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Predictions For Big Data In 2016

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Fresh on the heels of the advent of the new year, Big Data enthusiasts might wonder what the coming year beholds like machine learning updates, data as a service that works in real time, Markets of algorithms, Spark and much, much more.

Predictions-for-Big-Data-in-2016

 

  • The Emergence of the Chief Data Officer

The sweeping changes in recent times has made aware that enterprises realize that they are in real need of a working strategy in order to compete with competitors who are digital-native.This strategy is ideally formed by a Chief Data Officer.

  •  Empowering Business Users

Due to a lack of proper talent in the Big Data industry more tools will emerge that reveal information to the users directly. Salesforce and Microsoft are both letting non-coders create apps that are intended to make visualizations of business data.

  •  Intelligence Embedding

In recent times it is often seen that organizations embed analytic function pieces directly in the required apps. In fact, according to the predictions by the IDC by 2020, it is projected that all software related to business analytics will include prescriptive analytics which will be based on cognitive computing functionality.

  •  An End to Talent Shortage

There is an acute shortage of data scientists. As reported recently by A.T. Kearney, the business consultancy no less than 72 % of global companies that are the leaders in their respective markets, find it hard to recruit quality talent in data science. But recent predictions by the International Institute for Analytics run contrary. According to it that scarcity of quality talent in the field may be reduced in 2016 as companies put into use new tactics.

  • Machine Learning Comes of Age

Machine learning basically revolves around the creation of algorithms that lets computers sort of learn from experience. It is attracting more than its fair bit of attention in organizations that seek to automate processed that otherwise require the intervention of humans.

  •  The Rise of the Data-As-A- Service Model of Business

The recent acquisition of the Weather Company by IBM shows signs on what is looming over the horizon. In all likelihood companies will adopt the business model of services that consist of data streams and package and sell the data acquired by them.

The Relationship between Big Data, Christmas and Santa

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As a Big Data enthusiast, I look forward to data analytics playing a crucial role in the holiday season that is upon us. It is no exaggeration if we state that all sorts of people from CEOs to retailers to consumers will wile away the time crunching numbers this particular December.

The holiday season also is shopping for most consumers as, according to a report, no less than 25% of ecommerce transactions in the UK in last year i.e. 2014 were conducted in November and December. Companies face a transition from sales made from physical locations to those made online from desktops and laptops and mobile devices

The Relationship between Big Data, Christmas and Santa

  • Holiday Customers Lack in Loyalty

Loyalty takes the back seats as more and more buying choices are dictated by the capacity of the wallet. And when you have fifty vendors of repute to choose from while buying an item, the financial motive of savings prevails. Big Data lets businesses know the shopping days when their sales peaks.

All this highlights the need for personalization and how it makes them so much more likely to spend. In fact, according to studies personalization leads to 5 to 8 times increase on the return on investment in marketing and even improves sales by 10%. The report also stated that 56% of consumers made purchase decisions and the final purchase through computers. So, companies need to make the most of the data provided by companies like TIBC Spotfire and ShopperTrak.

  • Using Data in December

But the first question that comes to the mind about the use of Big Data during the period that shopaholics are at the peak of their activities, is why does it remain so underutilized. Tableau conducted a study that found that no less than 24% of all retailers based in the UK do not make effective use of data when the shopping season reaches its peak.

  • Use of Data by Consumers

Consumers also stand to gain when using it for the purposes of making purchase decisions. To cite a great example, we may consider the case of the WatsonTrend app. It analyzes data from ratings, comments, use of social media, reviews and other sources of data available on the net. The lists of the app are updated on a regular basis. The data and algorithms form the backbone of the app which may serve as real time shopping guides and even future trends may be predicted.

So, this Christmas shower you near and dear ones with gifts as enlightened consumers, Merry Christmas!

Trends in Business Intelligence in 2016

Posted on by Dexlab

2015 is nearing its end and Big Data has finally come of age. Business intelligence or BI as it is often referred to is also progressing in leaps and bounds.

Which Business Intelligence Software Will you Buy

Many key trends are all set to emerge in the Business Intelligence market in 2016. Spending on traditional BI platforms has almost come to an unceremonious end.

