This course is part of the Data Science for Investment Professionals Specialization

4.7
stars
25 ratings

Shreenivas Kunte, CFA, CIPM

2,088 already enrolled

Offered By

Data Science for Investment Professionals SpecializationCFA Institute

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 2 of 3 in the

Data Science for Investment Professionals Specialization

Beginner Level

Background in investment products and firms

Approx. 18 hours to complete

English

What you will learn

Understand the importance of information patterns and the concept of causality in decision making models
Assess and apply probability concepts to real-world investing scenarios
Describe and use linear and logistic regression modeling for investment decision making
Compare simple time-series models and understand their limitations

Skills you will gain

Data Science
Logistic Regression Modeling
Linear Regression Modeling
Python Programming
Statistical Concepts

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 3 in the

Data Science for Investment Professionals Specialization

Beginner Level

Background in investment products and firms

Approx. 18 hours to complete

English

Instructor

Instructor rating

4.59/5 (10 Ratings)

Shreenivas Kunte, CFA, CIPM

Director, Continuing Education and Advocacy

2,088 Learners

1 Course

Offered by

CFA Institute

Syllabus - What you will learn from this course

Week

Week 1

24 minutes to complete

Welcome to Statistics for Machine Learning for Investment Professionals

24 minutes to complete

1 video (Total 4 min), 1 reading

2 hours to complete

Data and Patterns

2 hours to complete

5 videos (Total 35 min), 2 readings, 3 quizzes

5 videos

Data and Patterns1m

Data and the Importance of Patterns10m

How Do We Model Relationships between Variables? Part 111m

How Do We Model Relationships between Variables? Part 24m

Expert Insights: Data and Patterns7m

2 readings

Data and Alternative Sources of Alpha10m

Financial Models10m

3 practice exercises

Data and the Importance of Patterns15m

Modeling Relationships between Variables20m

Data and Patterns Graded Assessment20m

Week

Week 2

2 hours to complete

Randomness and Probability

2 hours to complete

6 videos (Total 52 min), 2 readings, 3 quizzes

6 videos

Randomness and Probability1m

Random Processes and Probability Distributions, Part 112m

Random Processes and Probability Distributions, Part 27m

Probability Concepts, Part 112m

Probability Concepts, Part 29m

Expert Insights: Probability Concepts7m

2 readings

Real-Life Case Study: Common Probability Distributions10m

Real-Life Example: Bayes Theorem10m

3 practice exercises

Random Processes and Probability Distributions15m

Probability Concepts15m

Randomness and Probability Graded Assessment20m

Week

Week 3

6 hours to complete

Linear Regression

6 hours to complete

15 videos (Total 113 min), 2 readings, 12 quizzes

15 videos

Linear Regression2m

From Patterns to Regressions, Part 18m

From Patterns to Regressions, Part 27m

Regression with Python and R, Part 310m

From Patterns to Regressions, Part 46m

Linear Regression Assumptions and Interpreting Output, Part 16m

Linear Regression Assumptions and Interpreting Output, Part 211m

Linear Regression Assumptions and Interpreting Output, Part 38m

Linear Regression Assumptions and Interpreting Output, Part 46m

Linear Regression Dummies, Part 17m

Linear Regression Dummies, Part 28m

Linear Regression Assumption Violations, Part 18m

Linear Regression Assumption Violations, Part 24m

Linear Regression Assumption Violations, Part 33m

Expert Insights: Linear Regression10m

2 readings

Installing Python & Anaconda5m

An R-Squared Chart Taxonomy: Seeing Is Not Believing10m

5 practice exercises

From Patterns to Regressions15m

Linear Regression Assumptions15m

Linear Regression: Using Dummy Variables15m

Linear Regression Assumption Violations30m

Linear Regression Graded Assessment30m

Week

Week 4

5 hours to complete

Advanced Regression Concepts

5 hours to complete

10 videos (Total 70 min), 2 readings, 10 quizzes

10 videos

Advanced Regression Concepts3m

Gradient Descent, Part 110m

Gradient Descent, Part 23m

Bias–Variance and Linear Regression Limitations9m

Bias–Variance: Python Case Study8m

Bias and Variance: In-Practice Examples3m

Logistic Regression10m

Logistic Regression: Using Python6m

Logistic Regression: Real-World Examples6m

Expert Insights: Logistic Regression7m

2 readings

Quick Snapshot: Linear vs. Logistic Regression10m

Detecting Earnings Manipulation and Fraud: A Light Tutorial on Probit Analysis10m

7 practice exercises

Gradient Descent Use Case15m

Gradient Descent5m

Bias–Variance and Linear Regression Limitations15m

Bias and Variance: In-Practice Examples15m

Logistic Regression30m

Logistic Regression: Real-World Examples30m

Advanced Regression Concepts Graded Assessment30m

Reviews

4.7

4 reviews

5 stars
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4 stars
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2 stars
3.84%
1 star
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TOP REVIEWS FROM STATISTICS FOR MACHINE LEARNING FOR INVESTMENT PROFESSIONALS

by CLSep 28, 2022

Its a course that serves as a good refresher on statistical analysis. Real applications makes statistical concepts easier to relate

View all reviews

About the Data Science for Investment Professionals Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Does completing this course give me credit in any CFA Institute Programs?
No. Completing this course or any other courses offered by CFA Institute on Coursera does not count as credit in the CFA Program, CIPM Program, or any other professional certificate or credential offered by CFA Institute.

More questions? Visit the Learner Help Center.

Statistics for Machine Learning for Investment Professionals

About this Course

What you will learn

Skills you will gain