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Interactive Data Discovery and Predictive Analytics: Extract Useful Knowledge from Data

Interactive-Data-Discovery-and-Predictive-Analytics-Extract-Useful-Knowledge-from-Data

Impressive predictive analytics coupled with interactive data discovery technology enable rational SAS analysts to distinguish pertinent trends and interactions in datasets, and pan out questions from all dimensions. This smashing concoction of technologies also allows business users to exchange ideas with pundits, to create, modify and pick the best predictive models, constructively.

 

 

A comprehensive SAS solution might be the key to empower users in taking better business decisions, without wasting much time. This kind of interactive solution must involve ceaseless communication, giving enough room to even non-technical users to explore data visually, develop analytic models, and share fruitful results.  

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Why Data Science Matters More Than Data Scientists?

More is always better, isn’t it? But does it always holds true, especially when it comes to customer data? Maybe not, because business is all about extracting meaningful insights from data, and if that cannot be acted upon then it is of no good.

 
Why Data Science Matters More Than Data Scientists?
 

Recently, Accenture concluded that one of the greatest challenges that marketers face nowadays is to discover the right ways to turn data into productive insights and then into action. For that, you would need analytics professionals who do know how to collect, store and integrate information, while mastering the technology aspect.

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How to Leverage AI Strategy in Business?

Everyday some company or the other are deploying AI into their systems – whether its Spotify’s machine learning program or Bank of America’s chatbot Erica – it seems AI has broken the shackles and left the machine room to enter the mainstream business.

 

How to Leverage AI Strategy in Business?

 

Today’s AI algorithms are framed on remarkably factual machine sight, speech and hearing, and they have easy access to global cache of information. Thanks to Deep Learning, meteoric growth in data and other cutting edge AI techniques, AI performance is staggeringly improving. With these developments, it may seem possible for CIOs, enterprise architects, application managers who are still in nascent stage in gaining expertise in AI to feel like they are lagging behind somewhere. Contrarily, they are doing well for themselves.

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Here’s How to Make Data More Actionable for Better Decision-making

Every customer demand needs to be fulfilled, and CEO’s expect marketing analysts to deliver them. Being a key marketing initiative, optimizing every customer experience is a significant deal to seal for marketers all around the globe.

 

Here’s How to Make Data More Actionable for Better Decision-making

 

Data, of course, plays a crucial role in marketing endeavors – but only the data that is interpretable makes sense, rendering other data useless. To turn data into actionable, organizations need to understand the accuracy of data and in the process should be successful in turning insights into action.

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Risk Analytics Market: Serious Growth Rate Projection for 2017-2021

Want to get to the core of understanding risk within various business frameworks? The answer is Risk Analytics. This new breed of data analytics facilitates organizations in precisely defining, recognizing and managing their risk, and its need is going to increase in the coming few years. New developments in risk analytics are gaining limelight and bringing a notable transformation in the market, while enhancing its overall capability.

 
Risk Analytics Market: Serious Growth Rate Projection for 2017-2021
 

Recently, a team of analysts had eureka moment – they introduced a new concept of real-time risk analytics – it is nothing but a modern, more advanced version of traditional risk analytics methods. Here, the prediction is based on real-time data – it processes, examines and determines risk all on a real-time basis – hence top notch financial institutions are putting real-time risk analytics to best use to manage and mitigate associated risks. Several asset management, portfolio management and hedge fund firms, and investment banks are relying on this mode of risk analytics to modify their operating principles to play in accordance with investment and market shifts.

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Market Risk Analytics: How Top Notch Companies Are Assessing Intricate Risks​

Risk analytics tools boost operational efficiency. But do you know what tools to implement to derive the best results?

 
Market Risk Analytics: How Top Notch Companies Are Assessing Intricate Risks
 

With the burgeoning demand for big data all over the world, major corporate houses are taking risk analytics – the process of collecting, analyzing and measuring real-time data to forecast future risk for improved decision-making – to a new high.

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Credit Risk Modelling: A Basic Overview

