double quote Supercharge your career growth in Machine Learning

Multiple Variate Analysis

4.57
average rating

Ratings

Beginner

Level

1.5 Hrs

Learning hours

1.9K+

Learners

Skills you’ll Learn

About this Free Certificate Course

Data analysis is a domain that has a key role to play in almost all of the domains it involves in. To think of it, there are many ways to go about performing data analysis as there are numerous types of data that need to be collected, handled, and analyzed respectively. Multivariate analysis is important in today’s world of IT as it has the capability to pick up multiple points of data for observation at once and eventually even supply the same to a Machine Learning model to further amplify the effect of analysis and solve things quicker. Keeping this in mind, we here at Great Learning have come up with this course on multivariate analysis to help you completely grasp all of the content and get upto speed with all the foundationals that are required to proceed in this domain.

Check out our PG Course in Machine learning Today.

Why upskill with us?

check circle outline
1000+ free courses
In-demand skills & tools
access time
Free life time Access

Course Outline

Introduction to Data Analysis
Summary

This comprehensive module on Organizational Behaviour, covers the concept, importance, elements, levels, types, and objectives of the field. It provides a deep understanding of the subject to enhance interpersonal and communication skills and create a positive work environment.

Multivariate Analysis Course Agenda
Understanding Multivariate Analysis
Various MVA Techniques
Advantages of MVA
Demonstration in Python

Premium programs from top universities

Make the right decision for your career growth today!

KNOW MORE

Trusted by 10 Million+ Learners globally

What our learners say about the course

Find out how our platform helped our learners to upskill in their career.

4.57
Course Rating
75%
18%
5%
2%
0%

Ratings & Reviews of this Course

Reviewer Profile
Vishal Randhawa

5.0

“Detailed and Practical Course on Multivariate Analysis”
This course provided a comprehensive understanding of various multivariate analysis techniques, along with practical examples. The curriculum was well-structured, and the quizzes helped in reinforcing the concepts effectively. It’s an excellent course for anyone looking to explore advanced data analysis techniques.
linkedin profile

Multiple Variate Analysis

1.5 Learning Hours . Beginner

Why upskill with us?

check circle outline
1000+ free courses
In-demand skills & tools
access time
Free life time Access
10 Million+ learners

Success stories

Can Great Learning Academy courses help your career? Our learners tell us how.

And thousands more such success stories..

Frequently Asked Questions

What is multivariate analysis and how does it work?

In the realm of data analytics, we use various techniques to get desired outcomes in different situations. Multiple variant analysis is a range of statistical techniques that are used to undergo data analysis. This involves taking multiple measurements and then studying their relationship among different experiments to understand the outcomes better. Multivariate analysis was first started by Wishart in 1928, and since then, it has found its application in the fields of biology, education, and psychology. 

What is an example of multivariate analysis?

In the marketing industry, professionals often have to run a lot of techniques to undergo market research and analysis. When dealing with more than one variable, multivariate techniques help in reaching robust conclusions and add clarity to the research. Let us take an example of marketing research where a company wants to understand the impact of variable ‘sales’ on the other variable ‘advertisement expenditure.

 

We will apply the multivariate technique to understand the relation between these two variables and the impact of one on another. One way to achieve this is by making a list of sales through various advertising channels and how each advertising channel impacts the overall sales. The data and findings can be worked into a model to find certain patterns or correlations to understand the impact of these variables on various outcomes. This will help the company to make informed decisions and set an advertising budget accordingly for driving more sales. 

 

What is the goal of multivariate analysis?

The goals of multivariate analysis include :

  • The primary aim of doing multivariate analysis is to establish a correlation between multiple variables and predict outcomes.

  • These techniques help in sorting out data and simplifying it as much as possible. This helps in making informed decisions and drawing solid interpretations without altering the data structure.

  • Multiple variables used in multivariate techniques help significantly in the sorting of data. Different variables are grouped depending on their key features.

  • Multivariate analysis also helps to test and conduct statistical hypotheses. This helps us determine whether our assumptions are correct or not. 

How Can I learn multivariate analysis from scratch?

The Multiple Variate Analysis course at Great Learning is beginner-friendly and helps you learn multivariate techniques from scratch. The course is created by experienced professionals and will equip you with the required skills to master the key concepts of data analysis using multivariate analysis. The course is offered free of cost and is suitable for anyone who wants to learn Multivariate Analysis without any prior technical experience. 

Will I get a certificate after completing this Multiple Variate Analysis free course?

Yes, you will get a certificate of completion for Multiple Variate Analysis after completing all the modules and cracking the assessment. The assessment tests your knowledge of the subject and badges your skills.

How much does this Multiple Variate Analysis course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Multiple Variate Analysis can get started with this course.

Is there any limit on how many times I can take this free course?

Once you enroll in the Multiple Variate Analysis course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Multiple Variate Analysis course?

Great Learning Academy provides this Multiple Variate Analysis course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

What are the steps to enroll in this Multiple Variate Analysis course?

Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.

Will I have lifetime access to this free Multiple Variate Analysis course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to.

