One of the Best Online Correlation And Dependence Courses is Correlations, Association & Hypothesis Testing (with Python). In statistical analysis and machine learning, exploring and measuring the strength of connections between variables/features is crucial. The course's apps are all written in Python. Junior analysts as well as more seasoned data scientists will benefit from this training. This course will help you create the correct foundation at an early stage of your career if you are an aspiring/junior data analyst/scientist in particular.

This course will help you revisit and eventually enhance your grasp of the assessment of relationships between variables/features if you are a seasoned data scientist. Each part examines a variety of statistical measures in connection to variable correlations before constructing statistical hypothesis tests to assess the strength of these relationships. Throughout the course, there are practical sessions where you will learn how to implement the methods described (using Python) and run various hypothesis tests using real-world datasets.

A quiz is also included at the end of each section. These exams are designed to assist you in consolidating the primary topics taught in the course. You will have a clear and cohesive grasp of covariances, correlations, t-test, Chi-squared test, ANOVA, F-test, and much more at the end of the course.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 3 hours to complete
• Subtitles: English

Course Rating: 5/5
Enroll here: https://www.udemy.com/course/with-python-correlations-association-hypothesis-testing/

Introduction to Week 2: Correlation And Dependence is among the Best Online Correlation And Dependence Courses. Linear Regression in R for Public Health is now available! "The art and science of avoiding disease, extending life, and promoting health through the coordinated efforts of society," according to the definition of public health. Knowing what causes sickness and what makes it worse is obviously crucial. This necessitates the creation of statistical models that explain how patient and environmental variables influence their likelihood of being unwell.

This course will teach you how to build such models from the ground up, starting with an introduction to correlation and linear regression, then leading you through importing and analyzing data, and finally teaching you how to fit models. These models will show how patient and other variables impact outcomes such as lung function, using the example of respiratory illness.

The remaining courses in this series will cover two more members of the regression family, including linear regression. Regression models have a lot in common, even if the mathematical specifics are different. This course will teach you how to prepare data, evaluate the model's fit to the data, and verify the model's underlying assumptions - all of which are critical activities in any sort of regression.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 15 hours to complete
• Subtitles: English

Course Rating: 4.8/5
Enroll here: https://www.coursera.org/lecture/linear-regression-r-public-health/introduction-to-week-2-o2YAs

Before you proceed to run a regression model, you must first grasp the idea of correlation. This week, you'll learn what Pearson's and Spearman's correlation coefficients are and how to use them in R to measure the strength of the relationship between a risk factor or predictor and the patient result.

After that, you'll learn about linear regression and the concept of model assumptions, which is at the heart of much statistical analysis. The technique of determining the degree of association between two variables is known as correlation. Correlation is a powerful and fascinating statistical tool.

The correlation is one of the most straightforward descriptive statistics to grasp, and it's also one of the most often employed. The measurement of a relationship between two or more variables is referred to as correlation. This link is represented by a correlational coefficient, which is typically abbreviated as 'r.' A correlational coefficient, which can vary from –1.0 to +1.0, gives two key pieces of information about a relationship: Intensity and Direction.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 1 hours to complete
• Subtitles: English

Course Rating: 4.8/5
Enroll here: https://www.coursera.org/lecture/linear-regression-r-public-health/pearsons-correlation-part-i-QXM4o

Correlation, Regression and Curve Fitting is also one of the Best Online Correlation And Dependence Courses. This course covers the concepts of correlation, regression, and curve fitting, which are all highly valuable statistical techniques. Engineering students, management students, research scholars, mathematics scholars, and industry experts who desire to pursue a career in the fields of data science, data analytics, business analytics, machine learning, and artificial intelligence will find this course quite valuable.

This course will not only assist students in passing their university exams, but it will also provide the groundwork for competitive exams at the national and international levels. If you're an industry expert or a working professional, this will help you lay the groundwork for analyzing trends in datasets and making better decisions. The course's most appealing feature is that it does not require any prior knowledge of mathematics or statistics because all prerequisites are covered in the course.

This course also lays the groundwork for operational research, and if you're a management student, it will assist you in doing so. After completing this course, you will be able to recognize data trends, develop statistical models, and use them to aid in decision-making. As a result, if you work in the sector as a manager, this course will be beneficial to you.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 6 hours to complete
• Subtitles: English

Course Rating: 4.7/5
Enroll here: https://www.udemy.com/course/correlation-regression-and-curve-fitting/

In statistical analysis and machine learning, exploring and measuring the strength of connections between variables/features is crucial. Junior analysts as well as more seasoned data scientists will benefit from this training. This course will help you create the correct foundation at an early stage of your career if you are an aspiring/junior data analyst/scientist in particular. This course will help you revisit and eventually enhance your grasp of the assessment of relationships between variables/features if you are a seasoned data scientist.

Each part examines a variety of statistical measures in connection to variable correlations before constructing statistical hypothesis tests to assess the strength of these relationships. As one of the Best Online Correlation And Dependence Courses, the purpose of this Correlations, Associations and Hypothesis Testing (with R) is to apply the methodologies presented in the course (using R) and run various hypothesis tests on real-world datasets. You'll also study and master the art of interpreting data in a larger perspective.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 15 hours to complete
• Subtitles: English

Course Rating: 4.7/5
Enroll here: https://www.udemy.com/course/correlations-associations-and-hypothesis-testing-with-r/

It's no secret that a number of these subjects have been thoroughly discussed on the internet. Hundreds of times. It is, however, very hard to discover an organized program that explains why specific statistical tests are utilized so frequently. Although modern software packages and computer languages automate the majority of these tasks, this course provides you with something much more valuable: critical thinking skills. Computers and programming languages function similarly to ships at sea. They are excellent vessels that will transport you to your intended location; nevertheless, it is up to you, the aspiring data scientist or BI analyst, to navigate and steer them in the appropriate way.

