# What Is Factor Analysis in Data Science?

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Factor analysis is a part of the general linear model (GLM). It is a method in which large amounts of data are collected and reduced in size to a smaller dataset. This reduction in the size of the dataset ensures that the data is manageable and easily understood by people.

In addition to manageability and interpretability, it helps extract patterns in data as well as show the characteristics that are commonly seen in the different patterns (that are extracted)It helps create a  variable set for data points in the datasets that are similar. This similar set of data is also known as dimensions.

## Assumption

An assumption while dealing with factor analysis is that, in a collection of the variables observed, there is a set of underlying variables, which is known as ‘factor’. This factor helps explain the inter-relationship between these variables.

• There should be a linear relationship between the variables in the data.
• There should be no multicollinearity between variables in the data.
• There should be true correlation between the variables and factors in the data.

There are multiple methods to extract factors from data, but principal component analysis is one of the most frequently used methods. In Principal component analysis (PCA), maximum variance is extracted and placed in the first factor. Once this is done, the variance explained by the first set of factors is eliminated and then maximum variance is again extracted for the second factor. This goes on until the last factor in the variable set.

## Types of factor analysis

The word ‘factor’ in factor analysis refers to the variable set which has similar patterns. They are sometimes associated with a hidden variable, which is also known as confounding variable. This hidden variable is not measured directly. The ‘factors’ talk about the variation in data which can be explained.

There are two types of factors:

1. Exploratory;
2. Confirmatory

### Exploratory factor analysis

This deals with data that is unstructured or when the person/s dealing with the data are clueless about the structure of the data and the dimensions of the variable associated with the data. Exploratory factor analysis gives information about the optimum number of factors which may be required to represent the data. If a researcher wishes to explore patterns, it is suggested to use exploratory factor analysis.

### Confirmatory factor analysis

This kind of analysis is used to verify the structure of the data, given the condition that the people dealing with the data are aware of its structure and dimensions of the variable associated with the data. This kind of analysis helps specify the number of factors required to perform the analysis.

Factor analysis is a multivariate method- this means it deals with multiple variables associated with data. This is a data reduction technique wherein the basic idea is to use a smaller set of variables, which is known as ‘factors’, that is a representation of a bigger set of variables.

It helps the researcher in understanding whether the relationship between the observed variables (aka manifest variables) and their underlying construct exists or not.

If a researcher wishes to perform hypothesis testing, it is suggested to use exploratory factor analysis.

## What are factors?

Factors can be understood as a construct which can’t be measured with the help of a single variable. Factor analysis is generally used with interval data, but it can be used for ordinal data as well.

## What is ordinal data?

Ordinal data is statistical data in which variables exist in naturally occurring categories that are in a particular order. The distance between categories in ordinal data can’t be found using ordinal data itself.

For a dataset to be ordinal data, it needs to fulfil few conditions.

1. Multiple terms in the dataset are in an ordered fashion.
2. The difference between variables in the dataset is not homogeneous/uniform.

A group of ordinal numbers indicates ordinal data, and a group of ordinal data can be represented using an ordinal scale.

Likert Scale is one type of ordinal data. Let us understand Likert scale with the help of an example:

Suppose we have a question that says “Please indicate how satisfied you are with this product purchase”. A Likert scale may have numbers between 0/1 to 5 or 0/1 to 10. On this scale, 0/1 indicates a lesser value and 5 or 10 indicates a higher value.

Let us understand ordinal data with the help of another example. If we have variables stored in a specific order, say “low, medium, high” or “not happy, slightly happy, happy, very happy, extremely happy”, it is considered as ordinal data.

## Conditions for variables in factor analysis

These variables (in factor analysis) need to be linearly associated with each other. Linear relationship or association describes a relationship that forms a straight line when two variables are plotted on a graph. It can also be represented as a mathematical equation in the form ‘y = mx + b’.

This linear associativity can be checked by plotting scatterplots of the pairs of variables. This indicates that the variables need to be moderately correlated to each other.

If the variables are not correlated, the number of factors will be the same as the number of original variables. This means that performing factor analysis on this kind of variables would be useless.

## How can factor analysis be performed?

Factor analysis is a complex mathematical procedure. It can be performed with the help of software applications. Before performing the analysis, it is essential to check if the data is relevant. This can be done with the help of Kaiser-Meyer-Olkin test.

