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Category: Big Data

Big Data Hacks: 5 Amazing Free Data Sources

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Big data hacks: 5 amazing free data sources

With the data explosion revealing a continuum of numbers and facts and figures across the web and across businesses, it is of no doubt that data is omnipresent. But as the saying goes, sometimes it is hard to see the forest due to all the trees.  A big myth among several companies is that they need to hire data analysts to look for their own data for analysis and to reap the benefits from Big Data analytics. But you must realize that this is far from the truth.

There are more than hundreds in fact even thousands of free data sets available for analysis and use for those who are smart enough to know where to look for them. Here is a list of 5 most popular free data set sources that are widely used globally. There are several more out there for those who are keen enough to look for them.

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  • Data.gov: in compliance to the promise made by the US government last year all the government data is available for free on the internet in this site. The site is a useful source of information on everything starting from numbers in association to crime to climate change and much more.
  • Socrata: another great place to get scoop on the latest government related data along with some useful visualization tools that come built into the web portal.
  • org another place to access government data for free. One can get access to government data from the US, Canada, EU, CKAN and more.
  • World Health Organization data portal: a place to access all the statistics of hunger, health and disease of the world can be accessed here.
  • FaceBook Graph: FaceBook over the past few years has tightened their security and privacy settings. But there are still some amounts of data open to eyes without any privacy. And FaceBook provides information and access to all this data with their Graph API. While users may not be happy to share them with the world, they probably have not yet figured out how to hide them.

A bonus free data source that could also be fun to explore.

Face.com: get face recognition data with this fascinating tool and analyze possibilities like the creator.

These days a lot of forward thinking companies are trying to data driven, but they may not have ample resources to get their own data right away. So, it may be a good idea to begin with these publicly available free data sources. The best tip for data scientists is to learn to ask the right questions to get the right answers.

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Highest Paying Jobs in Noida

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Noida or in other words the hub of IT firms and outsourcing services in India is based in Gautam Budh Nagar district of Uttar Pradesh. Noida expands to be New Okhla Industrial Development Authority as it lies under the supervision and management of the organization named so. Over the past few years several multinational companies especially those dealing with IT outsourcing and software development services have set base in this area, due to the low prices of commercial real estate and availability of skilled workers with affordable costs. Some of the big names that have settled in Noida are – TSYS International, IBM, One97, EXL Service, Abstract Consultancy, Fujitsu, AON Hewitt, CSC, Ebix etc. and these major firms generate a number of jobs every year.

 

Highest paying jobs in Noida

 

As the young job seekers of India may already know (unless they live under a rock) Big Data is driving the job market into hyper-drive and is the new megatrend to watch out for.

 

But do not just take our word for it or even the internet’s, we are data driven people being the pioneering Big Data Hadoop institute in Noida and thus, believe that numbers speak louder than words. So, we conducted a survey with the help of the top job hunting sites in India to understand the job scenario and determine whether Big Data is in fact a megatrend. And this is what we found in our research… Continue reading “Highest Paying Jobs in Noida”

A few easy steps to be a SUCCESSFUL Data Scientist

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A-few-easy-steps-to-be-a-successful-data-scientist (1)

Data science has soared high for the past few years now; sending the job market into turbo pace where organizations are opening up their C-suite positions for unicorns to take their mountainous heap of data and make sense of it all to generate the big bucks. And professionals from a variety of fields are now eyeing the attractive position of data analyst as a possible profitable career move.

We went about questioning the faculty at our premiere data science and excel dashboard training institute to know how one can emerge as a successful data scientist, in this fast expanding field. We wanted to take an objective position from a recruiter’s point of view and create a list of technical and non-technical skills which are essential to be deemed an asset employee in the field of data science.

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A noteworthy point to be mentioned here is that every other organization will evaluate skills and knowledge in different tools with varying perspectives. Thus, this list in no way is an exhaustive one. But if a candidate has these songs then he/she will make a strong case in their favor as a potential data scientist.

The technical aspects:

Academia:

Most data scientists are highly educated professionals with more than 88 percent of them having a Master’s degree and 46 percent of them have a PhD degree. There are exceptions to these generalized figures but a strong educational background is necessary for aspiring data scientists to understand the complex subject of data science in depth. The field of data science can be seen in the middle of a Venn diagram with intersecting circles of subjects like Mathematics and Statistics 32%, Engineering 16% and Computer Science and Programming 19%.