What distinguishes this course from the rest of the statistics courses available? It's a high-definition video and animation production (this isn't a collection of dull lectures!). There's also a knowledgeable instructor (An adept mathematician and statistician who has competed at an international level). It covers all of the important correlation statistical concepts and abilities you'll need to work as a marketing analyst, a business intelligence analyst, a data analyst, a data scientist, or a data scientist. It also includes extensive case studies to help you remember what you've studied. Next, outstanding customer service: if you have a question or just wish to contact us, you will receive a response within one business day.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 5 hours to complete
• Subtitles: English [Auto], Dutch [Auto] , French [Auto], German [Auto], Indonesian [Auto], Italian [Auto], Polish [Auto], Portuguese [Auto], Romanian [Auto], Spanish [Auto]

Course Rating: 4.6/5
Enroll here: https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/

There are no prerequisites for this course. This course is for people who are new to data science or have only a little familiarity with it. This course will also assist students who need a review or refresher in correlation analysis or Python. There is no need to install any program. A professional data scientist with over 20 years of expertise in teaching and education will lead the class.

Dr. Adam Ross Nelson has worked with pupils of all ages and from all corners of the globe. He holds a doctorate in education. He's worked with high school pupils, college students, graduate students, and working professionals. He also commonly assists technical people in entering and progressing through the data science field. Students can use knowledge checks to measure their own learning. A capstone experience is also available. The capstone project will require students to build correlation analysis in Python from the ground up. There are no prerequisites for this course.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 2.5 hours to complete
• Subtitles: English

Course Rating: 4.5/5
Enroll here: https://www.udemy.com/course/data-science-quick-start/

Finding patterns in data using machine learning algorithms is one of the most intriguing areas of business analytics. This course will provide you a basic understanding of why machine learning algorithms are so vital, as well as how the models that come from those algorithms are utilized to obtain meaningful insight into business challenges. Some algorithms are used to forecast quantitative outputs, while others are intended to anticipate how an outcome will be classified. For constructing meaningful groupings from a large amount of data, several methods are utilized. You will be able to define when each algorithm should be used after completing this course.

You'll also have the chance to run these algorithms with R and RStudio and discuss the findings with R notebooks. You will study about Regression Algorithm for Testing and Predicting Business Data in Course Orientation and Module 1. Exploratory data analysis (EDA) is an important phase in the business analytic process, but it is a time-consuming method for discovering complicated associations. Furthermore, the visualizations commonly employed for EDA are not well suited to assessing confidence in results or generating predictions.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 1 hours to complete
• Subtitles: English

Course Rating: 4.4/5
Enroll here: https://www.coursera.org/lecture/machine-learning-algorithms-r-business-analytics/correlation-iaclH

In this course, you will learn about the basics of probability and how it relates to statistics and data analytics. You can learn how to compute probabilities, the difference between independent and dependent outcomes, and conditional events. Let's look at discrete and continuous random variables, as well as how they relate to data collecting. You finish the course with learning about Gaussian (normal) random variables and the Central Limit Theorem, as well as its usefulness in statistics and data science.

This course is part of CU Boulder's Master of Science in Data Science (MS-DS) program, which is available on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together academics from the Applied Mathematics, Computer Science, Information Science, and other departments at CU Boulder. The MS-DS is excellent for persons with a broad variety of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics, as it is based on performance rather than application.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 1 hours to complete
• Subtitles: English

Course Rating: 4.2/5
Enroll here: https://www.coursera.org/lecture/probability-theory-foundation-for-data-science/covariance-and-correlation-aL9HY

This course will educate you to comprehend two fundamental statistical concepts, Correlation and Regression, as well as Six Sigma. This course is divided into five components. Correlation, Correlation Analysis, Calculating Correlation Coefficient, Practical Application of Correlation and Regression with an Example, Regression, Significance F and p-values, Coefficients, Residual, and Conclusion make up Section 1. The second section covers regression analysis, as well as the practical application of each regression analysis with an example and the usage of Minitab to do regression analysis. Nonlinear Regression Analysis is covered in Section 3.

This course will teach you how to calculate the correlation coefficient using Karl Pearson's approach, Spearman's Rank Difference Method, and the Method of Concurrent Deviations. Several major Karl Pearson approaches, including as the Direct Method and the Assumed Mean Method, have been thoroughly explored here. Correlation in Grouped Series was also thoroughly described. Both ways with different rankings and situations with the same ranks have been detailed in Spearman's Method, as well as the Method of Concurrent Deviations.

This course offers:

• Flexible deadlines: Reset deadlines in accordance to your schedule.
• Certificate: Earn a Certificate upon completion
• 100% online
• Beginner Level
• Approx. 6.5 hours to complete
• Subtitles: English

Course Rating: 4.1/5
Enroll here: https://www.udemy.com/course/statistics-for-data-analytics/