# Probability Theory, Statistics and Exploratory Data Analysis

Probability Theory, Statistics and Exploratory Data Analysis
Probability, probability, and statistics are fundamental mathematical and computer science concepts that underlie a great many fields in statistics and computer science. We need to understand these concepts as we dive into statistics and computer science topics.

In this course you will learn the concepts and use probability distributions to solve problems in statistics and computer science. We will use a practical software package implemented in Python, and a simple statistical tool developed in R, to get you started. You will also learn how to integrate these key concepts to form meaningful conclusions.

This is the third course in the specialization Probability and Statistics. We will introduce a number of statistical concepts and you will use these concepts to make sense of data. We will use Python to implement these concepts in a practical and usable way. You will also learn how to integrate these key concepts to form meaningful conclusions.

This course is the fourth course in a sequence that features interactive programming, data exploration, machine learning, and optimization. This series will consist of four assignments that require you to design, implement, and analyze a simple algorithm. This is a hands-on, fun series that will allow you to practice and expand your skill set in statistics and computer science.

Please note that the free version of this class gives you access to all of the instructional videos and handouts. The peer feedback and quizzes are only available in the paid version.Probability
Randomness
Inference
Probabilistic Graphical Models
Probability and statistics are fundamental mathematical and computer science concepts that underlie a great many fields in statistics and computer science. We need to understand these concepts as we dive into statistics and computer science topics.

In this course you will learn the concepts and use probability distributions to solve problems in statistics and computer science. We will use a practical software package implemented in Python, and a simple statistical tool developed in R, to get you started. You will also learn how to integrate these key concepts to form meaningful conclusions.

This is the second course in a sequence that features interactive programming, data exploration, machine learning, and optimization. This series will consist of four assignments that require you to design, implement, and analyze a simple algorithm. This is a hands-on, fun series that will allow you to practice and expand your skill set in statistics and computer science.

Please note that the free version of this class gives you access to all of the instructional videos and handouts. The peer feedback and quizzes are only available in the paid version.Probability
Randomness
Inference
Probability, Statistics and Probability Distributions
In this course, we’ll introduce the basic concepts and use the integral calculus of probability to solve problems in statistics and computer science. We’ll use Python to implement