, Basics of Data Science and Machine Learning
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