Credit Risk Modelling: A Basic Overview

HISTORICAL BACKGROUND

The root cause for the Financial Crisis which stormed the globe in 2008 was the Sub-prime crisis which appeared in USA during late 2006. A sub-prime lending practice started in USA during 2003-2006. During the later parts of 2003, the housing sector started expanding and housing prices also increased. It has been shown that the housing prices were growing exponentially at that time. As a result, the housing prices followed a super-exponential or hyperbolic growth path. Such super-exponential paths for asset prices are termed as ‘bubbles’ So USA was riding a Housing price bubble. Now the bankers, started giving loans to the sub-prime segments. This segment comprised of customers who hardly had the eligibility to pay back the loans. However, since the loans were backed by mortgages bankers believed that with housing price increases the they could not only recover the loans but earn profits by selling off the houses. The expectations made by the bankers that asset prices always would ride the rising curve was erroneous. Hence, when the housing prices crashed the loans were not recoverable. Many banks sold off these loans to the investment banks who converted the loans into asset based securities. These assets based securities were disbursed all over the globe by the investments banks, the largest being done by Lehmann Brothers. When the underlying assets went valueless and the investors lost their investments, many of the investment banks collapsed. This caused the Financial Crisis and a huge loss of investors and tax-payers wealth. The involvement of Systematically Important Financial Institutions (SIFIs) and Globally Systematically Important Financial Institutions (G-SIFIs) into the frivolous lending process had amplified the intensity and the exposure of the crisis.

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Data to Fill in the Gaps: Using Data Analytics to Seek Retail Advantage

Retailers need to know their customers well – who they are, what stuffs they like to buy, how they would pay and what they think about the product or service. The best part is that there’s an ocean of data now available to fill in the gaps. Every time a customer visits the store, a long trail of customer data churns out for the retailers to explore.

 
Data to Fill in the Gaps: Using Data Analytics to Seek Retail Advantage
 

With the help of this data, the retailers improve sales figures, customer service and interaction and their product offerings. Leveraging data is crucial. According to Gartner, retailers seek advanced analytic capabilities to shine bright in this age of digitized market solutions.

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10 Amazing Excel Templates to Help Maintain an Organized Budget

Managing a proper budget is an essential task. Professionals working in enterprises are somehow involved in performing this task to some degree. Fortunately, a handful number of budgeting Excel templates is available online – check out some of our favorites:

 

10 Amazing Excel Templates to Help Maintain an Organized Budget

 

Back to the basics, budget is something more than an educative, future projection of what should happen to a company’s financial position over a specific course of time, based on the latest trends, current data and previous history. The accuracy of the entire forecast depends on the quality of financial data accumulated during the budget period – it is here that a robust set of data collection tools saves the day.

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Data Science and Machine Learning: In What State They Are To Be Found?

Keen to have a sweeping view of data science and machine learning as a whole? 

Want to crack who is playing tricks with data and what’s happening in and around the budding field of machine learning across industries?

Looking for ways to know how aspiring, young data scientists are breaking into the IT field to invent something new each day?

Hold your breath, tight. The below report showcases few of our intrinsic findings – which we derived from Kaggle’s industry-wide survey. Also, interactive visualizations are on the offer.

  1. On an average, data scientists fall under the age bar of 30 years old, but as a matter of fact, this age limit is subject to change. For example, the average age of data scientists from India tends to be 9 years younger than the average scientists from Australia.
  2. Python is the most commonly used language programs in India, but data scientists at large are relying on R now.
  3. Most of the data scientists are likely to possess a Master’s degree, however those who bags a salary of more than $150K mostly have a doctoral degree under their hood.

Who’s Using Data?

A lot of ways are there to nab who’s working with data, but in here we will fix our gaze on the demographic statistics and the background of people who are working in data science.

What is your age?

To kick start our discussion, according to the Kaggle survey, the average age of respondents was 30 years old subject to some variation. The respondents from India were on an average 9 years younger than those from Australia.

What is your employment situation?

What kind of job title you bag?

Anyone who uses code for data analysis is termed as a data scientist. But how true is this? In the vast realm of data science, there are a series of job titles that can be pegged. For instance, in Iran and Malaysia, the job title of data scientist is not so popular, they like to call data scientists by the name Scientist or Researcher. So, keep a note of it.

How much is your full-time annual salary?

While “compensation and benefits” ranked a little lower than “opportunities for professional developments”, the best part remains it can still be considered a reasonable compensation.

Check out how much a standard machine learning engineer brings home to in the US

What should be the highest formal education?

So, what’s going on in your mind? Should you get your hands on the next formal degree? Normally, most of the data scientists have obtained a full-time master’s degree, even if they haven’t they are at least data analytics’ certified. But professionals who come under a higher salary slab are more likely to possess a doctoral degree.

What are the most commonly used data science methods at work?

Largely, logistic regression is used in all the work areas except the domain of Military and Security, because in here Neural Networks are being implemented extensively.

Which tool is used at work?

Python was once the most used data analytics tool, but now it is replaced by R.

The original article can be viewed in Kaggle.

Kaggle: A Brief Note

Kaggle is an iconic platform for data scientists, allowing ample scope to connect, understand, discover and explore data. For years, Kaggle has been a diverse platform to drag in hundreds of data scientists and machine learning enthusiasts, and is still in the game.

For excellent data science certification in Pune, look no further than DexLab Analytics. Opt for their intensive data science training in Pune and unlock a string of impressive career milestones.

Interested in a career in Data Analyst?

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