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

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 236 ratings
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Gideon OZIK and Sean McOwen

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.

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What's inside

Syllabus

Consumption
The consumption module introduces students to the basics of consumption-based alternative data. By aggregating online and offline consumer purchase activity and behavioral datasets including geolocation data (e.g., cell locations, satellite imagery etc.), transaction data (e.g., credit card transaction logs and point of sale data), as well as consumer interaction with brands and products on social media, researchers can learn about company performance ahead of official company earning announcements. Such information may be extremely useful and can provide investment and risk management advantages. This module reviews the theoretical aspects of various consumption datasets, and provides practical demonstrations of relevant data analytics.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides practical examples and lab sessions in addition to theory
Taught by instructors recognized for their work in alternative data
Emphasizes practical application of alternative data in financial markets
Focuses on cutting-edge research and technology in financial data
Requires Python programming, investment theory, and statistics as prerequisites

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Reviews summary

Practical ml for asset management with python

According to learners, this course offers a solid introduction and practical application of Python and Machine Learning for asset management using alternative data. Many students appreciate the hands-on labs and practical examples, finding them highly relevant for real-world financial applications. The coverage of specific alternative data types like textual analysis and working with corporate filings (10-K/13-F) is frequently highlighted as a strength. Some reviewers mention the course is quite challenging and strongly emphasize the need for a strong prerequisite background in Python, investment theory, and statistics, noting that insufficient preparation can make the material difficult. While a few learners reported occasional issues with the lab environment, the course is largely viewed as a highly valuable resource for those aiming to apply data science to finance.
Covers key alternative data types effectively.
"The section on processing 10-K and 13-F filings was particularly useful and directly applicable to financial analysis."
"Provides a good overview of text analysis methods specifically for financial applications, which is a valuable skill."
"The course successfully introduces the core concepts around alternative data and their relevance in asset management."
Apply theory with hands-on code examples.
"The hands-on coding and projects are the strongest part of the course for me, allowing immediate application of learned concepts."
"Practical lab sessions were essential to grasp the concepts and see how they work with real-world financial data."
"I liked the practical examples on working with alternative datasets, especially the web scraping and text analysis parts."
Some reported technical problems.
"Experienced some bugs and issues with the provided lab notebooks not running correctly out of the box."
"Had a bit of trouble setting up or getting the lab environment to work smoothly at times."
"A few code examples in the labs required minor modifications to run properly."
Moves quickly; could be deeper in areas.
"Felt a bit rushed in certain sections, especially the machine learning parts, given the complexity."
"Could use more in-depth coverage on specific ML techniques or optimization strategies relevant to finance."
"The course assumes a high level of prior knowledge and moves very fast through the topics presented."
Requires strong background knowledge.
"Make sure you are very comfortable with Python programming, statistics, and investment theory before starting this course."
"I found it quite challenging without a solid investment background. The course moves fast assuming you know the basics."
"This is definitely not a beginner course. The prerequisites listed are crucial for keeping up with the material."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python and Machine-Learning for Asset Management with Alternative Data Sets with these activities:
Form a Study Group with Classmates
Enhance your understanding of course concepts by engaging in discussions and collaborative learning with peers.
Show steps
  • Identify a group of classmates with similar interests and learning goals.
  • Establish regular meeting times and a designated study space.
  • Review course materials together, discuss concepts, and solve problems collaboratively.
  • Provide feedback and support to each other.
Follow Tutorials on Financial Data Analysis
Expand your knowledge of financial data analysis by following tutorials that provide step-by-step guidance.
Browse courses on Financial Data Analysis
Show steps
  • Identify reputable sources for financial data analysis tutorials, such as Coursera or Udemy.
  • Choose a tutorial that aligns with your skill level and interests.
  • Follow the tutorial instructions and complete the exercises.
  • Apply the techniques learned in the tutorial to analyze real-world financial data.
Review 'Machine Learning with Python' by Sebastian Raschka
Review the fundamental concepts of machine learning and data mining with Python by reading this book.
View Build a Large Language Model (From Scratch) on Amazon
Show steps
  • Read Chapters 1-4 to gain an understanding of the basics of machine learning.
  • Complete the exercises in Chapters 1-4 to practice implementing machine learning algorithms.
Five other activities
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Show all eight activities
Practice Python Coding Challenges
Sharpen your Python coding skills by solving coding challenges on platforms like LeetCode or HackerRank.
Browse courses on Python
Show steps
  • Identify a coding challenge platform that aligns with your skill level.
  • Choose a challenge and read the problem statement carefully.
  • Implement your solution and test it against the provided test cases.
  • Review the solutions of others to learn alternative approaches and improve your own code.
Attend a Workshop on Alternative Data for Financial Markets
Gain practical insights into alternative data by attending a workshop led by industry experts.
Show steps
  • Research and identify workshops that focus on alternative data in financial markets.
  • Register for a workshop that aligns with your schedule and interests.
  • Attend the workshop and actively participate in discussions.
  • Follow up with the workshop organizers or speakers to continue learning and connect with others in the field.
Create a Data Visualization Dashboard
Consolidate your understanding of data visualization by creating an interactive dashboard using tools like Tableau or Power BI.
Browse courses on Data Visualization
Show steps
  • Gather and clean a dataset that aligns with your interests.
  • Choose a visualization tool and explore its features.
  • Design and implement visualizations to represent key insights from the data.
  • Create an interactive dashboard that allows users to explore the data and make informed decisions.
Develop a Financial Data Analysis Model
Apply your knowledge of financial data analysis to create a model that addresses a specific financial problem or opportunity.
Browse courses on Financial Data Analysis
Show steps
  • Identify a financial problem or opportunity that can be addressed through data analysis.
  • Gather and preprocess relevant financial data.
  • Develop a statistical or machine learning model to predict or optimize financial outcomes.
  • Validate and evaluate the performance of the model.
  • Write a report that documents the model development process and results.
Mentor a Junior Student in Financial Data Analysis
Solidify your understanding of financial data analysis by sharing your knowledge and skills with a junior student.
Browse courses on Financial Data Analysis
Show steps
  • Identify a junior student who is interested in financial data analysis.
  • Establish regular mentoring sessions and provide guidance on coursework, projects, and career development.
  • Share your experiences and insights in the field of financial data analysis.
  • Provide feedback and support to help the student develop their skills and knowledge.

