Data Science for Investment Professionals

CFA Institute via Coursera Specialization

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Details

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Duration & workload

22 weeks, 3 hours a week
Level

Beginner

Found in

Overview

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.

Syllabus

Course 1: Data and Statistics Foundation for Investment Professionals
- Offered by CFA Institute. Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction ... Enroll for free.

Course 2: Statistics for Machine Learning for Investment Professionals
- Offered by CFA Institute. One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in ... Enroll for free.

Course 3: Machine Learning for Investment Professionals
- Offered by CFA Institute. This course is uniquely tailored to the needs of investment professionals or those with investment industry ... Enroll for free.

Courses

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6 weeks
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This course 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. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.

In this course, you will learn how to:
- Distinguish between supervised and unsupervised machine learning and deep learning
- Describe how machine learning algorithm performance is evaluated
- Describe supervised and unsupervised machine learning algorithms and determine the problems they are best suited for
- Describe neural networks, deep learning nets, and reinforcement learning
- Choose an appropriate machine learning algorithm
- Describe the value of integrating machine learning and data projects in the investment process
- Work with data scientists and investment teams to harness information and insights from within large and alternative data sets
- Apply the CFA Institute Ethical Decision-Making Framework to machine learning dilemmas

This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.
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7 weeks
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Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing.

In this course you will learn how to:
- Explain basic statistical measures and their application to real-life data sets
- Calculate and interpret measures of dispersion and explain deviations from a normal distribution
- Understand the use and appropriateness of different distributions
- Compare and contrast ways of visualizing data and create them using Python (no prior knowledge of Python necessary)
- Explain sampling theory and draw inferences about population parameters from sample statistics
- Formulate hypotheses on investment problems

This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.
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6 weeks
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One of the biggest changes in the past decade is the rapid adoption of machine learning, AI, and big data in investment decision making. This course introduces learners with knowledge of the investment industry to foundational statistical concepts underpinning machine learning as well as advanced AI techniques. This course demonstrates core modeling frameworks along with carefully selected real-world investment practice examples. The course seeks to familiarize learners with two important programming languages — Python and R (no prior knowledge of Python or R necessary). The motivation is to demonstrate the elegance — and speed — simple programming brings to the investment decision-making process. The reading material in this course offers in-practice insights curated from the blogs of CFA Institute as well as other leading publications.

After taking this course you will be able to:
- Describe the importance of identifying information patterns for building models
- Explain probability concepts for solving investing problems
- Explain the use of linear regression and interpret related Python and R code
- Describe gradient descent, explain logistic regression, and interpret Python and R code
- Describe the characteristics and uses of time-series models

This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute.