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EMERGENCE OF DATA DRIVEN SOCIETY AND STRATEGY (ADVANCED)

NEWS


TitleEMERGENCE OF DATA DRIVEN SOCIETY AND STRATEGY (ADVANCED) [ Syllabus ]

Outline of this course
Whether you are going to do some kind of business or go into the company, data-driven analytical problem-solving skills and hands-on understanding of data utilization are essential in the days ahead.
This course aims to develop minimum data literacy to survive in this data-driven age for the students with a practical basic understanding of analytical thinking and data literacy (skills covered in the “Data-Driven” basic class), on the assumption that they do not have much experience in data analysis and utilization.
In this course, we will cover introductions to data preparation, data visualization, and machine learning including deep learning.
Classes will be conducted interactively as much as possible. We will invest a considerable amount of time for each homework review and answers to the questions received in a weekly questionnaire.
★ This course will be delivered to a student who completed the introductory Data-Driven class successfully (just audited students are not allowed).
★ This is not a course to nurture experts in information science and big data processing
- Natural language processing
- Image processing
- Machine learning
- Data infrastructure construction
- Real time processing, etc.
■Expected students
Those who really want to make a change by analyzing the data and utilizing the data rather than information science nerds.
People who want to understand how data and analysis are delivering the values in this world and who want to acquire basic skills on them.
People who want to get the feel of how the data can be used for general decision making.
People who have tried various analyzes so far, but who do not get the point of value creation using data.


Faculty Kazuto Ataka
Term2019 Fall
Level Undergraduate


Inquiry - Inquiry about this course


Lecture Video & Materials
Click the lecture title to see lecture materials and video
#012019/09/25 概論
- 講義資料.pdf
本講義の立ち位置を説明


#022019/10/02 調査設計初級(定性 & 属性だし)
- 課題用データ(zip)
- 講義資料.pdf
調査設計のおさらい
属性だし入門


#032019/10/09 セグメンテーション& value proposition
- 講義用データ.xlsx
- 講義用ファイル.pdf(アンケート内容)
- 講義資料.pdf
value propositionとは


#042019/10/16 形態素解析と共起ネットワーク
- R演習環境 (URL)
- 講義資料.pdf
形態素解析入門


#052019/10/23 大量データ成型
データ整形入門

★JavaScript補講

#062019/10/30 大量データ可視化入門
大量データ可視化ライブラリ

★Python補講

#072019/11/06 Programming & Python 入門
文法
library の使い方

#082019/11/27 機械学習初歩(分類、予測など)
基礎概念、、、教師アリ、ナシ
Liblinear(線形ライブラリ)python

#092019/12/04 機械学習初歩2

#102019/12/11 深層学習入門1(クラスの延長として補講を実施します)
Python, Numpy/Scipy

#112019/12/18 深層学習入門2(クラスの延長として補講を実施します)
同上

#122019/12/25 深層学習入門3(クラスの延長として補講を実施します)
同上

#132020/01/08 深層学習入門4(クラスの延長として補講を実施します)
同上

#142020/01/15 セキュリティとプライバシー
ビッグデータとリテラシ


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