## Kaiser-Meyer-Olkin test

This is also known as the KMO test, which is used to see how well the data is suited to perform factor analysis. It measures the sampling adequacy for every variable in the model.

This statistic measures the proportion of variance among all the variables in the data. The lower the proportion, more suited the data is to perform factor analysis.

• KMO returns values between 0 and 1.
• If KMO value lies between 0.8 and 1, it means that the sampling is adequate.
• If KMO value is less than 0.6 or lies between 0.5 and 0.6, it means that the sampling is not adequate. This means proper actions need to be taken.
• If KMO value is closer to 0, this indicates that the data contains large number of partial correlations in comparison to the sum of correlations. This is not suited for factor analysis.
• Values between 0 and 0.49 are considered unacceptable.
• Values between 0.50 and 0.59 are considered not good.
• Values between 0.60 and 0.69 are considered mediocre.
• Values between 0.70 and 0.79 are considered to be good.
• Values between 0.80 and 0.89 are considered to be great.
• Values between 0.90 and 1.00 are considered to be absolutely fantastic.

The formula to perform KMO test is:

Here, R =  which is the correlation matrix;

and U =  which is the partial covariance matrix.

Once the relevant data has been collected, factor analysis can be performed in a variety of ways.

### Using Stata

It can be performed in Stata with the help of postestimation command- ‘estat kmo.

### Using R

It can be performed in R using the command ‘KMO(r)’ where ‘r’ refers to the correlation matrix that needs to be analysed.

### Using SPSS

SPSS is a statistical platform that can be used to run factor analysis. First go to Analyze -> Dimension Reduction -> Factor, and check the “KMO and Bartlett’s test of sphericity” box.

If the measure of sampling adequacy (MSA) for single variable is needed, the ‘”anti-image” box needs to be checked. An ‘anti-image’ box shows the MSAs listed in diagonals of matrix.

The test can also be executed by specifying KMO in the Factor Analysis command. The KMO statistic is found in the “KMO and Bartlett’s Test” table in the Factor output.

#### Conclusion

In short, Factor Analysis brings in simplicity after reducing variables. Factor Analysis, including Principal Component Analysis, is also often used along with segmentation studies. In this post, we understood about the factor analysis method, and the assumptions made before working on the method. We also saw different kinds of factor analysis, and how they can be performed on different platforms.

### Dipayan Ghatak

Project Manager

Leading Projects across geographies in Microsoft Consultant Services.

