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  1. Home
  2. Learning Paths

Data Science for Investment Professionals Learning Path

Data Science for Investment Professionals is a Specialization designed by Coursera. It consists of three courses and can be completed in five months with 3 hours of learning content .

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Description

This Specialization is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process.

Through the three courses, you will learn techniques for presenting data and importance of the “data story”, produce data visualizations using Python, assess and apply probability concepts to investing scenarios, compare simple time-series models and understand their limitations, discover how machine learning applications can address investment problems, and understand how to apply the CFA Institute Ethical Decision-Making Framework to machine learning dilemmas.

All that you learn in this Specialization will give you the knowledge and confidence to explain clearly and “translate” machine learning concepts and their application to real-world investment problems to a non-expert audience and clients.

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Free Learning Paths by Career Track

Bootstrap Data ScientistBootstrap Data AnalystBootstrap Business AnalystBootstrap Data EngineerBootstrap Chief Data OfficerBootstrap Data Architect

Included Courses

Codecademy
CFA Institute
Free
1. Data and Statistics Foundation for Investment Professionals
Neil Govier

21 hours

Beginner

1,581
Python Big Data Data Analysis Data Science Data Visualization Decision Trees
Coursera
CFA Institute
Free
2. Statistics for Machine Learning for Investment Professionals
Shreenivas Kunte

18 hours

Beginner
Python Artificial Intelligence Big Data Data Analysis Data Modeling Data Science
Coursera
CFA Institute
Free
3. Machine Learning for Investment Professionals
Anastasia Diakaki

17 hours

Beginner
Algorithms Artificial Intelligence Classification Data Analysis Data Ethics
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