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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 4번째 강좌:
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일정에 따라 마감일을 재설정합니다.
중급 단계

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

완료하는 데 약 19시간 필요
영어
자막: 영어

배울 내용

  • Learn what alternative data is and how it is used in financial market applications. 

  • Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

  • Perform data analysis of real-world alternative datasets using Python.

  • Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance

귀하가 습득할 기술

Advanced vizualisationBasics of consuption-based alternative dataText mining methodologiesWeb-scritpting tools
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 4개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

완료하는 데 약 19시간 필요
영어
자막: 영어

제공자:

EDHEC 경영대학원 로고

EDHEC 경영대학원

강의 계획 - 이 강좌에서 배울 내용

1

1

완료하는 데 5시간 필요

Consumption

완료하는 데 5시간 필요
10개 동영상 (총 74분), 5 개의 읽기 자료, 1 개의 테스트
10개의 동영상
What is consumption data?8m
Geolocation and foot-traffic5m
Lab session: Introduction to the Uber Dataset6m
Lab session: Points of Interest5m
Lab session: Mapping Data with Folium9m
Lab session: Testing Seasonality11m
Application: Consumption data and earning surprises7m
Application:Consumption-based proxies for private information and managers behavior7m
Application: Additional applications of consumption data7m
5개의 읽기 자료
Material at your disposal5m
Note about HeatMapWithTime2m
Extra materials on consumption1시간
Additional resources on the interest of real-time corporate sales'measures1시간
Additional resources on Predicting Performance using Consumer Big Data1시간
1개 연습문제
Graded Quiz on Consumption30m
2

2

완료하는 데 3시간 필요

Textual Analysis for Financial Applications

완료하는 데 3시간 필요
8개 동영상 (총 75분), 2 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Introduction to textual analysis3m
Processing text into vectors12m
Normalizing textual data5m
Lab session: Introduction to Webscraping11m
Lab session: Applied Text Data Processing11m
Lab session: Company Distances and Industry Distances15m
Application: applying similarity analysis on corporate filings to predict returns9m
2개의 읽기 자료
Extra materials on Textual Analysis for Financial Applications1시간 10분
Additional resources on textual analysis for financial applications1시간
1개 연습문제
Graded Quiz on Textual Analysis for Financial Applications
3

3

완료하는 데 4시간 필요

Processing Corporate Filings

완료하는 데 4시간 필요
8개 동영상 (총 69분), 4 개의 읽기 자료, 1 개의 테스트
8개의 동영상
Lab session: Working with 10-K Data7m
Lab session: Applications of TF-IDF11m
Lab session: Risk Analysis9m
Lab session: Working with 13-F Data10m
Lab session: Comparing Holding Similarities11m
Application: network centrality, competition links and stock returns8m
Application: Using location data to measure home bias to predict returns4m
4개의 읽기 자료
Instructor's announcement2m
Extra materials on Processing Corporate Filings30m
Additional resources30m
Additional resources on processing corporate fillings1시간 15분
1개 연습문제
Graded Quiz on Processing Corporate Filings
4

4

완료하는 데 7시간 필요

Using Media-Derived Data

완료하는 데 7시간 필요
7개 동영상 (총 62분), 5 개의 읽기 자료, 1 개의 테스트
7개의 동영상
Sentiment Analysis6m
Lab session: Twitter Dataset Introduction10m
Lab session: Network Visualization4m
Lab session: Replicating PageRank12m
Lab session: Applied Sentiment Analysis7m
Application: Using media to predict financial market variables10m
5개의 읽기 자료
Additional resources1시간
Additional resources1시간 15분
Extra materials on Using Media-Derived Data1시간 10분
Additional resources on using media derived-data2시간 30분
Data recap10m
1개 연습문제
Graded Quiz on Using Media-Derived Data

검토

PYTHON AND MACHINE-LEARNING FOR ASSET MANAGEMENT WITH ALTERNATIVE DATA SETS의 최상위 리뷰

모든 리뷰 보기

Investment Management with Python and Machine Learning 특화 과정 정보

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions....
Investment Management with Python and Machine Learning

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