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Understanding the Difference Between Factor and Cluster Analysis

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Understanding the Difference Between Factor and Cluster Analysis

Cluster analysis and factor analysis are two different statistical methods in data analytics which are used heavily in analytical methods of subjects like natural sciences and behavioural sciences. The names of these analytical methods are so because both these methods allow the users to divide the data into either clusters or into factors.

Most newly established data analysts have this common confusion that both these methods are almost similar. But while these two methods may look similar on the surface but they differ in several ways including their applications and objectives.

Difference in objectives between cluster analysis and factor analysis:

One key difference between cluster analysis and factor analysis is the fact that they have distinguished objectives. For factor analysis the usual objective is to explain the correlation with a data set and understand how the variables relate to each other. But on the other hand the objective of cluster analysis is to address the heterogeneity in the individual data sets.

Put in simpler words the spirit of cluster analysis is to help in categorization but that of factor analysis are a form of simplification.

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Difference is solutions:

This is not an easy section for drawing a line of separation in between cluster and factor analysis. That is because the results or solutions obtainable from both these analysis is subjective to their application. But still one could say that with factor analysis provides in a way the ‘best’ solutions to the researcher. This best solution is in the sense that the researcher can optimize a certain aspect of the solution this is known as orthogonality which offers ease of interpretation for the analysts.

But in case of cluster analysis this is not the case. The reasons behind that being all algorithms which can yield the best solutions for cluster analysis are usually computationally incompetent. Thus, researchers cannot trust this method of cluster analysis as it does not guarantee an optimal solution.

Difference in applications:

Cluster analysis and factor analysis differ in how they are applied to data, especially when it comes to applying them to real data. This is because factor analysis can reduce the unwieldy variables sets and boil them down to a smaller set of factors. This makes it suitable for simplifying otherwise complex models of analysis. Moreover, factor analysis also comes with a sort of confirmatory use researchers can use this method to develop a set of hypotheses based on how the variables in the data set are related.  After that the researcher can run a factor analysis to further confirm these hypotheses.

But cluster analysis on the other hand is suitable only for categorizing objects as per certain predetermined criteria. In cluster analysis a researcher can measure selected aspects of say a group of newly discovered plants and then place these plants into categories of species grouped by employing cluster analysis.

Here is an infographic to better explain the difference between cluster analysis and factor analysis: 

 

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Aspiring Data Analysts Must Know the Answer to These Interview Questions

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Aspiring data analysts must know the answer to these interview questions

You have recently completed Data analyst certification and are hunting vigorously for a job as a data scientist. But the prospect of sitting for such an important job role at a corporate firm in front of a room full of C-suite interviewers is an intimidating prospect. But fear not as we at DexLab Analytics have got you covered both inside the class room as well out.

This megatrend on Big Data analysts started first in 2013, when the leading universities of the world began to realize the gap in between the demand and supply of Big Data professionals. And soon several , Data analyst training institutes cropped up here and there and rooms transformed into classrooms with several students being keen to learn about the steps to handle Big Data  and to join the ranks of data scientists which is a highly sought after profession of these days. Continue reading “Aspiring Data Analysts Must Know the Answer to These Interview Questions”

Trending Data Job Role: Chief Data Officer

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Trending data job role: Chief Data Officer

Financial firms are going berserk in order to employ the best Chief Data Officers from around the world. This is the new hype in the C-suite world who wants to manage risks associated with data and also grasp its opportunities for conducting better business.

These days all financial firms are sincerely focused on maintaining their data and governing them to comply with the latest rules and regulations. They want to comply with customer demands to maintain their competitive edge and stay on top of the game. And in order to maintain this, the financial services teams are on a hyper drive in hiring the C-suite role of a Chief Data Officer i.e. CDO.

Recent developments in the regulatory mandates of Volcker Rule of the Dodd-Frank Act in relation to capital planning have made it difficult for financial organizations to aggregate and manage their data. In a recent stress test a large number of major US corporate banks and other financial institutions have failed as the quality of their data was not up to scratch.

But expert data analyst and scientists state that only regulatory compliance is not the main issue at hand. Effective risk management goes hand-in-hand with efficient data management. And firms are lacking that front as they do not manage their data effectively and are simply gambling with chances of a hug penalty at the risk of losing customers and acquiring a bad name in the business.

