Statistics: Statistical Modeling Made Easy for ALL

Description

Are you eager to create efficient statistical models for your business?

Do you wish to harness the power of statistical knowledge to analyze data and draw meaningful conclusions?

Would you like to distinguish between credible insights and questionable conclusions through quantitative evidence?

If your answer is yes, then this comprehensive course is tailor-made for you!

In statistical modeling, you apply statistical analysis to datasets, establishing mathematical relationships between random and non-random variables. This course is your complete guide, designed especially for beginners, to grasp the fundamentals of Statistical Modeling with Python and its practical applications. You'll learn to construct statistical models from the ground up, using Python to implement statistical concepts effectively. Each module presents engaging content that covers essential theoretical statistical concepts.

The third module focuses on hypothesis testing, a cornerstone of statistics. You'll not only learn key terminology but also gain insight into null and alternate hypotheses. To help you grasp the real-world application, we've included two case studies in this area.

The final two modules delve into regression, another critical statistical concept. Alongside statistical modeling, you'll also become proficient in Python, a language extensively used throughout the course.

Designed for beginners with basic programming knowledge and those entirely new to Data Analysis, Statistical Models, Statistics, and Python, this course offers exceptional value compared to similar courses that often cost hundreds of dollars. You'll access over 3 hours of HD video lectures, divided into 53 concise videos, complete with course materials and code links, making it one of the most comprehensive courses for Statistical Modeling with Python available.

Why Enroll in This Course?

This course simplifies complex statistical modeling principles, making them accessible to all learners.

Structured to emphasize the practical applications of Statistical Modeling in real-world scenarios, it offers a unique hands-on experience through three case studies. You'll master Statistical Modeling concepts and methodologies and apply them using

Python, making the learning process:

• Easy to understand

• Descriptive and self-explanatory

• Relevant and concise

• Practical with live coding

• A comprehensive package with three case studies

Our passionate teaching approach ensures that you not only understand the theoretical concepts but also gain practical experience. The course materials include high-quality video content, course materials and code links, handouts, and evaluation exercises. Our dedicated team is always ready to assist with any course-related queries. 

Course Content:

You'll learn Python programming and utilize statistical concepts to develop Statistical Models. Here are some topics you'll explore:

1. Course Overview

2. Overview of Summary Statistics:

   • Average

   • Mean, Mode, Median

   • Standard 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 and Regression:

   • Correlation and Covariance

   • Testing for Correlation

   • Linear Regression

   • Coefficients

5. Multiple Regression:

   • Hypothesis Testing and F-Test

   • Multiple Regression

   • Coefficients

Enroll in this course today and embark on your journey to becoming a Statistical Modeling expert!

Upon successful completion of this course, you will:

• Apply the concepts and theories of Statistical Modeling across various domains

• Create real-world Statistical Models and implement them using Python

• Understand and evaluate Statistical Models effectively

Who this course is for:

• Learners seeking to advance their applied Python skills

• Data Science Enthusiasts

• Research Scholars

• Data Scientists

• Individuals eager to master the relationship between Statistics and Python

• Those interested in building custom Statistical Models for their applications

• Individuals looking to implement Python algorithms for Statistical Models

• Enthusiasts passionate about rule-based and conversational models

List of Keywords:

• Statistical Modeling

• Python

• Data Analysis

• Hypothesis Testing

• Linear Regression

• Multiple Regression

• Correlation

• Data Science

• Statistical Models

• Python Programming

• Significance

• Null Hypothesis

• P-value

• Critical Value

• Data Scientists

• Summary Statistics

• Variance

• IQR

• Applied Python Skills

• Custom Statistical Models

TAKE THIS COURSE