Advanced Financial Data Science & AI Bootcamp

Schedule & Location

About the Bootcamp

Welcome to the four-day Cognitive Class Data Science Bootcamp! This is an intensive hands-on bootcamp where you can learn the advanced topics of data science from IBM data scientists. You will learn machine learning, Deep Learning, Big Data, and Apache Spark. You will have a chance to apply your new skills to many finance-related projects.

What you will achieve at the end of the bootcamp:

An understanding of data analysis, machine learning, Apache Spark, and deep learning techniques. Also, libraries such as Pandas, Scikit-learn, and TensorFlow. All the materials are delivered through hands-on sessions.

Prerequisite course requirements:

You are either already comfortable programming in Python, or you have successfully completed the following online course: Python for Data Science (

This free Python course provides a beginner-friendly introduction to Python. Practice through lab exercises and you will be ready to start data analysis in the bootcamp.

Participants must supply their own laptops.

Bootcamp Agenda

Advanced Data Science Bootcamp for Finance

Bootcamp length: 4 days

Day 1:

  • Introduction
  • Introduction to Cognitive Class/Jupyter notebooks
  • Python Review
  • Data Analysis with Python
  • Data Science package: Numpy and Pandas

Afternoon: Statistics 1 with Python

  • Descriptive Statistics
  • Histograms Box plot
  • Probability Mass Functions
  • Normal Distribution
  • Linear Regression

Day 2: Machine Learning with Python (Classification)

  • KNN
  • Logistics Regression
  • SVM
  • Model Comparison
  • Random Forest


  • Logistic Regression and Softmax
  • Neural Networks
  • Deep Convolutional Neural Networks

Day 3:

  • Important finance and statistics concepts
  • Overview of linear algebra, matrix computations, and optimization
  • Introduction to Monte Carlo simulation modeling
  • Introduction to asset pricing by simulation
  • Factor models


  • Portfolio selection: portfolio optimization in practice, optimization under uncertainty
  • Quantitative risk management: risk measure VaR and CVaR, credit risk modeling
  • Asset pricing methods: binomial trees and Monte Carlo simulation, hedging, option pricing, derivatives

Day 4:

  • Sentiment analysis, text analytics, NLP and other applications of data analytics and AI finance
  • AI-based stress testing of financial portfolios
  • Cognitive portfolio selection


  • Exam

Participants with a passing grade will receive:


Data Science bootcamp validated

 IBM validated badge

  • an IBM course completion certificate
  • an IBM badge

Both the completion certificate and the badge will be stored and verifiable by Documentorum, an academic credentials blockchain.


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