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 makings
(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 recognize the risks of data-driven decision makings. | Students don't recognize the risks of data-driven decision makings. |
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 dataflow from the society, and may be used in important decision makings. Big data is a recent and not well-defined concept but a naming of a series of processing ideas and methods handling such huge dataflow. It is different from well-established processing methods in the last century, depends on the huge processing power on recent computers, and has large benefits along with serious 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 worked as developing engineer to learn the recent trend of analytics and information technology.
授業の進め方・方法:
(self-study & preparation) The students are required to 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 are to be done in English.
選択必修の種別・旧カリ科目名
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 & preparation) write summary of three shifts of information analysis caused by big data |
recognize the social effect from big data
|
| 2週 |
The outline of three shifts of information analysis caused by big data
(self-study & preparation) write summary of processing ALL data |
understand the three shifts of information analysis
|
| 3週 |
Processing ALL data instead of some samples
(self-study & preparation) write summary of handling messy data |
understand the difference of using ALL data instead of sampled data
|
| 4週 |
Handling messy data
(self-study & preparation) write summary of causality vs. correlation (part 1) |
grasp the meaning of "messy" data
|
| 5週 |
Leaving causality to satisfying with correlations
(self-study & preparation) write summary of causality vs. correlation (part 2) |
distinguish correlation from causality
|
| 6週 |
Leaving causality to satisfying with correlations
(self-study & preparation) write summary of turning data into valuable information |
distinguish correlation from causality
|
| 7週 |
Datafication: turning data into valuable information
(self-study & preparation) write summary of Datafication |
grasp the meaning of "Datafication"
|
| 8週 |
Datafication: turning data into valuable information
(self-study & preparation) write 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 & preparation) write summary of 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 & preparation) write summary of value chain (part 1) |
understand the option value of data
|
| 11週 |
Implications: data, skills, and ideas for the value chain
(self-study & preparation) write summary of value chain (part 2) |
know the value chain of data analysis
|
| 12週 |
Implications: data, skills, and ideas for the value chain
(self-study & preparation) write 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 & preparation) write summary of controlling data |
understand the risk of big data
|
| 14週 |
Control: from privacy to accountability, the algorithmist
(self-study & preparation) write summary of next issues of big data |
know some ideas of controlling data analysis
|
| 15週 |
Next: when data speaks, the bigger 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 |