Statistical Modeling Explained using Python



Learn complete Statistical Analysis Alongside Regression Analysis in Python

What you will learn

• Learn the about basics of statistical modeling in python

• Learn how to calculate the Average(Mean, Mode, Median) by python

• Learn how to calculate the Standard derivation

• Learn how to calculate the IQR and Variance

• Learn the basics of Hypothesis Testing

• Learn the significance of Hypothesis Testing

• Learn what are the terminologies of Hypothesis Testing

• Learn what is the P and Critical value in the Hypothesis Testing

• Learn the hands-on Implementation of Statistical Modeling by Python

• Learn about the Regression and about the Multiple Regression and its components

• And much more…

Description

Comprehensive Course Description:

Have you ever wanted to build a simple, easy and efficient Statistical Model for your business?

Do you need an efficient instructor for your education?

You might have searched for many relevant courses, but this course is different!

This course is a complete package for beginners to learn the basics of Statistical Modeling with Python, its applications and building it from scratch by using Statistics concepts with python. Every module has engaging content covering necessary theoretical concepts with a complete practical approach used along with brief theoretical concepts.

We will be starting with the theoretical and practical concepts of Statistical Modeling, after providing you with the basic knowledge of Statistical Modeling. You will be able to learn about the important fundamental concepts of Statistical Models which are the basic building blocks of it.

This complete package will enable you to learn the basics to advance mechanism of developing Statistical Models by using python. We’ll be using Python as a programming language in this course, which is the hottest language nowadays if we talk about machine learning. Python will be taught from elementary level up to an advanced level so that any machine learning concept can be implemented.

This comprehensive course will be your guide to learning how to use the power of Python to evaluate your Statistical Models based on the datasets. We’ll learn all the basic and necessary concepts for developing Statistical Models along with the Python.

This course is designed for both beginners with some programming experience and those who know nothing about Data Analysis, Statistical Models, Statistics and Python.

This comprehensive course is comparable to other Statistical Models Development with Python courses that usually cost hundreds of dollars, but now you can learn all that information at a fraction of the cost in only one course! With over 3 hours of HD video lectures that are divided into many videos and detailed code notebooks for every address, this is one of the most comprehensive courses for Statistical Modeling with Python on Udemy!

Why Should You Enroll in This Course?

The course is crafted to help you understand not only the role and impact of Statistical Modeling industry in real world applications but it provides a very unique hands on experience on developing complete Statistical Models for your customized dataset by using various projects. This straightforward learning by doing course will help you in mastering the concepts and methodology with regards to Python.

This course is:

· Easy to understand.

· Expressive and self-explanatory

· To the point

· Practical with live coding

· A complete package with three in depth projects covering complete course contents

Teaching Is Our Passion:

We focus on creating online tutorials that encourage learning by doing. We aim to provide you with more than a superficial look at practical approach towards developing Statistical Models using Python. For instance, this course has one project in the final module which will help you to see for yourself via experimentation the practical implementation of Statistics with python on the real-world datasets. We have worked extra hard to ensure you understand the concepts clearly. We want you to have a sound understanding of the basics before you move onward to the more complex concepts. The course materials that make certain you accomplish all this include high-quality video content, course notes, meaningful course materials, handouts, and evaluation exercises. You can also get in touch with our friendly team in case of any queries.

Course Content:

We’ll teach you how to program with Python, how to use Statistics concepts to develop Statistical Models! Here are just a few of the topics that we will be learning:

1. Course Overview

2. Overview of Summary Statistics

§ Average

§ Mean, Mode, Median

§ Std. Deviation

§ Variance

§ IQR

3. Hypothesis Testing


§ Basics of Hypothesis Testing

§ Significance

§ Terminologies in Hypothesis Testing

§ Null and Alternate Hypothesis

§ Test Statistics

§ P-value

§ Critical Value and decision

4. Correlation & Regression

§ Correlation and Covariance

§ Testing for correlation

§ Linear Regression

§ Coefficients

5. Multiple Regression

§ Hypothesis Testing and F-Test

§ Multiple Regression

§ Coefficients

Enroll in the course and become a Statistical Modeling expert today!

After completing this course successfully, you will be able to:

· Relate the concepts and theories for Statistical Modeling in various domains

· Understand and implement Python for building real world Statistical Models

· Understand evaluate the Statistical models

Who this course is for:

· People who want to advance their skills in applied Python

· People who want to master relation of Statistics with Python

· People who want to build customized Statistical Models for their applications

· People who want to implement Python algorithms for Statistical Models

· Individuals who are passionate about rule based and conversational Models

· Research Scholars


· Data Scientists

English
language

Content

Introduction

Course Introduction
Instructor
AI Sciences
Course Outline
Links for the Course’s Materials and Codes

Summary Statistics

Links for the Course’s Materials and Codes
Module Intoduction
Overview
Summary Statistics
Average Types
Mean
Median
Median Example
Mode
Case Study For Average
IQR
Variance
Standard Deviation
Averages in Python
Std Deviation and Variance in Python
IQR in Python

Correlation and Regression

Links for the Course’s Materials and Codes
Module Introduction
Covariance and Correlation
Correlation
Regression
Correlation and Covariance in Python
Entering Input
Linear Regression Results

Multiple Regression

Links for the Course’s Materials and Codes
Module Overview
Motivation for Multiple Regression
Formula for MR
Preparing the Data
Multiple Regression in Python

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