About this Course

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Intermediate Level

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

Approx. 19 hours to complete
English
Subtitles: English

Skills you will gain

Advanced vizualisationBasics of consuption-based alternative dataText mining methodologiesWeb-scritpting tools
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

Approx. 19 hours to complete
English
Subtitles: English

Offered by

Placeholder

EDHEC Business School

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Consumption

5 hours to complete
10 videos (Total 74 min), 5 readings, 1 quiz
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 readings
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 practice exercise
Graded Quiz on Consumption30m
Week
2

Week 2

3 hours to complete

Textual Analysis for Financial Applications

3 hours to complete
8 videos (Total 75 min), 2 readings, 1 quiz
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 readings
Extra materials on Textual Analysis for Financial Applications1h 10m
Additional resources on textual analysis for financial applications1h
1 practice exercise
Graded Quiz on Textual Analysis for Financial Applications
Week
3

Week 3

4 hours to complete

Processing Corporate Filings

4 hours to complete
8 videos (Total 69 min), 4 readings, 1 quiz
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 readings
Instructor's announcement2m
Extra materials on Processing Corporate Filings30m
Additional resources30m
Additional resources on processing corporate fillings1h 15m
1 practice exercise
Graded Quiz on Processing Corporate Filings
Week
4

Week 4

7 hours to complete

Using Media-Derived Data

7 hours to complete
7 videos (Total 62 min), 5 readings, 1 quiz
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
5 readings
Additional resources1h
Additional resources1h 15m
Extra materials on Using Media-Derived Data1h 10m
Additional resources on using media derived-data2h 30m
Data recap10m
1 practice exercise
Graded Quiz on Using Media-Derived Data

Reviews

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About the Investment Management with Python and Machine Learning Specialization

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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