What is data analytics?In the world of IT, every small bit of data count; even information that looks like pure nonsense has its significance. So, how do we retrieve the significance from this data? This is where Data Science and analytics comes into the picture.  Data Analytics is a process where data is inspected, transformed and interpreted to discover some useful bits of information from all the noise and make decisions accordingly. It forms the entire basis of the social media industry and finds a lot of use in IT, finance, hospitality and even social sciences. The scope in data analytics is nearly endless since all facets of life deal with the storage, processing and interpretation of data.Why data analytics? Data Analytics in this Information Age has nearly endless opportunities since literally everything in this era hinges on the importance of proper processing and data analysis. The insights from any data are crucial for any business. 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It is for those candidates who have a master’s degree in a field related to data analytics. It is one of the few vendor-neutral certifications on the list and must be converted to CAP within 6 years, so it offers a good opportunity for those with a long term path in a Data Analytics career. It also needs to be renewed every three years, like the CAP certification. Like its professional counterpart, aCAP helps a candidate step out in a vendor-neutral manner and drastically increases their professional credibility. Prerequisite: Master’s degree in any discipline related to data Analytics. Exam Fee: The base price is$300. For individuals who are members of INFORMS the price is $200. (Source). There is an extensive syllabus which covers: i. Business Problem Framing, ii. Analytics Problem Framing, iii. Data, iv. Methodology Selection, v. Model Building, vi. Deployment, vii. Lifecycle Management of the Analytics process, problem-solving, data science and visualisation and much more.4. 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Exam Fee: The cost for each exam is$180. (Source) An exception is Predictive Modelling using the SAS Enterprise Miner, costing $250, This exam can be taken in the English language. One can join the SAS Academy for Data Science and also take a practice exam beforehand. Salary: You can get a job as a SAS Data Analyst that pays up to$90,000 per year! (Source) 5. IBM Data Science Professional CertificateWhenever someone studies the history of a computer, IBM (International Business Machines) is the first brand that comes up. IBM is still alive and kicking, now having forayed into and becoming a major player in the Big Data segment. The IBM Data Science Professional certificate is one of the beginner-level certificates if you want to sink your hands into the world of data analysis. 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It is also behind the SAS Institute for Data Science. Hence, SAS is the organisation you would want to go to if you're aiming for a long-term career in data science. Conclusion To conclude, big data and data Analytics are a field of endless opportunities. By investing in the right credential, one can pave the way to a viable and lucrative career path. Beware though, there are lots of companies that provide certifications, but only recognised and reputed credentials will give you the opportunities you are seeking. Hiring companies look for these certifications as a mark of authenticity of your hands-on experience and the amount of work you can handle effectively. Therefore, the credential you choose for yourself plays a vital role in the career you can have in the field of Data analytics. Happy learning! 5631 Top Data Analytics Certifications What is data analytics?In the world of IT, every s... Read More ## Why Should You Start a Career in Machine Learning? If you are even remotely interested in technology you would have heard of machine learning. In fact machine learning is now a buzzword and there are dozens of articles and research papers dedicated to it. Machine learning is a technique which makes the machine learn from past experiences. Complex domain problems can be resolved quickly and efficiently using Machine Learning techniques. We are living in an age where huge amounts of data are produced every second. This explosion of data has led to creation of machine learning models which can be used to analyse data and to benefit businesses. This article tries to answer a few important concepts related to Machine Learning and informs you about the career path in this prestigious and important domain.What is Machine Learning?So, here’s your introduction to Machine Learning. This term was coined in the year 1997. “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences.”, as defined in the book on ML written by Mitchell in 1997. The difference between a traditional programming and programming using Machine Learning is depicted here, the first Approach (a) is a traditional approach, and second approach (b) is a Machine Learning based approach.Machine Learning encompasses the techniques in AI which allow the system to learn automatically looking at the data available. While learning, the system tries to improve the experience without making any explicit efforts in programming. Any machine learning application follows the following steps broadlySelecting the training datasetAs the definition indicates, machine learning algorithms require past experience, that is data, for learning. So, selection of appropriate data is the key for any machine learning application.Preparing the dataset by preprocessing the dataOnce the decision about the data is made, it needs to be prepared for use. Machine learning algorithms are very susceptible to the small changes in data. To get the right insights, data must be preprocessed which includes data cleaning and data transformation. Exploring the basic statistics and properties of dataTo understand what the data wishes to convey, the data engineer or Machine Learning engineer needs to understand the properties of data in detail. These details are understood by studying the statistical properties of data. Visualization is an important process to understand the data in detail.Selecting the appropriate algorithm to apply on the datasetOnce the data is ready and understood in detail, then appropriate Machine Learning algorithms or models are selected. The choice of algorithm depends on characteristics of data as well as type of task to be performed on the data. The choice also depends on what kind of output is required from the data.Checking the performance and fine-tuning the parameters of the algorithmThe model or algorithm chosen is fine-tuned to get improved performance. If multiple models are applied, then they are weighed against the performance. The final algorithm is again fine-tuned to get appropriate output and performance.Why Pursue a Career in Machine Learning in 2021?A recent survey has estimated that the jobs in AI and ML have grown by more than 300%. Even before the pandemic struck, Machine Learning skills were in high demand and the demand is expected to increase two-fold in the near future.A career in machine learning gives you the opportunity to make significant contributions in AI, the future of technology. All the big and small businesses are adopting Machine Learning models to improve their bottom-line margins and return on investment. The use of Machine Learning has gone beyond just technology and it is now used in diverse industries including healthcare, automobile, manufacturing, government and more. This has greatly enhanced the value of Machine Learning experts who can earn an average salary of$112,000.  