미국대학원_과제_빅데이터, 애널리틱스, 데이터과학의 현재와 미래_Columbia Univ_Current and Future State of Analytics, Big Data, Data Science
- 최초 등록일
- 2020.12.26
- 최종 저작일
- 2019.12
- 5페이지/
MS 워드
- 가격 2,000원
소개글
"미국대학원_과제_빅데이터, 애널리틱스, 데이터과학의 현재와 미래_Columbia Univ_Current and Future State of Analytics, Big Data, Data Science"에 대한 내용입니다.
목차
Ⅰ. Current and Future Trends
1. Trends in Analytical Techniques
2. Trends in People in Analytics
3. Trends in Analytics in Organizations
Ⅱ. The Key Trends & Strategy
1. People in Analytics
2. Analytics in Organizations
본문내용
1. Trends in Analytical Techniques
The most popular word that describes today’s analytics is machine learning. In almost every analytical application that companies develop today involves in machine learning techniques. For example, decision trees predict which employee is likely to leave or which customer will stop using a cell phone service. In customer recommendation systems, KNN (K Nearest Neighbors) suggests a series of products a particular customer will love to browse by making predictions based on the customer’s search history, ratings, and the shopping pattern of a similar customer. The machine learning techniques also enable the companies to predict which customer segment will most likely to buy a particular product, dramatically increasing sales. While there are many other techniques that are applied to the industries for various purposes, machine learning has been around for a while; it is derived from AI (Artificial Intelligence) which existed since approximately 1950’s. Back then, the AI needed detailed instructions for every action the machine was supposed to make.
참고 자료
Thomas H. Davenport (2014). Big Data @ Work.
Alistair Croll (2015). Data: Emerging Trends and Technologies.
Margaret Rouse (2014). Self-Service Analytics
http://searchbusinessanalytics.techtarget.com/definition/self-service-analytics
