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
| 最低限の到達レベルの目安(可) | | |
| 評価項目(ア) | Students can explain the concept of big data | | |
| 評価項目(イ) | Students can describe three characteristic features of big data | | |
| 評価項目(ウ) | Students 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 C2-4 「情報と計測・制御」に関する専門知識の修得
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.
授業の進め方・方法:
注意点:
The students are expected to have reseptive English skills of TOEIC 500 or higher, because all the lectures, discussions, asignments, and tests are to be done in English. The students are also required to read the assigned pages of the text before every lesson, write short summaries and present them to the class.
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 |
|
| 2週 |
The outline of three shifts of information analysis caused by big data |
|
| 3週 |
Processing ALL data instead of some samples |
|
| 4週 |
Handling messy data |
|
| 5週 |
Leaving causality to satisfying with correlations |
|
| 6週 |
Leaving causality to satisfying with correlations |
|
| 7週 |
Datafication: turning data into valuable information |
|
| 8週 |
Datafication: turning data into valuable information |
|
| 4thQ |
| 9週 |
Value: non-rivalrous option value of data |
|
| 10週 |
Value: non-rivalrous option value of data |
|
| 11週 |
Implications: data, skills, and ideas for the value chain |
|
| 12週 |
Implications: data, skills, and ideas for the value chain |
|
| 13週 |
Risks：privacy, punishment based on the probability, dictatorship of data |
|
| 14週 |
Control: from privacy to accountability, the algorithmist |
|
| 15週 |
Next: when data speaks, the bigger data |
|
| 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 |