Probability And Statistics For Data Science
- Offered byThe knowledge academy
Probability And Statistics For Data Science at The knowledge academy Overview
Duration | 2 days |
Start from | Start Now |
Total fee | ₹45,995 |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Admission Process
Probability And Statistics For Data Science at The knowledge academy Highlights
- Earn a certificate after completion of course
- Engage in activities, and communicate with your trainer and peers
Probability And Statistics For Data Science at The knowledge academy Course details
Data Scientists
Machine Learning Engineers
Data Analysts
Business Analysts
Product Managers
Quantitative Analysts
Statisticians
To represent and analyze uncertain phenomena using a framework
To quantify the outcome of the experiment as belonging to a specific event
To assign probabilities to each occurrence of interest and an experiment
To become accustomed to Markov chains and different statistical types
To generate samples from the appropriate conditional distribution
To evaluate the occurrence of a particular event that influences another event
Probability and Statistics form the foundational pillars of Data Science, providing the necessary tools for understanding uncertainty, variability, and making informed decisions based on data
This training course in India delves into the fundamental concepts of probability and statistics, emphasizing their crucial role in the field of Data Science
Delegates will explore how these concepts contribute to the extraction of meaningful insights and patterns from data
Probability And Statistics For Data Science at The knowledge academy Curriculum
Day 1: Probability and Random Variables
Module 1: Basic Probability Theory
Probability Spaces
Conditional Probability
Independence
Module 2: Random Variables
Random Variables Intro
Discrete Random Variables
Continuous Random Variables
Conditioning on Events
Functions of Random Variables
Generating Random Variables
Module 3: Multivariate Random Variables
Introduction
Discrete & Continuous Multivariate Variables
Joint Distributions
Independence
Functions of Several Variables
Generating Multivariate Variables & Rejection Sampling
Module 4: Expectation
Expectation Operator, Mean and Variance
Covariance, Conditional Expectation
Module 5: Random Processes
Intro, Mean, and Autocovariance Functions
IID
Gaussian Process
Poisson Process
Random Walk
Day 2: Statistics and Applications (8 hours total)
Module 6: Convergence of Random Processes
Types of Convergence
Law of Large Numbers
Central Limit Theorem
Monte Carlo Simulation
Module 7: Markov Chains
Markov Property and Basic Concepts
Recurrence
Periodicity
Convergence
Introduction to Markov-Chain Monte Carlo (MCMC)
Module 8: Descriptive Statistics
What are Descriptive Statistics
Examples and Types of Descriptive Statistics
Module 9: Frequentist Statistics
Mean Square Error
Consistency
Confidence Intervals
Parametric vs Nonparametric Model Estimation
Maximum Likelihood Estimation (MLE)
Module 10: Bayesian Statistics
Bayesian Parametric Models
Conjugate Priors
Bayesian Estimators
Module 11: Hypothesis Testing
Hypothesis-Testing Framework
Parametric Testing
Nonparametric Testing: The Permutation Test
Multiple Testing Correction
Module 12: Linear Regression
Introduction to Linear Regression
Linear Models & Applications
Least-Squares Estimation
Probability And Statistics For Data Science at The knowledge academy Admission Process
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