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The opportunities in this position of Chief Data Officer:

While the aspects of regulatory compliance and risk management are becoming more and more complex every day, but that is not the only reason to move up information management positions and invite them into the boardroom. That is why as most financial organizations know that good governance requires strong data management skills with good understanding of architecture and analytics. Companies have come to realize that this kind of information can prove to be effective and provide them with competitive advantage in terms of reaching out to customers and protecting them with the offering of innovative products and services.

According to latest research, experts predicted that 25 percent of every financial organization will have employed a Chief Data Officer by the end of 2015. The job responsibility of this role is still clouded and most organizations are trying to refine and boil it down, but as of now three main roles have been identified – data governance, data analysis and data architecture and technology. While according to this survey 77 percent of the CDOs will remain focused in governance focused but their responsibilities are likely to grow into other areas as well. The main objective behind data architecture is to oversee how data is sourced, integrated and then consumed in the global organizations. The way to lead efficiencies in this respect is to consider this aspect in depth. Thus, it can be concluded that data analytics has the most potential.

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Data analysis resources to keep you updated

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Data analysis resources to keep you updated

One should always be proactive about building upon what they already know and have learnt, and with explosion of the web such resources can be obtained fairly easily. The problem is not the availability of resources but the abundance from it. Due to the availability of too many choices it often becomes difficult to gauge if the sources are actually authentic.

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So, here is a list of books, websites and other resources which we think are authentic:

To stay on top of the latest trends and analyses reports and what’s new in the realm of analytics here are the best latest blogs:

  • FiveThirtyEight: the main man behind this blog is Nate Silver, a data whiz kid, this blog is the place to find out data analysis and visualizations of political, economic and cultural issues. The content in his blogs are usually light-hearted and interactive yet pointed with illustrative examples of data can be used in day-to-day activities.
  • Flowing Data: this is an interesting blog where Dr. Nathan Yau, PhD reveals how the data personnel – like designers, analysts, scientists and statisticians can analyze and visualize data to gather a better understanding of the world around us. It is especially fun to read as Yau offers a funny approach about the regular challenges faced by a data professional in this field. One can also find job recommendations, tutorials and other resources in this blog.
  • Simply statistics: this is another blog that is managed by expert professors each from Ivy League colleges like Johns Hopkins University, Harvard University and the Dana Ferber Cancer Institute. These professors also talk about how data is being used or misused around the world in different industries.
  • Hunch: this blog has been created by John Langford from Microsoft Research, he is the doctor of learning there and his blog talks about machine learning basics of what we know and how we use what we know. This is a good read for those who are new in the field of machine learning and do not yet know how things work in machine learning as it provides an in-depth view of new ideas and events going on in this industry.

To connect to other fellow data scientists and analysts to inquire about questions that may arise while you try the tread the treacherous roads of the data world, these are few communities of data analysts you can follow.

    1. Kaggle competitions: this is a popular community that all data scientists are likely to come across. This is a platform where one can find data prediction competitors. This is a platform where one can search for upcoming competitions in data analysis the website also features a forum where a visitor can ask any question or find a partner for the competition, share resources and ask for support to make a good career in data science.
    2. Metaoptimize: this is a question and answer community for people who are into machine learning, natural language processing, data mining and more. Badges are awarded as per votes on questions are awarded. Thus, making it becomes simpler for the visitors to discover the most popular helpful answers to the questions.
    3. Datatau: this website is best described as hacker news for data scientists and it lives up to this description to the last word. People share career advice with each other; interesting articles are shared amongst the users and then commented upon also the people here share useful information to those new to the world of data analytics.
    4. DexLab Analytics blogs: while DexLab Analytics is one of the leading data analytics training institute in Gurgaon, but they maintain regular blogs about the latest developments in the field of data science and provide India-specific as well global data related news. For students pursuing or aspiring to pursue a career in data science must follow the daily posts from this institute.

In conclusion we would like to add that while there are several resources from where one can obtain valuable information about data analysis. Thus, keeping this list as a starting point you can find several other experts out there to help you learn more about data analytics.

 

Interested in a career in Data Analyst?

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To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
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