Huge numbers of jobs are expected to be created in the coming years.  Here are a few reasons why one should pursue a career in Machine Learning:The global machine learning market is expected to touch \$20.83B in 2024, according to Forbes.  We are living in a digital age and this explosion of data has made the use of machine learning models a necessity. Machine Learning is the only way to extract meaning out of data and businesses need Machine Learning engineers to analyze huge data and gain insights from them to improve their businesses.If you like numbers, if you like research, if you like to read and test and if you have a passion to analyse, then machine learning is the career for you. Learning the right tools and programming languages will help you use machine learning to provide appropriate solutions to complex problems, overcome challenges and grow the business.Machine Learning is a great career option for those interested in computer science and mathematics. They can come up with new Machine Learning algorithms and techniques to cater to the needs of various business domains.As explained above, a career in machine learning is both rewarding and lucrative. There are huge number of opportunities available if you have the right expertise and knowledge. On an average, Machine Learning engineers get higher salaries, than other software developers.Years of experience in the Machine Learning domain, helps you break into data scientist roles, which is not just among the hottest careers of our generation but also a highly respected and lucrative career. Right skills in the right business domain helps you progress and make a mark for yourself in your organization. For example, if you have expertise in pharmaceutical industries and experience working in Machine learning, then you may land job roles as a data scientist consultant in big pharmaceutical companies.Statistics on Machine learning growth and the industries that use MLAccording to a research paper in AI Multiple (https://research.aimultiple.com/ml-stats/), the Machine Learning market will grow to 9 Billion USD by the end of 2022. There are various areas where Machine Learning models and solutions are getting deployed, and businesses see an overall increase of 44% investments in this area. North America is one of the leading regions in the adoption of Machine Learning followed by Asia.The Global Machine Learning market will grow by 42% which is evident from the following graph. Image sourceThere is a huge demand for Machine Learning modelling because of the large use of Cloud Based Applications and Services. The pandemic has changed the face of businesses, making them heavily dependent on Cloud and AI based services. Google, IBM, and Amazon are just some of the companies that have invested heavily in AI and Machine Learning based application development, to provide robust solutions for problems faced by small to large scale businesses. Machine Learning and Cloud based solutions are scalable and secure for all types of business.ML analyses and interprets data patterns, computing and developing algorithms for various business purposes.Advantages of Machine Learning courseNow that we have established the advantages of perusing a career in Machine Learning, let’s understand from where to start our machine learning journey. The best option would be to start with a Machine Learning course. There are various platforms which offer popular Machine Learning courses. One can always start with an online course which is both effective and safe in these COVID times.These courses start with an introduction to Machine Learning and then slowly help you to build your skills in the domain. Many courses even start with the basics of programming languages such as Python, which are important for building Machine Learning models. Courses from reputed institutions will hand hold you through the basics. Once the basics are clear, you may switch to an offline course and get the required certification.Online certifications have the same value as offline classes. They are a great way to clear your doubts and get personalized help to grow your knowledge. These courses can be completed along with your normal job or education, as most are self-paced and can be taken at a time of your convenience. There are plenty of online blogs and articles to aid you in completion of your certification.Machine Learning courses include many real time case studies which help you in understanding the basics and application aspects. Learning and applying are both important and are covered in good Machine Learning Courses. So, do your research and pick an online tutorial that is from a reputable institute.What Does the Career Path in Machine Learning Look Like?One can start their career in Machine Learning domain as a developer or application programmer. But the acquisition of the right skills and experience can lead you to various career paths. Following are some of the career options in Machine Learning (not an exhaustive list):Data ScientistA data scientist is a person with rich experience in a particular business field. A person who has a knowledge of domain, as well as machine learning modelling, is a data scientist. Data Scientists’ job is to study the data carefully and suggest accurate models to improve the business.AI and Machine Learning EngineerAn AI engineer is responsible for choosing the proper Machine Learning Algorithm based on natural language processing and neural network. They are responsible for applying it in AI applications like personalized advertising.  A Machine Learning Engineer is responsible for creating the appropriate models for improvement of the businessData EngineerA Data Engineer, as the name suggests, is responsible to collect data and make it ready for the application of Machine Learning models. Identification of the right data and making it ready for extraction of further insights is the main work of a data engineer.Business AnalystA person who studies the business and analyzes the data to get insights from it is a Business Analyst. He or she is responsible for extracting the insights from the data at hand.Business Intelligence (BI) DeveloperA BI developer uses Machine Learning and Data Analytics techniques to work on a large amount of data. Proper representation of data to suit business decisions, using the latest tools for creation of intuitive dashboards is the role of a BI developer.  