Knowledge in applications like SAS and/or R Programming:

In depth knowledge in any one of the above tools is absolutely necessary for aspiring data scientists as these form the foundation of data analysis and predictive modeling. Different companies give preference to different analysis tools from R and SAS, a relatively new open source program that is also slowly being incorporated into companies is Hadoop.

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For those from a computer science background:

  • Coding skills in Python – the most common coding language currently in use in Python. But some companies may also demand their data scientists to know Perl, C++, Java or C.
  • Understanding of Hadoop environment – not always an absolute necessity but can prove to be advantageous in most cases. Another strong selling point may be experience in Pig or Hive. Acquaintance with cloud based tools like Amazon S3 may also be advantageous.
  • Must have the ability to work with unstructured data with knowledge in NoSQL and must be proficient in executing complex queries in SQL.

Non-technical skills:

  • Impeccable communicational skills so that data personnel can translate their technical findings into non-technical inputs comprehensible by the non-techies like sales and marketing.
  • A strong understanding of the business or the industry the company operates in. leverage the company’s data to achieve its business objectives with strong business acumen.
  • Must have profound intellectual curiosity to filter out the problem areas and find solutions against the same.

 

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How Non-profits Can Benefit From Modern Data Analytics Tools

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Data analytics solutions in the past took several years to be planned and deployed, but those days are long gone. By adopting a few data analytics processes in their operations, non-profit organizations can streamline and improve their operations and outreach for achieving better results. Earlier this was not possible as data analytics did not adapt well with the existing technologies and processes. Also there was a lack of well-equipped personnel as the data analysis tools were so complex in nature, that it required the IT personnel to take care of analysis tasks for clients.

 

How non-profits cans benefit from modern data analytics tools Continue reading “How Non-profits Can Benefit From Modern Data Analytics Tools”

The Most Important Algorithms Every Data Scientist Must Know

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Algorithms are now like the air we breathe; it has become an inevitable part of our daily lives and is also included in all types of businesses. Experts like Gartner has called this age as the algorithm business which is the key driving force that is overthrowing the traditional ways in which we do our business and manage operations.

The most important algorithms of machine learning

In fact the algorithm boom with uber diversification has reached a new high, so much so that now each function in a business has its own algorithm and one can buy their own from the algorithm marketplace. This was developed by algorithm developers at Algorithmia to save the precious time and money of business operators and other fellow developers and offers a plethora of more than 800 algorithms in the fields of machine learning, audio and visual processing and computer vision.

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But we as data enthusiasts in the same field with an undying love for algorithm would like to suggest that not all the algorithms from the Algorithmia marketplace may be suitable for your needs. Business needs are highly subjective and environment based. And things as dynamic as algorithms can produce different types of results even in the slightly different situations. Also the use of algorithms depends on a number of factors on how they can be applied and what results one can expect from their application. The variables on which the application of algorithms depends are as follows: type and volume of the data sets, the function the algorithm will be applied for and the industry in which the algorithm will be applied.

Hence, not always reaching for the easy option of buying a readymade algorithm off the shelf and simply tweaking it to fit into your model may not always be the most cost-effective or time saving way to go. So, it is highly recommended for data scientists to educate themselves well on the most important algorithms that must be known by them, as well as the back of their hands. A data scientist must also know how each algorithm is developed and also which purpose calls for which algorithm to be applied.

So, our experts associated with DexLab Analytics developed an infographic to let big data analysts know the 12 most essential algorithms that must still be included in the repertoire of a skilled data scientist. To know more about data science courses drop DexLab Analytics and find your true data-based calling.

 

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Are You a Student of Statistics? – You must know these 3 things

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Are you a student of statistics?

We a premiere statistical and data analysis training institute offering courses on Big Data Hadoop, Business intelligence and Ai. We asked our faculty to tell us the three most important things that every student of elementary statistics should know.

