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

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.

Textual Analysis for Financial Applications

Module 2 is an introduction to text mining as well as a demonstration of how to get from data retrieval (web scraping) to financial market insights. Some of the classic text mining methodologies are covered such as vectorization of text (the bag of words approach), stop words for filtering, and term frequency-inverse document frequency (TF-IDF). Students will learn how text can be mathematically represented, and regularized/filtered to reduce noise. Measures of text-similarity will be covered in theoretical and practice sessions. Lab sessions go through examples of web scraping data, regularizing with the described techniques and finally, insights will be derived from the textual data.

Processing Corporate Filings

Module 3 is a practical extension of the text mining lessons to 10-K and 13-F, two of the most commonly researched corporate filings. This type of data can be extremely daunting when used by individual analysts due to the sheer size of the documents, but module 3 describes the methodologies for quantitatively analyzing these documents with Python code. Both the 10-K and 13-F documents are worked through, and within the lab sessions it is demonstrated how one can automatically pull this kind of data as well as define metrics around them. We investigate implementations of research in this field around similarity of given companies 10-K statements over time as well as similarity between fund holdings from the 13-F in the lab.

Using Media-Derived Data

The final module introduces both sentiment analysis in the context of textual data as well as network analysis in the context of connectivity of firms. Sentiment analysis is an avenue of potentially fruitful information that when done correctly can display what a general population might believe about a company (through for example social media) or even whether the company itself is positive or negative on future outlook (through analysis of tone in corporate filings). Network analysis, as shown in the research of course instructors and his colleagues, can be used to accurately capture how a financial network is oriented and what companies might perform well because of other firm’s mentioning them as a threat. The lab session of this module extends the corporate filings analysis to examine sentiment while also introducing a set of tweets which are then transformed into a network representation.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Advanced and practical asset management

Students say this course is an excellent choice for **finance** and **investment** practitioners, as it applies **Python** and **machine learning for asset management** with **alternative data sets**. Learners highly rate the **labs**, finding them **engaging** and well explained. The course is considered **highly relevant** and well organized. Instructors are praised for their expertise and teaching style. However, some students express concern that the explanations of technical concepts could be more detailed and the quizzes could be more closely related to the labs.

Covers a wide range of topics

"Trully amazing in the breadth of topics covered, including: "

"While the depth of the analysis was not too deep, it was deep enough for the analyses to be somewhat complex, smart and for there being some space where nuances and future work could be commented and suggested"

"This is perfectly understandable by the trade-off between depth and breadth for a course of reasonable length."

In-depth technical training

"Very In-depth technical and informative."

"Detailed Python notebooks clearly explained give valuable tools for analyzing data, and the lectures give ideas what to do with the analyzed data."

"The course surveys the most important approaches for financial alternative datasets. It will give you a framework for how to see and explore this type of data, build hypotheses and test them."

Effective hands-on experience

"Great lab sessions and very well explained theory."

"I liked the lab sessions a lot - this as very useful!"

"This is a fine conclusion to an amazing specialization. Thanks!"

Lack of in-depth explanations

"Some items in Labs (Sentiment Analysis) were missing from course resources."

"The content is really great, but it would have been even better if the code and applications were explained in somewhat more detail."

Needs improvement

"The graded quiz could be less theoretical and a bit more practical/applied."

"Very good theory and practice. Only comment is the lack of a connection of the quizzes to the notebooks, more questions related to the notebooks would be very beneficial."

"The course is quite good, but the labs were quite rushed - students would benefit from going through the notebooks in more detail with the teachers."

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

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Enhance your understanding of course concepts by engaging in discussions and collaborative learning with peers.

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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

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Expand your knowledge of financial data analysis by following tutorials that provide step-by-step guidance.

Browse courses on Financial Data Analysis

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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

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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

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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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Practice Python Coding Challenges

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Sharpen your Python coding skills by solving coding challenges on platforms like LeetCode or HackerRank.

Browse courses on Python

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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

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Gain practical insights into alternative data by attending a workshop led by industry experts.

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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

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Consolidate your understanding of data visualization by creating an interactive dashboard using tools like Tableau or Power BI.

Browse courses on Data Visualization

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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

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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

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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

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Solidify your understanding of financial data analysis by sharing your knowledge and skills with a junior student.

Browse courses on Financial Data Analysis

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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

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

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

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.

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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.

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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

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

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.

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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.

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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.

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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.

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