Human Machine Interface learning engineerCreating tools using machine learning techniques to ease the human machine interaction or automate decisions, is the role of a Human Machine Interface learning engineer. This person helps in generating choices for users to ease their work.Natural Language Processing (NLP) engineer or developerAs the name suggests, this person develops various techniques to process Natural Language constructs. Building applications or systems using machine learning techniques to build Natural Language based applications is their main task. They create multilingual Chatbots for use in websites and other applications.Why are Machine Learning Roles so popular?As mentioned above, the market growth of AI and ML has increased tremendously over the past years. The Machine Learning Techniques are applied in every domain including marketing, sales, product recommendations, brand retention, creating advertising, understanding the sentiments of customer, security, banking and more. Machine learning algorithms are also used in emails to ease the users work. This says a lot, and proves that a career in Machine Learning is in high demand as all businesses are incorporating various machine learning techniques and are improving their business.One can harness this popularity by skilling up with Machine Learning skills. Machine Learning models are now being used by every company, irrespective of their size--small or big, to get insights on their data and use these insights to improve the business. As every company wishes to grow faster, they are deploying more machine learning engineers to get their work done on time. Also, the migration of businesses to Cloud services for better security and scalability, has increased their requirement for more Machine Learning algorithms and models to cater to their needs.Introducing the Machine learning techniques and solutions has brought huge returns for businesses.  Machine Learning solution providers like Google, IBM, Microsoft etc. are investing in human resources for development of Machine Learning models and algorithms. The tools developed by them are popularly used by businesses to get early returns. It has been observed that there is significant increase in patents in Machine Learning domains since the past few years, indicating the quantum of work happening in this domain.Machine Learning SkillsLet’s visit a few important skills one must acquire to work in the domain of Machine Learning.Programming languagesKnowledge of programming is very important for a career in Machine Learning. Languages like Python and R are popularly used to develop applications using Machine Learning models and algorithms. Python, being the simplest and most flexible language, is very popular for AI and Machine Learning applications. These languages provide rich support of libraries for implementation of Machine Learning Algorithms. A person who is good in programming can work very efficiently in this domain.Mathematics and StatisticsThe base for Machine Learning is mathematics and statistics. Statistics applied to data help in understanding it in micro detail. Many machine learning models are based on the probability theory and require knowledge of linear algebra, transformations etc. A good understanding of statistics and probability increases the early adoption to Machine Learning domain.Analytical toolsA plethora of analytical tools are available where machine learning models are already implemented and made available for use. Also, these tools are very good for visualization purposes. Tools like IBM Cognos, PowerBI, Tableue etc are important to pursue a career as a  Machine Learning engineer.Machine Learning Algorithms and librariesTo become a master in this domain, one must master the libraries which are provided with various programming languages. The basic understanding of how machine learning algorithms work and are implemented is crucial.Data Modelling for Machine Learning based systemsData lies at the core of any Machine Learning application. So, modelling the data to suit the application of Machine Learning algorithms is an important task. Data modelling experts are the heart of development teams that develop machine learning based systems. SQL based solutions like Oracle, SQL Server, and NoSQL solutions are important for modelling data required for Machine Learning applications. MongoDB, DynamoDB, Riak are some important NOSQL based solutions available to process unstructured data for Machine Learning applications.Other than these skills, there are two other skills that may prove to be beneficial for those planning on a career in the Machine Learning domain:Natural Language processing techniquesFor E-commerce sites, customer feedback is very important and crucial in determining the roadmap of future products. Many customers give reviews for the products that they have used or give suggestions for improvement. These feedbacks and opinions are analyzed to gain more insights about the customers buying habits as well as about the products. This is part of natural language processing using Machine Learning. The likes of Google, Facebook, Twitter are developing machine learning algorithms for Natural Language Processing and are constantly working on improving their solutions. Knowledge of basics of Natural Language Processing techniques and libraries is must in the domain of Machine Learning.Image ProcessingKnowledge of Image and Video processing is very crucial when a solution is required to be developed in the area of security, weather forecasting, crop prediction etc. Machine Learning based solutions are very effective in these domains. Tools like Matlab, Octave, OpenCV are some important tools available to develop Machine Learning based solutions which require image or video processing.ConclusionMachine Learning is a technique to automate the tasks based on past experiences. This is among the most lucrative career choices right now and will continue to remain so in the future. Job opportunities are increasing day by day in this domain. Acquiring the right skills by opting for a proper Machine Learning course is important to grow in this domain. You can have an impressive career trajectory as a machine learning expert, provided you have the right skills and expertise.
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Why Should You Start a Career in Machine Learning?

If you are even remotely interested in technology ... Read More