Statistical Modeling Using Python

Comprehensive Course Description:

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

2. Do you want to learn from data and present your findings with statistical knowledge?

3. Do you want to differentiate between reasonable and doubtful conclusions based on quantitative evidence?

Then this short detailed course is for you!

In statistical modeling, you apply statistical analysis to datasets. And in a statistical model, there’s a mathematical relation between random and non-random variables. Social media data, public health data, and census data are some examples of datasets for statistical analysis.

This course is a whole package for beginners to learn the basics of Statistical Modeling with Python and its applications. You will learn how to build a statistical model from scratch by using statistical concepts with Python. The engaging content in each module covers the fundamental theoretical statistical concepts.

The third module covers hypothesis testing, which is the most essential procedure in statistics. In addition to learning about the terminologies in hypothesis testing, you also learn about null hypothesis and alternate hypothesis. Two case studies on this topic have also been included to help you understand how statistical modeling works in the real world.

The last two modules cover regression, another core statistics concept. You can also become proficient in Python, as this programming language is used extensively in all the modules.

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

You can compare this comprehensive course to other Statistical Models Development with Python courses that usually cost hundreds of dollars. The good news is you can now learn all that information at a fraction of the cost! With over 3 hours of HD video lectures that are divided into 53 smaller videos and links to the course materials and codes, this is one of the most exhaustive courses for Statistical Modeling with Python on this platform!

Why Enroll in This Course?

Complex statistical modeling principles have been simplified in this course.

The course is structured to help you understand the role and impact of Statistical Modeling in real-world applications. In addition, three case studies provide a unique hands-on experience in developing complete Statistical Models for your customized dataset. This concise learning by doing course will help you master Statistical Modeling concepts and methodologies with regard to implementation in Python.

This course is:

· Easy to grasp.

· Descriptive and self-explanatory.

· Relevant and to the point.

· Practical with live coding.

· A full package with three case studies.

Passionate Teaching Makes Learning Effective, Easy, and Fun

The online tutorials have been designed to encourage learning by doing. You get more than a superficial look at the Statistical Models covered. For instance, this course has three case studies that will help you understand—via experimentation—the practical implementation of Statistical Modeling with Python on real-world datasets.

The instructor has worked extra hard to make sure you understand the theoretical concepts clearly. As a result, you will get a sound understanding of the basics before moving onward to complex concepts. The course-related materials include high-quality video content, links to course materials and codes, handouts, and evaluation exercises. Get in touch with our friendly, passionate team in case of any course-related doubts.

Course Content:

You’ll learn how to program with Python and how to use statistical concepts to develop Statistical Models! Here are just a few of the topics that you will be learning:

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 and become a Statistical Modeling expert today!

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

• · Relate the concepts and theories of Statistical Modeling in various domains.

• · Understand building real-world Statistical Models and implementing them in Python.

• · Understand and evaluate Statistical Models.

   Learners who want to advance their skills in applied Python.
  
  

  Who this course is for:

• · Data Science Enthusiasts.

• · Research Scholars.

• · Data Scientists.

• · People who want to master the relationship 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.

who this course is for:

• • Learners who want to advance their skills in applied Python.
• • Data Science Enthusiasts.
• • Research Scholars.
• • Data Scientists.
• • People who want to master the relationship 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.