Data Analysis Bootcamp

Data Analysis Training with Placement

Course Content:

Introduction to Data Analysis:

• The need for Data Analysis
• Reign of Data Analysis
• What we are going to achieve
• Real life examples

Probability and Random Process:

• Basics of probability and Random Process
• Theoretical and Intuitive Information

Mathematical framework of randomness:

• Formulation of random variables
• Properties of random signal
• Inferences from properties defined on random variables
• Random Processes as data generating phenomena

Parametric Methods:

• The concept of Least squares
• AR, MA models and relation between them
• ARX, ARMAX

Non Parametric Methods:

• Spectral Analysis
• Power Spectral Density
• Autocorrelation and Cross-correlation Analysis
• Impulse response and step response

State Estimation:

• Theoretical Background
• Kalman Filter with example

Hands on experience for Data Modeling, Control and signal processing:

• Simulations in MATLAB System Identification Toolbox