Data Science Specialization

Probability Theory

  • Probability Spaces and Random Variables
  • Distribution and Density functions
  • Law of large numbers
  • Univariate and multivariate distributions
  • Expected Values and Higher Order Moments
  • 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


  • 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

Introduction to Linear Algebra

Introduction to Calculus