Multivariate Theory (Covariance, Independence, Conditional Expectation)
Market Models and simple Portfolio Optimization
Discrete Market Models and Derivatives Pricing using Risk Neutral Probability measures
Stochastic Processes and martingales
Machine Learning For Practitioners
Data ETL, statistics and plotting
Linear Regression including Regularization techniques
Variables Selection - For Regressions, PCA, Correlated Variables AIC and CV use
Model Selection, Test/Train CV, Caret and scikit-learn. AIC, BIC, Bias Variance tradeoff, Use of test/train split and CV use
Classification Techniques
Ensemble Models: Trees, Random Forest, Gradient Boosting. Variable selection with ensemble techniques
Support Vector Machine
Practicum Analysis of a data set
Time Series Analysis and Forecasting
Stationarity
ARMA, ARIMA and GARCH models
Introduction to Spectrum
GMM
Introduction to VARs
Factor Models
Empirical Processes
Unit Roots
Cointegration
Filtering, State Space Models
Kalman Filter
Statistics
Analysis, Interpretation and Visualization of data
Confidence Intervals and Significance tests
Design of Experiments and Hypothesis Testing
Analysis of variance
Multiple Regression models
Advanced Machine Learning and Artificial Intelligence
Introduction to Deep Learning Principles, Tensor Flow and Keras
Convolutional Neural Networks
Hidden Markov Models
Recurrent Neural Networks
Deep Autoencoders
Reinforcement Learning
Numerical Methods
Linear and Non Linear Equations
Interpolation and Optimization
Finite Difference Method
Monte Carlo Simulations
Variance Reduction Techniques
Principal Component Analysis
Foundation Courses
All our data science courses require Linear Algebra and Advance Calculus as pre-requisite. We encourage students to take foundation courses in Linear Algebra and Calculus before diving into advanced data science courses
