Statistical Modeling Explained Using Python

Learn complete Statistical Analysis Alongside Regression Analysis in Python

What you'll 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…

Requirements

• No prior knowledge of Statistical Modeling, Data Analysis or Mathematics is needed. We will start from the basics and gradually build your knowledge in the subject

• A willingness to learn and practice

• Only basic Python is required

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 contentsTeaching 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 Overview2. Overview of Summary Statistics§ Average§ Mean, Mode, Median§ Std. Deviation§ Variance§ IQR3. Hypothesis Testing§ Basics of Hypothesis Testing§ Significance§ Terminologies in Hypothesis Testing§ Null and Alternate Hypothesis§ Test Statistics§ P-value§ Critical Value and decision4. Correlation & Regression§ Correlation and Covariance§ Testing for correlation§ Linear Regression§ Coefficients5. Multiple Regression§ Hypothesis Testing and F-Test§ Multiple Regression§ CoefficientsEnroll 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 modelsWho 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

Overview

Section 1: Introduction

Lecture 1 Course Introduction

Lecture 2 Instructor

Lecture 3 AI Sciences

Lecture 4 Course Outline

Lecture 5 Links for the Course's Materials and Codes

Section 2: Summary Statistics

Lecture 6 Links for the Course's Materials and Codes

Lecture 7 Module Intoduction

Lecture 8 Overview

Lecture 9 Summary Statistics

Lecture 10 Average Types

Lecture 11 Mean

Lecture 12 Median

Lecture 13 Median Example

Lecture 14 Mode

Lecture 15 Case Study For Average

Lecture 16 IQR

Lecture 17 Variance

Lecture 18 Standard Deviation

Lecture 19 Averages in Python

Lecture 20 Std Deviation and Variance in Python

Lecture 21 IQR in Python

Section 3: Hypothesis Testing

Lecture 22 Links for the Course's Materials and Codes

Lecture 23 Module Introduction

Lecture 24 Hypothesis Testing Overview

Lecture 25 Terminologies in Hypothesis Testing

Lecture 26 Null Hypothesis

Lecture 27 Alternate Hypothesis

Lecture 28 Test Statistics

Lecture 29 P-Value

Lecture 30 Critical Value

Lecture 31 Level of Significance

Lecture 32 Case Study 1

Lecture 33 Case Study 2

Lecture 34 Calculations for Python

Lecture 35 Steps of Hypothesis Testing

Lecture 36 Code Outcomes

Lecture 37 Calculation of Z in Python

Lecture 38 Norm Function

Lecture 39 P Value Python

Section 4: Correlation and Regression

Lecture 40 Links for the Course's Materials and Codes

Lecture 41 Module Introduction

Lecture 42 Covariance and Correlation

Lecture 43 Correlation

Lecture 44 Regression

Lecture 45 Correlation and Covariance in Python

Lecture 46 Entering Input

Lecture 47 Linear Regression Results

Section 5: Multiple Regression

Lecture 48 Links for the Course's Materials and Codes

Lecture 49 Module Overview

Lecture 50 Motivation for Multiple Regression

Lecture 51 Formula for MR

Lecture 52 Preparing the Data

Lecture 53 Multiple Regression in Python

• 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