Career center

Learners who complete Python and Machine-Learning for Asset Management with Alternative Data Sets will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use data to solve business problems. They use a variety of techniques to analyze data, including machine learning and artificial intelligence. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Data Scientists develop the skills they need to use alternative data to solve business problems in the financial industry.
See salaries and explore the career path for Data Scientist
Financial Analyst
Financial Analysts are responsible for evaluating the financial performance of companies and making recommendations on how to improve profitability. They use a variety of data to make their recommendations, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Financial Analysts develop the skills they need to use alternative data to make more informed recommendations.
See salaries and explore the career path for Financial Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use their models to make recommendations on investment decisions. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Quantitative Analysts develop the skills they need to use alternative data to build more accurate models.
See salaries and explore the career path for Quantitative Analyst
Data Analyst
Data Analysts use data to solve business problems. They use a variety of techniques to analyze data, including machine learning and artificial intelligence. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Data Analysts develop the skills they need to use alternative data to solve business problems in a variety of industries.
See salaries and explore the career path for Data Analyst
Investment Research Analyst
Investment Research Analysts provide research and analysis to investors. They use a variety of data to make recommendations, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Investment Research Analysts develop the skills they need to use alternative data to conduct more in-depth research.
See salaries and explore the career path for Investment Research Analyst
Risk Manager
Risk Managers are responsible for identifying and managing risks to their organizations. They use a variety of data to assess risk, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Risk Managers develop the skills they need to use alternative data to identify and manage risks more effectively.
See salaries and explore the career path for Risk Manager
Portfolio Manager
Portfolio Managers are responsible for managing investment portfolios for their clients. They use a variety of data to make investment decisions, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Portfolio Managers develop the skills they need to use alternative data to make more informed investment decisions.
See salaries and explore the career path for Portfolio Manager
Venture Capitalist
Venture Capitalists invest in early-stage companies. They use a variety of data to make investment decisions, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Venture Capitalists develop the skills they need to use alternative data to make more informed investment decisions.
See salaries and explore the career path for Venture Capitalist
Investment Banker
Investment Bankers help companies raise capital and advise them on mergers and acquisitions. They use a variety of data to make their recommendations, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Investment Bankers develop the skills they need to use alternative data to make more informed recommendations.
See salaries and explore the career path for Investment Banker
Private Equity Investor
Private Equity Investors invest in private companies. They use a variety of data to make investment decisions, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Private Equity Investors develop the skills they need to use alternative data to make more informed investment decisions.
See salaries and explore the career path for Private Equity Investor
Hedge Fund Manager
Hedge Fund Managers manage investment funds for their clients. They use a variety of data to make investment decisions, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Hedge Fund Managers develop the skills they need to use alternative data to make more informed investment decisions.
See salaries and explore the career path for Hedge Fund Manager
Consultant
Consultants provide advice to businesses and organizations. They use a variety of data to make recommendations, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Consultants develop the skills they need to use alternative data to provide more informed advice.
See salaries and explore the career path for Consultant
Financial Planner
Financial Planners help individuals and families plan for their financial future. They use a variety of data to make recommendations, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Financial Planners develop the skills they need to use alternative data to make more informed recommendations.
See salaries and explore the career path for Financial Planner
Economist
Economists study the economy and make recommendations on economic policy. They use a variety of data to make their recommendations, including alternative data such as consumption data, textual analysis, and corporate filings. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Economists develop the skills they need to use alternative data to conduct more rigorous economic analysis.
See salaries and explore the career path for Economist
Insurance Actuary
Insurance Actuaries use mathematical and statistical models to assess risk for insurance companies. They use their models to calculate premiums and make recommendations on insurance policies. The Python and Machine-Learning for Asset Management with Alternative Data Sets course can help Insurance Actuaries develop the skills they need to use alternative data to build more accurate models.
See salaries and explore the career path for Insurance Actuary

