Acerca de este Curso

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100 % en línea

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Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

Aprox. 11 horas para completar

Sugerido: 4 weeks of study, 2 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • 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

Habilidades que obtendrás

Advanced vizualisationBasics of consuption-based alternative dataText mining methodologiesWeb-scritpting tools

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

Aprox. 11 horas para completar

Sugerido: 4 weeks of study, 2 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

ofrecido por

Logotipo de Escuela de Negocios EDHEC

Escuela de Negocios EDHEC

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

5 horas para completar

Consumption

5 horas para completar
10 videos (Total 74 minutos), 5 lecturas, 1 cuestionario
10 videos
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
5 lecturas
Material at your disposal5m
Note about HeatMapWithTime2m
Extra materials on consumption1h
Additional resources on the interest of real-time corporate sales'measures1h
Additional resources on Predicting Performance using Consumer Big Data1h
1 ejercicio de práctica
Graded Quiz on Consumption30m
Semana
2

Semana 2

3 horas para completar

Textual Analysis for Financial Applications

3 horas para completar
8 videos (Total 75 minutos), 2 lecturas, 1 cuestionario
8 videos
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
2 lecturas
Extra materials on Textual Analysis for Financial Applications1h 10m
Additional resources on textual analysis for financial applications1h
1 ejercicio de práctica
Graded Quiz on Textual Analysis for Financial Applications
Semana
3

Semana 3

4 horas para completar

Processing Corporate Filings

4 horas para completar
8 videos (Total 69 minutos), 4 lecturas, 1 cuestionario
8 videos
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
4 lecturas
Instructor's announcement2m
Extra materials on Processing Corporate Filings30m
Additional resources30m
Additional resources on processing corporate fillings1h 15m
1 ejercicio de práctica
Graded Quiz on Processing Corporate Filings
Semana
4

Semana 4

7 horas para completar

Using Media-Derived Data

7 horas para completar
7 videos (Total 62 minutos), 4 lecturas, 1 cuestionario
7 videos
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
4 lecturas
Additional resources1h
Additional resources1h 15m
Extra materials on Using Media-Derived Data1h 10m
Additional resources on using media derived-data2h 30m
1 ejercicio de práctica
Graded Quiz on Using Media-Derived Data

Revisiones

Principales revisiones sobre PYTHON AND MACHINE-LEARNING FOR ASSET MANAGEMENT WITH ALTERNATIVE DATA SETS
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Acerca de Programa especializado Investment Management with Python and Machine Learning

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

Preguntas Frecuentes

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  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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