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Data Science Specialization

Probability and Distributions

  • Basic Probability theory and Bayes Theorem
  • Discrete and Continuous random variable
  • Expected Values and Higher Order moments
  • Quantiles and Inverse Quantiles
  • Statistical Distributions and density functions
  • MultiVariate theory (covariance, Independence, conditional expectation)
  • MultiVariate Distributions
  • Maximum Likelihood Methods
  • Goodness of fit tests and hypothesis testing

Machine Learning

  • Overview of statistics
  • Linear Regression
  • Bias Variance TradeOff
  • Classification
  • Resampling
  • Model Selection techniques
  • Tree Based Methods
  • Support Vector Machine and Boosting techniques

Numerical Methods

  • Linear and Non Linear Equations
  • Interpolation and Optimization
  • Finite Difference Method
  • Monte Carlo Simulations
  • Variance Reduction Techniques
  • Principal Component Analysis

Statistics

  • Analysis, Interpretation and Visualization of data
  • Confidence Intervals and Significance tests
  • Design of Experiments and Hypothesis Testing
  • Analysis of variance
  • Multiple Regression models

Time Series Analysis

  • Stationary and non-stationary processes
  • AR process
  • MA and ARMA
  • ARCH and GARCH processes
  • Stochastic Volatility model

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 advance data science courses

Introduction to Linear Algebra

Introduction to Calculus