Trends in Business Intelligence in 2016

  • As a matter of fact Gartner predicts a decline of more than 20% on traditional processes related to Business Intelligence in the coming year.
  • This is in sharp contrast to tools regarding data discovery and self-service Business Intelligence like tableau bi software which witnessed an incredible growth of 77.7% in the last fiscal.
  • Seattle is the place to be as both AWS and MSFT Azure bet big in the market for cloud BI. All eyes are now set on Google as we await their counter move making the competition really interesting.
  • Cloud BI services will face a hard battle as they compete with AWS QuickSight and cannot compete with the margins of AWS.
  • In general it may safely be predicted that the industry related to financial services will adopt the cloud with 20% of top financial institutions announcing a Cloud-first or Cloud-exclusive strategy regarding IT.
  • Industry insiders are abuzz and fascinated by the Internet of Things or IoT, but nevertheless it will soon dawn upon people that the data derived from sensors though abundant is for most uses quite useless.
  • Due to this there will emerge huge discussion which should be pragmatic in nature regarding how the data streams from sensors may be cleaned and the necessity of merging static data and that emerge from that of IoT.
  • Smart Cities are the way of the future and give us a fore-glimpse of how data from traffic sensors, transportation logs, social networks, emergency and census personnel may be used to drive quick insights and response plans for the community as a whole to natural disasters and events related to national security.

Source: Forbes

Big Data Strikes The Healthcare Industry With Carolinas Healthcare

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big data courses in gurgaon
A representative from the administrative section of Carolinas Healthcare recently revealed that they are huge fans of Big Data and are extensively harnessing this convenient technology to leverage the quality of facilities they offer their patients.

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Carolinas healthcare is a Charlotte – based firm which is extensively using this new form of data analysis technology at their very own data warehouse to evenly distribute its population of medical treatment seekers. This is helping them make the right choice in finding the most unique cases for their patient base. They are now able to take into consideration various segments that were ignored initially due to the systemic lack in the infrastructure of data management. Now they are counting segments like,environmental and geographical conditions in relation to diseases and are dividing them into segments for better efficiency in determining trends and patterns.

They hope to draw useful conclusions from such studies and to be able to make predictions so that they can minimize readmissions, inappropriate use of emergency aids and take care of the hospitalization procedures.Dr. Michael Dulin M.D. spoke on their latest venture by saying, “It is our firm belief at CHS that to deliver the best hospitalization and healthcare facilities to our patients, we need to make appropriate use of the huge amounts of data that is generated in the healthcare industry”. He is the chief clinical officer at the firm, Dickson Advanced Analytics Group which goes by the name DA2. The unit which was launched back in 2012 and is in its budding years currently. But already comprises of 130 experts who are all working together to make better use healthcare data which is a mountainous amount to begin with. It is of no doubt that Big Data is being of good use to the healthcare industry and there are glorious future prospects for experts concerned with this field, globally.

Dr. Michael further added, that “Taking into considerations the data on genomics, environmental poisons, lab results, demographics, physician’s notes and other data generated based on patients will provide them with the much needed insight required to tighten and personalize the world of health care.

Deep Learning and AI using Python

Current statistics suggest that the data generated in the healthcare world every two years is almost doubling every two years. This is the same for CHS. Thus, it is evident that CHS and other healthcare organizations will require using advanced data mining tools and use statistical methods to cope and thrive in the market.

 

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Big Data And The Internet Of Things

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The data that is derived from the Internet of Things may easily be used to make analysis and performance of equipment as well as do activity tracking for drivers and users with wearable devices. But provisions in IT need to be significantly increased.Intelligent Mechatronic Systems(IMS) collects on an average data points no fewer than 1.6billion on a daily basis from automobiles in Canada and U.S.

Deep Learning and AI using Python

The data is collected from hundreds of thousands of cars that have on board devices tracking acceleration, the distance traversed, the use of fuel as well as other information related to the operation of the vehicle.This data is then used as a means of supporting insurance programs that are based on use.Christopher Dell, IMS’s senior director recently stated they they were aware that the data available were of value, but what was lacking is the knowledge on how to utilize it.

But in the August of 2015, after a project that lasted for a year, IMS added to its arsenal a NoSQL database with Pentaho providing tools related to data integration and analytics. This lets the data scientists of the company increased flexibility to format the information. This enables the team of analytics to make micro analysis of the driving behavior of customers so that trends and patterns that might potentially enable insurers to customize the rates and policies based on usage.

In addition to this the company further is pursuing an aggressive growth policy through asmartphone app which will further enhance its abilities to collect data from vehicles and smart home systems making use of the Internet of Things.Similar to the case of IMS, organizations that look forward to analyze and collect data gathered from the IoT or the Internet of Things but often find that they need an upgrade of their IT architecture. This principle applies to enterprise as well as consumer sides of the IoT divide.

The boundaries of business increasingly fade away as data is gathered from fitness trackers, diagnostic gears, sensors used in industries, smartphones. The typical upgrade includes updating to big data management technologies like Hadoop, the processing engine Spark,NoSQL databases in addition to advanced tools of analytics with support for applications drivenby algorithms. In other cases all it is needed for the needs of data analytics is the correct combination of IoT data.

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Join DexLab Analytics’ Big Data certification course and kick start your career in the rapidly developing sector of data science.

