Mastering Probability and Statistics in Python
A comprehensive course that teaches you the concepts and methodologies of statistics and probability with data science.
Updated on Sep, 2023
Language - English
Course Description
This course is for individuals who want to learn statistics and probability along with its implementation in realistic projects. Data scientists and business analysts and those who want to upgrade their data analysis skills will also get the benefit. People who want to learn statistics and probability with real datasets in data science and are passionate about numbers and programming will get the most out of this course.
In today’s ultra-competitive business universe, probability and statistics are the most important fields of study. That is because statistical research presents businesses with the data they need to make informed decisions in every business area, whether it is market research, product development, product launch timing, customer data analysis, sales forecast, or employee performance.
But why do you need to master probability and statistics in Python?
The answer is that an expert grip on the concepts of statistics and probability with data science will enable you to take your career to the next level. This course is designed carefully to reflect the most in-demand skills that will help you in understanding the concepts and methodology with regard to Python.
The course is as follows:
- Easy to understand
- Expressive
- Comprehensive
- Practical with live coding
- About establishing links between probability and machine learning
By the end of this course, you will be able to relate the concepts and theories in machine learning with probabilistic reasoning and understand the methodology of statistics and probability with data science, using real datasets.
The code files and all related files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Mastering-Probability-and-Statistics-in-Python
Goals
What will you learn in this course:
- The importance of statistics and probability in data science
- The foundations for machine learning and its roots in probability theory
- The concepts of absolute beginning in-depth with examples in Python
- Practical explanation and live coding with Python
- Probabilistic view of modern machine learning
- Implementation of Bayes’ classifier on a real dataset
Prerequisites
What are the prerequisites for this course?
- No prior knowledge is needed. You start from the basics and gradually build your knowledge of the subject. A basic understanding of Python will be a plus but not mandatory.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to the Course
2 Lectures
-
Introduction to the Instructor
12:36
12:36
-
Focus of the Course
10:15
10:15
Probability and Statistics
1 Lectures
Sets
9 Lectures
Experiment
4 Lectures
Probability Model
16 Lectures
Random Variables
11 Lectures
Continuous Random Variables
10 Lectures
Expectations
7 Lectures
Project Bayes' Classifier
1 Lectures
Multiple Random Variables
8 Lectures
Optional Estimation
7 Lectures
Mathematical Derivations for Math Lovers
5 Lectures
Instructor Details
Packt Publishing
Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.
Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.
As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.
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