So, let us get on with it:

  1. The notion that statistics is about numbers, is in the context only: statistics involves a rich treasure trove of numeric and graphical representation of displaying data to quantify them also it is very important to be capable of generating graphs along with numbers. But that is not the half part of statistics and the main interesting aspect is related to making the big leap from numbers and graphs to the realistic worldly interpretations. Uncannily statistics also poses to be a fascinating philosophical tension raising the question and healthy skepticism about we believe in and what we do not.
  2. The analysis part is not the most crucial part of a statistical study, the most important part lies with the when, where and how of gathering the data. We must not forget when we enter each number or data, calculate and plot the strategies we build on our understanding, but many a times at the time of interpretation that each every graph, data or number is a product of a fallible machine, be it organic or mechanical. If we are able to take proper care at the stage of sampling and observation we will be able to obtain great dividends at the final stage of interpretation and analysis of all our statistical efforts.
  3. All statistical functions off all kinds of mathematical sciences are based on a two-way communication system. This communication system should be between the statistician and non-statistician end. The main aim of statistical analysis is to put forward important social, public and scientific questions. A good statistician knows how to communicate with the public especially with those who are by and large not statisticians. Also the public here plays an important role and must possess simple idea of statistical conclusions to grasp what the statisticians have to say to them. This is an important criterion to be incorporated in the K-12 and college curriculum for elementary statistical students.

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If you agree with our views and would like to discuss further on statistics and its application on data analysis then feel free drop by DexLab Analytics and stay updated on the latest trends in data management and mining.

 

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Credit Risk Managers Must use Big Data in These Three Ways

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Credit risk managers must use Big Data in these three ways

While the developed nations are slowly recovering from the financial chaos of post depression, the credit risk managers are facing growing default rates as household debts are increasing with almost no relief in sight. As per the reports of the International Finance which stated at the end of 2015 that household debts have risen to by USD 7.7 trillion since the year 2007. It now stands at the heart stopping amount of a massive USD 44 trillion and the amount of debts increased in the emerging markets is of USD 6.2 trillion. The household loans of emerging economies calculating as per adult rose by 120 percent over the period and are now summed up to USD 3000.

To thrive in this market of increasing debts, credit risk managers must consider innovative methods to keep accuracy in check and decrease default rates. A good solution to this can be applying the data analytics to Big Data. Continue reading “Credit Risk Managers Must use Big Data in These Three Ways”

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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The Eclectic Mix of Big Data and Marketing

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The-Eclectic-Mix-of-Big-Data-and-Marketing

Marketers with even rudimentary knowledge of Big Data are better placed to precisely reach the largest amount of potential target customers than their counterparts who are uninitiated to the world of Big Data. Good marketers know the customers they target very well. Big Data facilitates this process.

The collection of data and its storage into separate data banks is simply a process part in acquiring raw data. This data should be reproduced in such a manner that marketers are able to easily grasp. And with the impending explosion of IoT devices, the amount of data too is expected to increase by leaps and bounds.

Marketers need to analyze the data available to them very carefully. The process involved is a complicated one which requires the use of specialized software tools.

This is where a translation management system comes into play. These tools may readily be used in order to get the desired insight from the vast pool of data available. Applications like these have made the process so simple that some people who are using it on a daily basis are even unaware that they are dealing with Big Data.

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Big Data Uses in Marketing

  • Honing Market Strategy Through Monitoring Trends Online

You may use tools as simple as Google Trends to keep abreast of the latest trends in the world of the internet. With a number of ways to customize and filter the results marketers have an easy access of that is trending at any instant and associate the product in ways that let it have increased traction.

  • Define Customer Profiles With Big Data

It is a good idea to consult Big Data while drawing up your ideal profile of customers. There is no more need to make educated guesses with things as they stand of today. Through the use of Big Data marketers have access to the various details like demographics, age, work profile of the consumers they target. The case study of the Avis Budget may be cited where it was found that Big Data facilitated the formation of an effective contact strategy.

  • Engaging the Buyer at the Correct Time

Timing, according to some marketers, of the essence when it comes to marketing. This process too is facilitated by Big Data which makes relevant and timely marketing strategies possible. We may take the case of displaying mobile ads at timings when the customer is most like to be online.

  • Content That Boosts Sales

Big Data also lets marketers know the content that gives them the extra edge when it comes to marketing their products. Some of the tools used in translation management make such an analysis possible with scores on individual pieces of content. Success and efficiency of assets both may be gauged through the use of such tools. With the required information marketers will be able to pinpoint content that customers liked.

  • Predictive Analysis

If the base CRM information of a particular company and other providers of Big Data is taken into account the marketer may get a predictive lead score which in turn may be used to make an accurate prediction of the behavior of leads in the future. The end result is that marketers acquire an indication of considerable clarity on their digital behaviors and should be taken into account more when considering lead scoring.

End Words

Now, do not both of them make up an eclectic mix.

 

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