The use of data science and machine learning in the investment industry is increasing. Financial firms are using artificial intelligence (AI) and machine learning to augment traditional investment decision making. In this course, we aim to bring clarity on how AI and machine learning are revolutionizing financial services. We will introduce key concepts and, through examples and case studies, will illustrate the role of machine learning, data science techniques, and AI in the investment industry. Rather than just showing how to write code or run experiments in Python, we will provide an intuitive understanding to machine learning with just enough mathematics and basic statistics.

You will learn:

  • Role of Machine Learning and AI in Financial services
  • When do we use Machine learning and AI techniques?
  • What are the key machine learning methodologies?
  • How do you choose an algorithm for a specific goal?
  • Practical Case studies with fully functional code


  • Session: 1.5 hours/session
  • Duration: 9 weeks
  • Case study + Labs using the QuSandbox

Who should attend?

  • Fundamental and quantitative analysts, risk and investment professionals, portfolio managers new to data science and machine learning
  • Financial professionals new to data-driven methodologies
  • Machine learning enthusiasts interested in use cases in fintech and financial organizations

Participants are expected to have a working knowledge of Python. Please consider taking the Just Enough Python for Data Science in Finance if you don’t know Python.

Analytics for a cause initiative:
QuantUniversity sponsors scholarships ,valued at $30,000 to their educational offerings to students from eight countries and 12 chapters, participating in the PRMIA - Professional Risk Managers'​ International Association Risk Management Challenge. Additional details about our announcement here

*Combo offer*

Add the 3-part "Just Enough Python for Data Science in Finance" course for just $150 ($349 value) that can be accessed on-demand


Starts Feb 1st

Number of sessions

9 Case studies + Labs using the QuSandbox

Course Duration

1.5 hours/session - 9 weeks


Online through QuAcademy

If you would like an invoice for your payment for reimbursement or related questions on alternative payment methods, please contact


Machine Learning and AI: An intuitive Introduction

  • Machine Learning vs Statistics: How has the world changed?
  • A tour of Machine Learning and AI methods
    • Supervised Learning Vs Unsupervised Learning
    • Deep Learning
    • Reinforcement Learning
  • Key drivers influencing the adoption of Machine Learning and AI
    • Big Data, Hardware, Fintech, AI, Alternative Data
  • Key applications
    • Credit risk, Personalization, Predicting risk, Portfolio optimization and selection
  • Key players
    • Technology companies, Data vendors, Banks, Fintech startups

Exploratory data analysis

  • Exploring and Visualizing large datasets
    • The Visualization zoo
    • A framework to decide how to chart datasets
    • Examples on how to build powerful dashboards
    • Case study 1: Visualizing Categorial, Numerical, Cross-sectional and Time series Financial datasets

Unsupervised Learning

  • Dimension reduction and visualizing datasets using PCA, T-SNE
  • Manifold Learning
  • Case study: Visualizing high-dimensional Datasets

Unsupervised Learning

  • Clustering Techniques
  • Distance measures
  • K-means
  • Hierarchical Clustering
  • Affinity Propagation
  • Case study 2: Using K-means for automatic clustering of stocks

Supervised Learning

Learn from the past: How does Supervised machine learning work?

  • Cross sectional data
  • Time series analysis
  • Regression, Random Forests and Neural Networks
  • Evaluating machine learning algorithms
  • Case study 3: Predicting interest rates and credit risk using Alternative data sets.

Neural Networks + Synthetic Data Generation

  • Introduction to Neural Networks and Deep Neural Networks
  • Case study 4: Synthetic Data Generation for VIX Scenarios

Natural Language Processing

  • Making sense of Text and Natural Language Processing
  • Sentiment Analysis: How to interpret sentiments and use it in stock selection?
  • Case study 5: Analyzing Earning calls using text analytics

Frontier topics

  • Key issues in adopting AI and Machine learning into investment workflows
  • How will Machine Learning and AI change the investment industry
  • Frontier topics
    • Anomaly detection
    • Reinforcement learning
    • Quantum Computing
    • Risk in Machine Learning and AI
    • Model governance, Interpretability and Model Management


Project presentations + Virtual Certificate Ceremony


Past Attendees of QuantUniversity workshops include Assette, Baruch College, Bentley College, Bloomberg, BNY Mellon, Boston University, Datacamp, Fidelity, Ford, Goldman Sachs, IBM, J.P. Morgan Chase, MathWorks, Matrix IFS, MIT Lincoln Labs, Morgan Stanley, Nataxis Global, Northeastern University, NYU, Pan Agora, Philips Health, Stevens Institute, T.D. Securities and many more..