到達目標
(ア) Students can explain the concept of big data.
(イ) Students can describe three characteristic features of big data.
(ウ) Students recognize the risks of data-driven decision-making.
(エ) Students can distinguish correlational analysis from causational analysis.
(オ) Students can explain a few effective examples of big data.
ルーブリック
| 理想的な到達レベルの目安 | 標準的な到達レベルの目安 | 未到達レベルの目安 |
| 評価項目(ア) | Students can explain the concept of big data. | Students understand the concept of big data. | Students don't understand the concept of big data. |
| 評価項目(イ) | Students can describe three characteristic features of big data. | Students understand three characteristic features of big data. | Students don't understand three characteristic features of big data. |
| 評価項目(ウ) | Students can explain the risks of data-driven decision makings. | Students understand the risks of data-driven decision makings. | Students don't recognize the risks of data-driven decision-makings. |
| 評価項目(エ) | Students can distinguish correlational analysis from causational analysis. | Students explain correlational analysis from the causational analysis. | Students don't distinguish correlational analysis from causational analysis. |
| 評価項目(オ) | Students can explain a few effective examples of big data. | Students explain a few effective examples of big data. | Students don't explain a few effective examples of big data. |
学科の到達目標項目との関係
学習・教育到達度目標 A4 コンピュータを利用した情報の保持・変換・伝達のための概念を理解し，説明できる．
JABEE d 当該分野において必要とされる専門的知識とそれらを応用する能力
本校教育目標 ① ものづくり能力
教育方法等
概要:
As engineers working in the century of knowledge, we should understand how some knowledge is created from daily data flow from society and may be used in essential decision-making. Big data is recent and needs to be a more well-defined concept, but it is a name for a series of processing ideas and methods for handling such a massive data flow. It differs from well-established processing methods in the last century, depends on the vast processing power of recent computers, and has significant benefits and severe risks to our society. This lecture intends to summarize the basis of big data for young engineering students. The lecture is based on the lecturer’s experience working as a developing engineer to learn the recent trends in analytics and information technology.
授業の進め方・方法:
(self-study & preparation) The students must read the assigned pages of the text before every lesson, write short summaries, and present them to the class.
注意点:
The students are expected to have receptive English skills of TOEIC 500 or higher because all the lectures, discussions, assignments, and tests will be done in English.
選択必修の種別・旧カリ科目名
None included in regulated technology.
授業の属性・履修上の区分
授業計画
|
|
週 |
授業内容 |
週ごとの到達目標 |
| 後期 |
| 3rdQ |
| 1週 |
Two examples of showing social effect from big data
(self-study and preparation) write a summary of three shifts of information analysis caused by big data |
Recognize the social effect of big data.
|
| 2週 |
The outline of three shifts of information analysis caused by big data
(self-study and preparation) write a summary of processing ALL data |
Understand the three shifts of information analysis.
|
| 3週 |
Processing ALL data instead of some samples
(self-study and preparation) write a summary of handling messy data |
Understand the difference between using all data and sampled data.
|
| 4週 |
Handling messy data
(self-study and preparation) write a summary of causality vs. correlation (part 1) |
Grasp the meaning of "messy" data.
|
| 5週 |
Leaving causality to satisfy with correlations
(self-study and preparation) write a summary of causality vs. correlation (part 2) |
Distinguish correlation from causality.
|
| 6週 |
Leaving causality to satisfy with correlations
(self-study and preparation) write a summary of turning data into valuable information |
Distinguish correlation from causality.
|
| 7週 |
Datafication: turning data into valuable information
(self-study and preparation) write a summary of Datafication |
Grasp the meaning of "Datafication".
|
| 8週 |
Datafication: turning data into valuable information
(self-study and preparation) write a summary of non-rivalrous option value of data (part 1) |
Grasp the meaning of "Datafication".
|
| 4thQ |
| 9週 |
Value: non-rivalrous option value of data
(self-study and preparation) write a summary of the non-rivalrous option value of data (part 2) |
Understand the option value of data.
|
| 10週 |
Value: non-rivalrous option value of data
(self-study and preparation) write a summary of the value chain (part 1) |
Understand the option value of data.
|
| 11週 |
Implications: data, skills, and ideas for the value chain
(self-study and preparation) write a summary of the value chain (part 2) |
Know the value chain of data analysis.
|
| 12週 |
Implications: data, skills, and ideas for the value chain
(self-study and preparation) write a summary of risks related to big data |
Know the value chain of data analysis.
|
| 13週 |
Risks: privacy, punishment based on the probability, dictatorship of data
(self-study and preparation) write a summary of controlling data |
Understand the risk of big data.
|
| 14週 |
Control: from privacy to accountability, the algorithms
(self-study and preparation) write a summary of the next issues of big data |
Know some ideas for controlling data analysis.
|
| 15週 |
Next: when data speaks, the more significant data |
Know the possible future of data analysis.
|
| 16週 |
|
|
モデルコアカリキュラムの学習内容と到達目標
| 分類 | 分野 | 学習内容 | 学習内容の到達目標 | 到達レベル | 授業週 |
評価割合
| 定期試験 | 課題 | 合計 |
| 総合評価割合 | 40 | 60 | 100 |
| 専門的能力 | 40 | 60 | 100 |