 

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3 Exceptional Free E-Books On Machine Learning

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According to the experts at Wikipedia Machine Learning happens to be computer science sub-field that has its origins in the detailed examination of recognition of patterns as well as the “computational learning theory” as put into practice in the world of A.I. or artificial intelligence.The subject investigates the study as well as the construction of algorithms which have the ability to pick up skills from and make predictions on the basis of the data that is available.

In this blog post we list some of the key texts that help out students and researchers in this particular field of study.

The Math Behind Machine Learning: How it Works – @Dexlabanalytics.

1. Machine Learning, Neural and Statistical Classification

Edited By: D.J. Spiegelhalter, D. Michie and C.C. Taylor

This book has for its base the ESPRIT or EC project Statlog which compared and made evaluations about a broad range of techniques on classification while at the same time assessing their merits and demerits in addition to applications across the range. The volume listed here is the integrated one which conducts a brief examination of a particular method along with their commercial application to real world scenarios. It encourages cross-disciplinarystudy of the fields of machine learning, neural networks as well as statistics.

Uber: Pioneering Machine Learning into Everything it Does – @Dexlabanalytics.

2. Bayesian Reasoning and Machine Learning

Written By: David Barber

The methods of machine learning have the ability to mine out the values out of data sets that are nothing short of being vast without taxing the computational abilities of the computer. They have established themselves as essential tools in industrial applications of a wide range like analysis of stock markets, search engines as well as sequencing of DNA and locomotion of robots. The field is a promising one and this book helps the students of computer science grasp the tough subject even if their mathematical backgrounds are decent at best.

Pandora: Blending Music with Machine Learning – @Dexlabanalytics.

3. Gaussian Processes for Machine Learning

Authors: Christopher Williams and Carl Rasmussen

Gaussian Processes or more known simply as GPs serve as a practical, principled and probabilistic approach to the learning as conducted in kernel machines. The Machine Learning community has been providing increased attention towards GPs throughout the better part of the last decade and the book serves the important function of sufficing as a unified and systematic treatment of the role of practical as well as theoretical aspect of GPs as present in machine learning. There was a long felt need for such a book and it does not disappoint with its self-contained and comprehensive treatment. This book is highly useful for students as well as researchers in the fields of applied statistics and machine learning.

If your appetite for knowledge on machine learning is far from being satiated, contact DexLab Analytics. It is a pioneering Data Science training institute catering for hundreds of aspiring students. Their analytics courses in Delhi are widely popular.

 

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Delimiters And Delimited Data in SAS

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In this blog post we will delve into the world of delimiters as found in the SAS system of data analysis tools. Delimiters are an essential part of SAS without whose guidance SAS would be in some blind inspite of all the data that surrounds it as the data supplied to it either internally or as an external file.

 

sas courses

What are Delimiters?

Delimiters are essentially symbols to SAS that lets SAS know that the data is separate. They distinguish one set or category of data from others. This should give you an idea about how essential a part of data analytics, a delimiter plays.

How are the Delimiters Symbolized?

SAS accepts the following symbols and key strokes as delimiters:

  •  ,
  •  :
  • Tab
  •  ~
  • &
  •  –

How to Import Delimited Data?

This command imports data infile

Data claim_data;

infile datalines dlm = “,”;

Input sex $ name $ claim_amount ; datalines;

Male,Mahesh,15000

Male,Naveen,10000

Female,Neeta,18000

Male,Amit,7500

Female,Geeta,12000

;

run;

data claim_data ;

Infile E:\Project FT\SAS\Course Material\Class 1\Claim Data comma.txt’ dlm = “,”;

Input sex $ name $ claim_amount ;

Run;

What are the Functions of Delimiters?

An INFILE option, the DSD or delimiter-sensitive data serves varied functions. They are as follows:

  • The default delimiters are changed to wanted ones from the default blank.
  • In case a row contains two delimiters, SAS interprets that there is an instance of a case of missing value.
  •  Delimiters also strip the quotes within which character values are placed.

So the command would boil down to:

data claim_data ;

Infile E:\Project FT\SAS\Course Material\Class 1\Claim Data comma.txt’ dsd;

Input sex $ name $ claim_amount ;

Run

How to Read Data from an External CSV file?

Our next task is to read data from external CSV files. In order to do so we have to input the following:

proc import datafile = “E:\Project FT\SAS\Course Material\Class 1\exam_results.csv”

dbms = csv replace out = class_10_result; Getnames = yes; run;

In a Nutshell

What are Format, Informat and DSD?

  • Informat : This command instructs SAS how exactly to going about reading the data.
  • Format : This instructs SAS about the exact way in which to show the details.
  • DSD : This defines how data is separated by a delimiter.

