## 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

#### 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