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Interesting Statistics of Employment: 5 Figures

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

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.

 

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The evolution of Big Data in business decision making

The evolution of Big Data in business decision making

Big Data is big. We have all established that, and now we know that all the noise about Big Data is not just hype but is reality. The data generated on earth is doubling in every 1.2 years and the mountainous heap of data keep streaming in from different sources with the increase in technology.

Let us look at some data to really understand how big, Big Data is growing:

  • The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device
  • On an average every day almost 11 billion texts are sent across the globe
  • 8 million videos are watched on YouTube alone
  • The global number of Google searches everyday is 5 billion

But the balance has long been tipped off as we have only been creating data but not consuming it enough for proper use. What we fail to realize is the fact that we are data agents, as we generate more than 25 quintillion bytes of data everyday through our daily online activities. The behaviors that add more numbers to this monstrous hill of data are – online communications, consumer transactions, online behavior, video streaming services and much more.

The numbers of 2012 suggest that world generated more than 2 Zetabytes of data. In simpler terms that is equal to 2 trillion gigabytes. What’s more alarming is the fact that by the year 2020, we will generate 35 trillions of data. To manage this growing amount of data we will need 10 times the servers we use now by 2020 and at least 50 times more data management systems and 75 times the files to manage it all.

The industry still is not prepared to handle such an explosion of data as 80 percent of this data is mainly unstructured data. Traditional statistical tools cannot handle this amount of data, as it is not only too big, but is also too complicated and unorganized to be analyzed with the limited functions offered by traditional statistical analysis tools.

In the realm of data analysts there are only 500 thousand computer scientists, but less than 3000 mathematicians. Thus, the talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds prepared to untangle the complex knots of intertwined data hiding useful information.

But to truly harness the complete potential of Big Data we need more human resource and more tools. For finding value we need to mine all this data.

Then what is the solution to this even bigger problem of tackling Big Data? We need Big Data Analytics. This is more than just a new technological avenue, but on the contrary this is fresh new way of thinking about the company objectives and the strategies created to achieve them. True understanding of Big data will help organizations understand their customers. Big Data analytics is the answer behind where the hidden opportunities lie.

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A few advanced tools that are currently in use in the data analysis industry are: SAS, R Programming, Hadoop, Pig, Spark and Hive. SAS is slowly emerging to be an increasingly popular tool to handle data analysis problems, which is why SAS experts are highly in-demand in the job market presently. To learn more about Big Data training institutes follow our latest posts in DexLab Analytics.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
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Python Vs R- Which You Want To Learn First

Python Vs R- Which You Want To Learn First

If Big Data interests you as a career choice and you are pretty much aware of the skills you need in order to be proficient in this field, in all likelihood you must be aware that R and Python are two leading languages used for analyzing data. And in case you are not really sure as to learn which of the mentioned articles first, this post will help you in making that decision.

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In the field of analysis of data, R and Python both are free solutions that are easy to install and get started with. And it is normal for the layman to wonder which to learn first. But you may thank the heavens as both are excellent choices.

Let’s Make Visualizations Better In Python with Matplotlib – @Dexlabanalytics.

A recent poll on the most widely used programming languages for analytics and data science reveal the following:

Python Vs R- Which You Want To Learn First

 

Reasons to Choose R

R has an illustrious history that stretches for a considerable period of time. In addition you receive support from an active, dedicated and thriving community. That translates to the fact that you are more likely to be helped in case you are in need of some assistance or have any queries to resolve. In addition another factor that works in the favor of R is the abundance of packages that contribute greatly to increasing its functionality and make it more accessible which put R as one of the front runners to being the data science tool of choice. R works well with computer languages like Java, C and C++.

How to Parse Data with Python – @Dexlabanalytics.

In situations that call for heavy tasks in statistical analysis as well as creating graphics R programming is the tool that you want to turn to. In R, you are able to perform convoluted mathematical operations with surprising ease like matrix multiplication. And the array-centered syntax of the language make the process of translating the math into lines of code far easier which especially true of persons with little or no coding knowledge and experience.

Reasons to Opt for Python

In contrast to the specialized nature of R, Python is a programming language that serves general purposes and is able to perform a variety of tasks like munging data, engineering and wrangling data, building web applications and scraping websites amongst others. It is also the easier one to master among the two especially if you have learned an OOP or object-oriented programming language previously. In addition the Code written in Python is scalable and may be maintained with more robust code than it is possible in case of R.

The Choice Between SAS Vs. R Vs. Python: Which to Learn First? – @Dexlabanalytics.

Though the data packages available are not as large and comprehensive as R, Python when used in conjunction with tools like Numpy, Pandas, Scikit etc it comes pretty close to the comprehensive functionality of R. Python is also being adopted for tasks like statistical work of intermediate and basic complexity as well as machine learning.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

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