The Role of Big Data in the Largest Database of Biometric Information

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BIG DATA

Aadhaar project from our very own India happens to on the most ambitious projects relying on Big Data ever to be undertaken. The goal is for the collection, storage and utilization of the biometric details of a population that has crossed the billion mark years ago. It is needless to say that a project of such epic proportions presents tremendous challenges but also gives rise to an incredible opportunity according to MapR, the company that is serving the technology behind the execution of this project.

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Aadhaar is in its essence a 12 digit number assigned to a person / an individual by the UIDA , the abbreviated form of “Unique Identification Authority of India” The project was born in 2009 and had former Infosys CEO and co-founder Nandan Nilekani as its first chairman and the architect of this grand project which needed much input in terms of the tech involved.

The intention is to make it an unique identifier for all Indian citizens and prevent the use of false identities and fraudulent activities. MapR which is head-quartered in California is the distributor and developer of “Apache APA +0.00% Hadoop” has been putting into use its extensive experience in integrating web-scale enterprise storageand real-time database tech, for the purposes of this project.

According to John Schroeder who is the CEO and co-founder of MapR, the project presents multiple challenges including analytics, storage and making sure that the data involved remains accurate and secure amidst authentications that amount to several millions over the course of each passing day.Individual persons are provided with their number and a iris-scan or fingerprint is taken so that their identity might be proved and queried to and matched from the database backbone to a headshot photo of the person. Each day witnesses over a hundred million verifications of identity and all this needs to be done in real-time in about 200 milliseconds.

India has a percentage of rural population many of which are yet to be connected to the digital grid and as Schroeder continues the solution had to be economical and be reliable even under low bandwidth situations and technology behind it needed to be resilient which would work even with areas with low levels of connectivity.

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For more information on big data and big data hadoop courses, peruse through the official site of DexLab Analytics. It is a major Big Data Hadoop institute in Gurgaon.

 

Source: Forbes

 

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Sure shot Ways to Crack Big Data Interviews

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Sure shot Ways to Crack Big Data Interviews

If you are a Big Data analyst looking for open position in the entry to mid level range of experience then you should prepare yourself with the following resources in your arsenal before you storm an interview with all guns blazing.

  • Adequate Expertise of Analytical tools like SAS for the processing of data

Make sure that you assign most of the time you have set aside for the preparation of your upcoming interview to brush up your knowledge regarding the tools of analytics that are relevant in your context. Ensure that you acquire proficiency in the analytics tool of your choice. For positions of junior levels the importance of expertise with a particular analytical tool like Hadoop, R or SAS cannot be overstressed. In such circumstances the focus centers around data preparation and processing. It is highly advisable that you review concepts related to the import and manipulation of data, the ability to read data even if it not standard say for example data whose input file types are multiple in number and mixed data formats. You also get to show off your skills at efficiently joining multiple datasets, selecting conditionally the observations or rows of data, how to go about heavy duty data processing of which SQL or macros are the most critical.

  • Make a Proper Review of End to End Business Process

This is most relevant towards candidates who have prior experience at working in the Big Data and Analytics industry. Prior experience inevitably gives rise to interviewers wanting to know more about the responsibilities that you shouldered and your role in the business process and how you fitted in the context of the broader picture. You should be able to convey to the interviewer that the data source is understood by you along with its processing and use.

  • A solid concept of the rudiments of statistics and algorithms

Again this tip is also for those with prior experience. Recruiters seek to know whether you are aware of issues likely to be faced by you while you confront problems regarding data and business. Even freshers are expected to know the fundamental concepts of statistics like rejection criteria, hypothesis testing outcomes, measures of model validation and the statistics related assumptions that a candidate must know about in order to implement algorithms of various sorts. In order to crack the interview you must be prepared with adequate knowledge of concepts related to statistics.

  • Prepare Yourself with At Least 2 Case Studies related to Business

The person on the other side of the interview table will undoubtedly try to make an assessment about your knowledge as far as business analytics is concerned and not solely to the proficiency you command in your tool of choice. Devote time to review projects on analytics you already have worked on if you have prior experience. Be prepared to elucidate on the business problem, the steps that were involved in the processing of data and the algorithm put into use in the creations of the models and reasons behind, and the way the results of the model was implemented. The interviewer might also ask about the challenges faced by you at any stage of the whole process, so keep in mind the issues faced by you in the past and their eventual resolution.

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  • Make Sure that Your Communication Remains Effective

If you are unable to effectively communicate then no much diligent preparations you make, they will be of no use. You can try out mock interviews and answering questions that the recruiter might ask. Spare yourself of the trouble of framing effective answers at the moment when the question is asked during an interview. Though you perhaps will be unable to anticipate each and every question, nevertheless but prior preparation will result in better and more coherent answers.

 

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