Python and Machine-Learning for Asset Management with Alternative Data Sets
- 4.4
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Course Summary
This course focuses on using alternative data to improve asset management through machine learning. The course covers topics such as data preprocessing, feature engineering, and model selection.Key Learning Points
- Learn how to use alternative data to improve asset management
- Understand the basics of machine learning
- Develop skills in data preprocessing, feature engineering, and model selection
Job Positions & Salaries of people who have taken this course might have
- USA: $110,000 - $140,000
- India: ₹8,00,000 - ₹12,00,000
- Spain: €30,000 - €50,000
- USA: $110,000 - $140,000
- India: ₹8,00,000 - ₹12,00,000
- Spain: €30,000 - €50,000
- USA: $80,000 - $120,000
- India: ₹6,00,000 - ₹10,00,000
- Spain: €25,000 - €40,000
- USA: $110,000 - $140,000
- India: ₹8,00,000 - ₹12,00,000
- Spain: €30,000 - €50,000
- USA: $80,000 - $120,000
- India: ₹6,00,000 - ₹10,00,000
- Spain: €25,000 - €40,000
- USA: $100,000 - $150,000
- India: ₹10,00,000 - ₹20,00,000
- Spain: €40,000 - €60,000
Related Topics for further study
Learning Outcomes
- Develop skills in using alternative data for asset management
- Understand the basics of machine learning
- Learn how to preprocess data, engineer features, and select models
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming
- Familiarity with statistics and linear algebra
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Data Science and Machine Learning Bootcamp
- Applied Data Science with Python
- Machine Learning for Trading
Related Education Paths
Notable People in This Field
- Kirk Borne
- Hilary Mason
Related Books
Description
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.
Knowledge
- Learn what alternative data is and how it is used in financial market applications.Â
- 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
Outline
- Consumption
- Welcome Video
- What is consumption data?
- Geolocation and foot-traffic
- Lab session: Introduction to the Uber Dataset
- Lab session: Points of Interest
- Lab session: Mapping Data with Folium
- Lab session: Testing Seasonality
- Application: Consumption data and earning surprises
- Application:Consumption-based proxies for private information and managers behavior
- Application: Additional applications of consumption data
- Material at your disposal
- Note about HeatMapWithTime
- Extra materials on consumption
- Additional resources on the interest of real-time corporate sales'measures
- Additional resources on Predicting Performance using Consumer Big Data
- Graded Quiz on Consumption
- Textual Analysis for Financial Applications
- Introduction to the open web
- Introduction to textual analysis
- Processing text into vectors
- Normalizing textual data
- Lab session: Introduction to Webscraping
- Lab session: Applied Text Data Processing
- Lab session: Company Distances and Industry Distances
- Application: applying similarity analysis on corporate filings to predict returns
- Extra materials on Textual Analysis for Financial Applications
- Additional resources on textual analysis for financial applications
- Processing Corporate Filings
- Introduction to Corporate Filings
- 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
- Application: network centrality, competition links and stock returns
- Application: Using location data to measure home bias to predict returns
- Instructor's announcement
- Extra materials on Processing Corporate Filings
- Additional resources
- Additional resources on processing corporate fillings
- Using Media-Derived Data
- Introduction to Media Information
- Sentiment Analysis
- Lab session: Twitter Dataset Introduction
- Lab session: Network Visualization
- Lab session: Replicating PageRank
- Lab session: Applied Sentiment Analysis
- Application: Using media to predict financial market variables
- Additional resources
- Additional resources
- Extra materials on Using Media-Derived Data
- Additional resources on using media derived-data
- Data recap
Summary of User Reviews
Learn how to use machine learning in asset management with alternative data in this course. Users have praised the course for its depth and practical applications.Key Aspect Users Liked About This Course
practical applicationsPros from User Reviews
- In-depth coverage of machine learning in asset management
- Real-world examples and case studies
- Practical applications of machine learning in finance
- Expert instructors with industry experience
Cons from User Reviews
- Some users felt that the course was too technical
- Lack of interaction with instructors and peers
- Requires prior knowledge of finance and programming
- Pacing may be too fast for some learners
Gideon OZIK
4.4 Raiting
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