Python and Machine-Learning for Asset Management with Alternative Data Sets
Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- المدة 21 ساعات hours
- الطبع بواسطة EDHEC Business School
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Offered by
عن
Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills.الوحدات
Theory
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3
Videos
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1
Readings
3
Videos
- Welcome Video
- What is consumption data?
- Geolocation and foot-traffic
1
Readings
- Material at your disposal
Lab sessions
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4
Videos
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2
Readings
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1
Lab
1
Labs
- Code and Data
4
Videos
- Lab session: Introduction to the Uber Dataset
- Lab session: Points of Interest
- Lab session: Mapping Data with Folium
- Lab session: Testing Seasonality
2
Readings
- Note about HeatMapWithTime
- Extra materials on consumption
Research/Application
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3
Videos
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2
Readings
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1
Discussion
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1
Assignment
1
Assignment
- Graded Quiz on Consumption
1
Discussions
- Data biases
3
Videos
- Application: Consumption data and earning surprises
- Application:Consumption-based proxies for private information and managers behavior
- Application: Additional applications of consumption data
2
Readings
- Additional resources on the interest of real-time corporate sales'measures
- Additional resources on Predicting Performance using Consumer Big Data
Theory
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4
Videos
4
Videos
- Introduction to the open web
- Introduction to textual analysis
- Processing text into vectors
- Normalizing textual data
Lab sessions
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3
Videos
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1
Readings
3
Videos
- Lab session: Introduction to Webscraping
- Lab session: Applied Text Data Processing
- Lab session: Company Distances and Industry Distances
1
Readings
- Extra materials on Textual Analysis for Financial Applications
Research/Application
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1
Videos
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1
Readings
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1
Discussion
-
1
Assignment
1
Assignment
- Graded Quiz on Textual Analysis for Financial Applications
1
Discussions
- Web scraping
1
Videos
- Application: applying similarity analysis on corporate filings to predict returns
1
Readings
- Additional resources on textual analysis for financial applications
Theory
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1
Videos
1
Videos
- Introduction to Corporate Filings
Lab sessions
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5
Videos
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4
Readings
5
Videos
- Lab session: Working with 10-K Data
- Lab session: Applications of TF-IDF
- Lab session: Risk Analysis
- Lab session: Working with 13-F Data
- Lab session: Comparing Holding Similarities
4
Readings
- Instructor's announcement
- Important note about 10-K lab
- Important message regarding 13F data
- Extra materials on Processing Corporate Filings
Research/Application
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2
Videos
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2
Readings
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1
Discussion
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1
Assignment
1
Assignment
- Graded Quiz on Processing Corporate Filings
1
Discussions
- 10-K and 13F filings
2
Videos
- Application: network centrality, competition links and stock returns
- Application: Using location data to measure home bias to predict returns
2
Readings
- Additional resources
- Additional resources on processing corporate fillings
Theory
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2
Videos
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2
Readings
2
Videos
- Introduction to Media Information
- Sentiment Analysis
2
Readings
- Additional resources
- Additional resources
Lab sessions
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4
Videos
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1
Readings
4
Videos
- Lab session: Twitter Dataset Introduction
- Lab session: Network Visualization
- Lab session: Replicating PageRank
- Lab session: Applied Sentiment Analysis
1
Readings
- Extra materials on Using Media-Derived Data
Research/Application
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1
Videos
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2
Readings
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1
Discussion
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1
Assignment
1
Assignment
- Graded Quiz on Using Media-Derived Data
1
Discussions
- Network analysis
1
Videos
- Application: Using media to predict financial market variables
2
Readings
- Additional resources on using media derived-data
- Data recap
Auto Summary
Explore Python and Machine-Learning for Asset Management with Alternative Data Sets, designed for professionals in Business & Management. Led by Coursera, this 1260-minute course delves into alternative data, offering cutting-edge research, practical lab sessions, and real-world applications. Ideal for aspiring data scientists in financial markets or those enhancing their analytics skills, with prerequisites in Python, Investment Theory, and Statistics. Available with a Starter subscription.
Gideon OZIK
Sean McOwen