Applications of Quantitative Finance – Advanced Concepts in Data Science

Take a look at what our course modules include in a little more detail:

Module One: kNN Design
What are the limits of a neural network?; Does more always make the networks stronger?; Computation costs and complications

Module Two: Big Data Manipulation
Introduction to Big Data; Hadoop and MapReduce functions; Storage vs. computation costs

Module Three: Advanced Statistics and Probability
Correlation and covariance; Moment and function derivatives; Precedent statistics

Module Four: Bayesian Inference and Gaussian Process
What is Bayesian Inference?; Gaussian distributions and process; Working with new ML model techniques

Module Five: NGrams and Their Applications
Text analysis basics; Understanding NGrams and their value; Bag of words concepts

Module Six: PyTorch Applications
Open source data science initiatives; PyTorch benefits and pitfalls; Supporting OS tools