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python and Machine-Learning for Asset Management with Alternative Data Sets.
Cover image
Asset Management: Tools And Issues
Save
Provides a comprehensive overview of machine learning techniques that can be used by asset managers. It covers a wide range of topics, including data preparation, feature engineering, model selection, and performance evaluation.
Asset Management: Tools and Issues
Hardcover
$$$$
Cover image
The Age of Prediction
Save
Provides a comprehensive overview of artificial intelligence for finance. It covers a wide range of topics, including machine learning, natural language processing, and computer vision.
The Age of Prediction: Algorithms, AI, and the...
Hardcover
$$
The Age of Prediction: Algorithms, AI, and the...
Kindle Edition
$$
Financial Theory with Python
Save
Provides a comprehensive overview of Python programming for financial applications. It covers a wide range of topics, including data manipulation, data analysis, and machine learning.
Financial Theory with Python: A Gentle Introduction
Paperback
$$
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers a wide range of topics, including data preparation, feature engineering, model selection, and performance evaluation.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Cover image
Python for Data Analysis
Save
Provides a comprehensive overview of Python programming for data analysis. It covers a wide range of topics, including data manipulation, data analysis, and data visualization.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check
Cover image
Deep Learning with TensorFlow and Keras
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Provides a comprehensive overview of TensorFlow, a popular deep learning library for Python. It covers a wide range of topics, including data preparation, model building, and training.
Deep Learning with TensorFlow and Keras - Third...
Paperback
$$
Deep Learning with TensorFlow 2 and Keras - Second...
Paperback
$$
Deep Learning with TensorFlow and Keras: Build and...
Kindle Edition
$$
Deep Learning with TensorFlow 2 and Keras:...
Kindle Edition
$$
Cover image
Data Science for Business
Save
Provides a comprehensive overview of data science for business. It covers a wide range of topics, including data management, data analysis, machine learning, and data visualization.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
Financial Econometrics
Save
Provides a comprehensive overview of econometric techniques used in finance. It covers a wide range of topics, including time series analysis, regression analysis, and forecasting.
Financial Econometrics
Paperback
$$$
Financial Econometrics: Models and Methods
Kindle Edition
$$$
Learning Python
Save
Provides a gentle introduction to machine learning with Python. It covers a wide range of topics, including data preparation, feature engineering, model selection, and performance evaluation.
Learning Python
Paperback
Check
Learning Python
Kindle Edition
Check
Cover image
Machine Learning for Business Analytics
Save
Provides a comprehensive overview of data mining techniques and their applications in business analytics. It covers a wide range of topics, including data preparation, feature engineering, model selection, and performance evaluation.
Machine Learning for Business Analytics: Concepts,...
Hardcover
$$$$
Machine Learning for Business Analytics: Concepts,...
Kindle Edition
$$$$

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Course image
Rating
4.4 Filled star Filled star Filled star Filled star Empty star
Effort
20 hours to complete
Level
Intermediate
Via
Coursera
Institution
EDHEC Business School
Instructors
Gideon OZIK
Sean McOwen
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Investment Management with Python and Machine Learning. It's comprised of four courses:
  1. Advanced Portfolio Construction and Analysis with Python
  2. Introduction to Portfolio Construction and Analysis with Python
  3. Python and Machine Learning for Asset Management
  4. Python and Machine-Learning for Asset Management with Alternative Data Sets (viewing)
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides practical examples and lab sessions in addition to theory
Taught by instructors recognized for their work in alternative data
Emphasizes practical application of alternative data in financial markets
Focuses on cutting-edge research and technology in financial data
Requires Python programming, investment theory, and statistics as prerequisites
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