As digitization grows in size and width, more and more companies are seeking ways to modify their digital lending services. It would be effective as well as profitable for both borrowers and lenders. And as a topping on the cake, companies resort to Artificial Intelligence and Big Data as they believe they are the future powerhouse of loans.
Originally banks being the lenders make the lending decision based on a loan applicant’s credit score – which is a 3-digit number collected from renowned credit bureaus, like Equifax and Experian. Credit scores are obtained from large piles of data, such as credit history length, payment history and credit line amounts, and are used to decide whether the applicants would be able to repay their debts. They are also good to determine the interest rate of loans.
Getting all work done from inside a browser through an internet-enable computer is no mean feat. Just after Google cracked the idea, Microsoft and Apple soon followed the bandwagon and developed online office suites of their own.
So, do you think Google deserves the tag of top dog or should you give accolades to Microsoft for developing a feature-rich office suite?
Of late, Microsoft- the tech mogul, like Google and IBM has deciphered massive potentials in launching quantum computing to eradicate the world’s biggest problems. Researchers at IBM, Intel and others are on an amazing scientific rat race to develop a commercially viable super quantum computer. Though they exist in laboratories, bringing it into the real world is going to be an altogether different endeavor.
The concept of quantum computer is somewhat different and is not based on the use of binary logic. According to its nature, a quantum computer is a simple yes/no/both device – where a programmer doesn’t have to choose between options, he is free to make all the choices in the world and that makes all the difference in the world.
We are back again with some awesome news! DexLab Analytics is organizing a comprehensive one-week training program for super-efficient data analytics and big data team of Mercer – a top notch multinational corporation that provides top-of-the-line solutions in Talent, Retirement and Investments worldwide.
The training module has started from Thursday, 21st September 2017 and our in-house senior consultants are imparting cutting edge technological knowledge about R Programming to the data-hungry Mercer professionals. The training is taking place at Mercer’s corporate in DLF Phase 3, Gurgaon Office: with the rising demand of data analytics and R skills to imbibe, the advancement in the field of health, wealth and careers is witnessing a steady growth. To target a larger audience and loyal clientele base, R Programming skills need to be harnessed properly so as to flourish the future of business on a larger scale.
R functions are used to produce chart templates to keep the look and feel of the reports intact.
In this post you will come across how to create chart templates with R functions – all the R users should be accustomed to the calling functions so as to perform calculations and outline plots accurately. Remember what colors and fonts to use each time: R functions are used as a short-cut for producing customary-looking charts.
Python wascreated by Guido van Rossum and first released in 1991.Python, as a programming platform, has gained a huge popularity within a short span of time because of its flexibility and the user-friendly interface. The software can be deployed easily for developing statistical models and machine learning algorithms
In fact, due to the advent of AI and ML, Python has a language has had a certain kind of rebirth as far as industrial use is concerned. Today, however, the focus is going to be on a particular section of the language, namely the control flow to create a basic system in Python.
Far from the conventional science disciplines, like physics or mathematics, Data Science is a budding discipline: which means there are no proper definition to explain what data science is and what role it does play.
Nevertheless, the internet is full of working definitions of data science. As per Wikipedia, Data Science is
(an) interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.
Microsoft Excel is one of the most significant computer software program making rounds in today’s workplace environment. More and more employees are being asked to hone their Excel skills in order to survive in the highly competitive market.
From the employer’s viewpoint, especially those who are associated with IT, the use of Excel is gaining prominence and for all the right reasons. Not only it is being used by business professionals to carry out daily functional tasks but also a large chunk of experts rely on it for decision support.
Tableau plays a very crucial role in data intelligence, data representations, data visualizations, and data modelling. The time has gone when folks used to draw graph, charts, on excel and variant types of resources or tool. Because provide us live data source accessibility with number of data sources like – spark, Google Products, Social network, programming language (R,Python,JAVA) whereas tableau uses VIZQL in background to speed up the performance. So now the time is to move towards Business Intelligence.As of now, you don’t need to worry about either data or data sources.
Without the great Analytical surge, everything was looking fit and fine. The economy was performing well. The IT industry was looking stable. The tech honchos were playing fine. And then IT happened! Data Analytics snatched the dazzling limelight all to itself.
It’s true once in a while, our market needs a good shaking, or else things tend to get sluggish and slow. Over time, the industries start decreasing in efficiency and business houses crumples. Therefore, the change induced by Big Data Analytics is one for good: it started pulling back the market to its former position. From medical science to military to security, the reach of Big Data Analytics can be witnessed everywhere.
The evolution of analytics is largely consistent and covers a wide span of industries. It’s not like it suddenly came into a lot of focus, its advancement was slow and steady. Now, it has strived to become extremely important to store, interpret, analyze and develop crucial insights – social media is deriving maximum benefits out of analytics, while customizing their products to make more money from advertisements. On the other hand, the service-oriented companies love to manipulate data that is generated through myriad social channels to trigger customer base.
Copyright © 2016 DexLab Solutions Corp All Rights Reserved | Privacy Policy | Legal Disclaimer | Terms and Conditions