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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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Interesting Statistics of Employment: 5 Figures

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Interesting Statistics of Employment: 5 Figures

It is a common sight to see the old and young talking about the job market that is going through a slump, regardless of the time or the economic conditions of the country; this picture usually is accompanied with some “cutting chai” at tea stalls on busy streets or cool cafes at the malls with the slurp of espresso with a tiny straw where the average upper-middle class youth talk about their first-world dreams while breathing progressive third-world air.

But is that really always the case? Data management or statistical analysis as we have established several times before, is sending the job market into hyper-drive, attracting millions of MNCs into the Indian soil and populating the job search portals with millions of opportunities in data.  But dare we only make statements, we are statisticians and we know that numbers do speak louder than simple statements.

So, in keeping with our love for figures and facts backed by data, DexLab Analytics has compiled a list of interesting statistics about the job market and the process of hiring.

#1 Each and every major corporate job position attracts a minimum of 250 applications!

Out of all these applications only 4 to 6 resumes get shortlisted and are called for interviews. Out of these 4 to 6 people only 1 lucky candidate is selected.

#2 Every job seeker takes into account 5 factors before accepting the position at a firm.

They are –

  • The company culture, values and overall work environment
  • Distance, ease of commute, location
  • Prospects of maintaining work/life balance
  • Growth prospects in career and
  • Pay package and compensation.

#3 Almost 94 percent of sales personnel revealed that base salary is the most important determining factor in the compensation package for them.

But 62 percent of sales personnel say that commission is the most important element.

#4 Out of 3 employees at least 2 say that most employers do not do or do not know how to use social media platforms for promoting job openings.

And 3 out of 4 employees also believe that most companies and employers do not know how to promote their brand on social media networks as well.

#5 Social media platforms are used to search for jobs by 79 percent of jobseekers.

This figure rises to 86 percent for younger job seekers who are in their initial 10 years of job search.

To learn more about statistical analysis and for Data analyst certification in Gurgaon drop by our website at DexLab Analytics.

 

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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.

2

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.

 

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Measuring Why Correlation does not Causation

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How about we first examine the thought of the connection and its application in the process of data analysis? Connection analysis is being utilized to distinguish or evaluate the relationship between two quantitative variables. The existence of variables should be between either dependent or free variable. To quantify between the reaction and indicator variable ‘r’ is used, which is the Connection Coefficient. The connection coefficient’s indication shows the affiliation’s bearing. The bearing should be either positive affiliation or negative affiliation. For instance, a connection of r = 0.95 demonstrates an in number, positive relationship between the two variables. Then again, if a relationship r = – 0.3 demonstrates a powerless, negative relationship between the two variables. The size of the relationship coefficient demonstrates the affiliation’s quality. In connection analysis, we can fall upon just four situations of affiliation.

qualitative

Situation 1 – The two variables have an in-number positive connection where r = 0.9

Situation 2 – The two variables have a powerless relationship where r = 0.3

Situation 3 – The two variables do not have any connection where r = 0

Situation 4 – The two variables have an in-number negative connection where r = – 0.9

Utilization of Case Correlation:

Promotion supervisor needs to distinguish the discriminating variable that is influencing the Conversion Rate of a site.

Business administrators need to discover whether the web journal redesign, identified with free arrival of online games, is creating the extra offer of income on the agreed day.

DayVisitors – Free Online Games Release UpdateRevenue
1180001500
2120001200
3150001600
410000900
58000950
6140001300
7120001100
8160001650
9100001050
10200001600

You may utilize excel function CORREL () in order to recognize the connection coefficient to quantify the relationship between the guests and the income. The relationship coefficient r for the aforementioned set of data is 0.90. It demonstrates that there is a solid relationship between the variable guests and income. Another flawless case for an in-number negative relationship is that at whatever point the precipitation diminishes, the horticulture’s yield diminishes. The relationship analysis likewise serves to further develop the analysis in multivariate insights.

 

Connection does not infer Causation:

This happens as soon as you attempt to discover the relationship between two autonomous variables or between an indigent variable and free variable. Association does not infer Causation implies those occasions that take place to correspond with one another – are not as a matter essentially related in a causal manner. This might be passed on that the variable X does not have an impact on the variable Y. It’s only an occurrence. We need to further accept or make it a theory that X is bringing about the impact on the Y variable. In the aforementioned utilization case, we discovered that the connection coefficient was at 0.89. It just demonstrates that there is a solid relationship between our Y Variable income and the X variable guests. Nonetheless, we don’t have any verification that if there is an expansion in the guests then the income additionally increments. No circumstances and end results is oblique here.

Microsoft acquires VoloMetrix

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Microsoft has acquired VoloMetrix to boost organizational analytics capabilities. This acquisition will combine VoloMetrix experience, technology and track record of success with Office 365 and Microsoft’s previously announced Delve Organizational Analytics. With this acquisition, Microsoft is aiming to fulfil its ambition to reinvent productivity and business process and how it will deliver new value to its customers with organizational analytics.
Organizational analytics helps businesses to measure performance metrics such as productivity, effectiveness and efficiency. This would lead to improve the profitability of the organization.

Unquestionably, the most important asset any company has is its people. Every day, it is people that make the decisions that effectively determine every company outcome. Historically there has been no data-driven way to connect employee behavior to business outcomes. Today, with the advent of powerful big data and predictive tools, a new field is emerging to solve this problem: people analytics. VoloMetrix is the leading people analytics company, using big data to optimize businesses’ performance by simplifying organizational structure, boosting employee engagement and increasing sales team effectiveness. Working with Fortune 100 companies worldwide, VoloMetrix’s patented technology extracts and analyzes anonymous aggregated collaboration data to reveal unprecedented insights into how employee behaviors drive business outcomes.

Ryan Fuller, Co-Founder and CEO at VoloMetrix said in his blog, He had started VoloMetrix 4.5 years ago with the belief that people are every company’s most valuable asset and the mission to transform knowledge worker productivity through data, transparency and feedback loops. His goals were to fundamentally change companies understanding of how their people drive their outcomes and empower every employee to take back their time and have the very best tools to be successful. He had opportunity to work with dozens of global 2000 companies to prove out the science of Organizational Analytics and apply it to help organization.
VoloMetrix has an excellent capability and the applications of the software for the organizations to deliver the productivity improvement metrics.
It has three high level solutions for the organizations:

1.    Sales Productivity
2.    Organizational Simplification
3.    Employee Engagement

Each solution has various sub dimension capabilities to solve the problems related to organizational analytics. They are Margin Optimization, Coordination, Predictive Analytics, Time Budgeting, Process delivery, Cost reduction, Workforce planning, Org network analysis, Collaboration. These capabilities address the area where the organization can leverage to improve the productivity.

Who needs what?

Sales Productivity – Sales Leadership, Sales Operations, CMOs and CFOs.
Organizational Simplification – CIOs, COOs, Corporate Strategy Teams and Consultants
Employee Engagement – Corporate Strategy, HR, CEOs, COOs and Strategy Consultants.
This acquisition can certainly help Microsoft to provide the best possible integration of VoloMetrix into Office 365.

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