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Python and Machine-Learning for Asset Management with Alternative Data Sets

Approx. 19 hours to complete

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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

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

Intermediate

Course 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.
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 applications

Pros 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

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