Data Science & Machine Learning for Finance

Join live classes taught by top rated instructors


Average instructor rating of 4.8 


We have helped thousands of students all over the world with our courses.


We’re always adding new material – and you’ll get lifetime access to all the content: videos, code and more!


After successfully passing the course you will receive a certificate of completion that you can show to employers.

(3 Payments of $199)


Save 20% before 11pm EST on September 3 Using this Code at Checkout: 

What You Get

  • 12 week course, with the instructor teaching live
  • Live Q&A during and after the weekly lectures
  • 2 projects that you can add to GitHub and show off to employers
  • Office hours every week to ask questions live
  • Unlimited email/chat Q&A during the course
  • Lifetime access to our video and code database

Increase Your Salary

Make More Money From Your Investments

Level Up Your Skills

Create Projects You Can Add to Your Resume


Designed to be an Ivey League quality course for a faction of the price, the Data Science & Machine Learning for Finance course will teach you the fundamentals of Data Science/Machine Learning and how to apply them to the finance world in only 12 weeks. The course is fast paced

The Curriculum

Registration Closes: October 15, 2020

Course Starts: October 22, 2020

Course Length: 12 Weeks (Excluding Holiday Breaks)

Effort: 8-11 Hours Per Week, Self Paced Learning

Class Schedule: Every Wednesday at 8pm EST (5pm PDT)

Lecture Duration: 1 Hour Per Week,  Live Over Zoom

2 Projects that you’ll be able to add to your resume

Week 1: Course Introduction

  • Introductions
  • Course Layout and Tools
  • System Setup
  • Q&A

Week 2: Python Refresher

  • Numbers, Strings and Loops
  • Functions
  • Libraries
  • Q&A

Week 3: Introduction to Pandas

  • Dataframes and Series
  • Dataframe Operations
  • Q&A

Week 4: Working With Databases

  • SQL Database Introduction
  • Connecting to a Database
  • Read/Write to Database
  • Q&A

Week 1: Pandas in Finance

  • Loading a CSV File
  • Graphing
  • Q&A

Week 2: Working With Stock and Economic Data

  • Using Pandas Datareader
  • Economic Data from FRED
  • Project 1 Introduction
  • Q&A

Week 3: Statistics With Financial Data

  • Getting Yahoo Finance Data
  • Calculating Returns
  • Correlations
  • Q&A

Week 4: Financial Metrics

  • CAGR, StDev, Max Drawdown and Sharpe Ratio
  • Fundamental Analysis
  • Q&A

Week 1: Introduction to Machine Learning

  • Supervised vs Unsupervised Learning
  • Neural Networks
  • SciKit Learn
  • Keras

Week 2: Getting and Organizing Data for Machine Learning

  • Project 2 Introduction

Week 3: Analyzing Text Data With Machine Learning 


Week 4: Analyzing Image Data with Machine Learning 



Robert has spent most of his career managing software teams on Wall Street and at various software startups.

He’s taught thousands of students in computer
programming and finance both in person and online.

In college, he received his Masters of Finance from a top business school and used to teach finance to classrooms with over 100 students each.

Robert has been programming computers for over 20 years using many different languages including Python, JavaScript, C, C++, Ruby on Rails and more.

More than 2,500 students have taken our Python courses.

Here's What They Have to Say:

A practical approach to coding and finance. The instructor makes you think deep about where the data is arriving from, how to use it, and where to get it.

A real resume-worthy course that won’t go away for a while.

Thomas Dines

This course will get your foot on the door! It works as a guide for the those new to the development world, helps you build knowledge and helps you decide what to focus on after you are done.

Harry Fisher

Loved this course! Robert was able to help get my trading algorithm working and it’s been
making me money every day!

Brent O'Hara

This was a great course! The instructor was very clear, explained the topics well, and went over some very interesting concepts.

I learned a lot about how to make better investment decisions using Python and Pandas.

Magdalena Loiero