Recommended Free Machine Learning courses

Free
Application of Machine Learning in Finance
course card image

Free

Beginner

Free
Machine Learning Algorithms
course card image

Free

Beginner

Free
Predictive Analytics
course card image

Free

Beginner

Free
Python Project Ideas
course card image

Free

INTERMEDIATE

Similar courses you might like

Free
Logistic Regression
course card image

Free

Beginner

Free
Machine Learning Modelling
course card image

Free

Beginner

Free
Probability and Probability Distributions for Machine Learning
course card image

Free

Beginner

Free
Linear Regression
course card image

Free

Beginner

Related Machine Learning Courses

50% Average salary hike
Explore degree and certificate programs from world-class universities that take your career forward.
Personalized Recommendations
checkmark icon
Placement assistance
checkmark icon
Personalized mentorship
checkmark icon
Detailed curriculum
checkmark icon
Learn from world-class faculties

                                                             Multiple Variate Analysis

 

What Are The Benefits of The Multiple Variate Analysis Course?

Great Learning offers a beginner-friendly certificate course in Multiple Variate Analysis which offers the following benefits :

  • The course is ideal for aspiring data analysts or data science professionals who want to make a good career in this domain. 

  • The course has a comprehensive curriculum carefully designed by professionals in this field to help you guide through various concepts of multivariate analysis step-by-step before progressing to the advanced concepts.

  • The course covers topics such as data analysis, techniques of MVA, advantages of MVA, demonstration in Python, and more. You can access the course material as soon as you get enrolled in the course.

  • It is a self-paced course available free of cost to anyone who wants to learn the basics of MVA techniques. You can easily pursue this course along with your full-time job by spending around two to three hours a week.  

  • After successful completion of the course, you will also receive a shareable certificate. This can be attached to your resume to be shared with your existing or new employer to give you added advantage.

  • We also offer placement assistance to assist you in preparing for interviews n and enhance your career prospects. 

 

What are the prerequisites to enroll in this course?

Basic knowledge of Python basics and Python libraries for Data Science is recommended to enrol for this course but it is not mandatory. The course is ideal for beginners as well as professionals.

 

History of Multiple Variate Analysis 

In 1776, Joey-Lou Lagrange came up with the derivation of the first multivariate technique known as "The Multivariate Normal Distribution". Fast forward to 1827, Laplace adds multiple linear regression to the derivation. In 1928, Wishart presented a paper with multivariate analysis that contained multiple variables. Later in the 1930s, R.A.Fisher published theoretical work on MVA along with inferential statistics. Since then, MVA techniques have been used in fields of mathematics, psychology, education, biology, and more. 

 

Multivariate Analysis Techniques and Examples 

Broadly speaking, multivariate analysis techniques can be divided into two categories-

  • Dependence techniques 

  • Interdependence techniques 

 

Dependence Techniques 

Dependence techniques or methods are used when one or more variables share a dependent relationship with one another. These methods are used to understand the correlation between cause and effect. For example, let us take more than two variables that can be used to describe or predict the value of the third variable. The dependent variable ‘weight’ shares a relationship with two independent variables ‘height’ and ‘age.’

 

Dependence techniques are majorly used in machine learning. The data analyst uses the data to build predictive models. In this method, independent and dependent variables are defined to make accurate predictions. 

 

Interdependence Techniques 

These techniques are used to find out the underlying patterns and structural makeup within a data set. Since it does not contain any dependent variables, the data analyst is not looking for causal relationships between the variables. Instead, the variables are grouped in meaningful ways to understand the structure of a data set. 

 

Below are some examples of various MVA techniques used in data science -

  • Multiple Linear Regression : This involves a dependence method that contains both dependent and independent variables. The relationship between both these types of variables is explored to predict future outcomes accurately. For example, if a data analyst has to predict crop growth, the variable ‘crop growth’ is the dependent variable here. Now the effect of independent variables or factors is studied, which includes rainfall, temperature, humidity, and so on. The data analyst will prepare a multiple linear regression model that explores the proportion to which all these various factors influence crop growth.  

 

  •  Multivariate Analysis of Variance : This method is used to measure the correlation of independent variables with that of dependent variables. The independent variables here are categorical in nature, whereas dependent variables are metric. The metric variable usually has a numerical value, while the categorical variable denotes a category. 

 

  • Cluster Analysis : Cluster analysis is an interdependence technique that is used to group similar variables or data sets into a cluster. While grouping variables, data analyst ensures that the variables within a cluster are similar to each other. Afterward, intercluster and intracluster distances are also measured. The overall analysis helps to find patterns in the data and understand how the data is distributed.

 

  • For example, let us suppose that a marketing company wants to segment its customers for their targeted campaigns. Cluster analysis can be used here to segment different customers into clusters with similar attributes such as geographical locations or spending habits. 

 

  • Factor analysis Another interdependence technique is used in a data set with too many variables. In this method, the data analyst will prepare predictive models to find patterns in the data sets. While creating a model with many data sets, there is always a chance of overfitting. Overfitting is an error that reduces the chances of predicting an accurate outcome. By detecting data sets that are too closely related to each other, the data analyst can undergo factor analysis to analyze data sets and make subsequent predictions. 

 

  • For example, if there is a data set containing an individual’s income, education, occupation, etc., then these variables have a high degree of correlation between them. Therefore, these can be grouped into one variable - socioeconomic status. By reducing the variables into one group, we are not losing any crucial information. These variables adequately indicate attributes of individual variables. Once the data is streamlined, the data analyst can further analyze it to make predictions. 

 

  • Multiple logistic regression : This technique is used to predict a binary outcome which means that there can be only two outcomes. Using interdependent variables, the data analyst can find out whether an event will occur (1) or not (0). 

 

For example, an insurance firm wants to know whether each potential customer will make a claim or not. Using logistic regression analysis, the data analyst will include multiple interdependent variables such as age, income, occupation, health condition, and so on. These variables are then used to build a model to calculate the probability of the event or making a claim. 

Enrol for Free