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Latest Open Source Tools in Data Analytics Beyond Apache Spark

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Latest Open Source Tools in Data Analytics Beyond Apache Spark

In the IT world change is always in the air, but especially in the realm of data analytics, profound change is coming up as open source tools are making a huge impact. Well you may already be familiar with most of the stars in the open source space like Hadoop and Spark. But with the growing demand for new analytical tools which will help to round up the data holistically within the analytical ecosystem. A noteworthy point about these tools is the fact that they can be customized to process streaming data.

With the emergence of the IoT (Internet of things) that is giving rise to numerous devices and sensors which will add to this stream of data production, this forms one of the key trends why we need more advanced data analytics tools. The use of streaming data analysis is used for enhanced drug discovery, and institutes like SETI and NASA are also collaborating with each other to analyze terabytes of data, that are highly complex and stream deep in space radio signals.

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The Apache Hadoop Spark software has made several headlines in the realm of data analytics that allowed billions of development funds to be showered at it by IBM along with other companies. But along with the big players several small open source projects are also on the rise. Here are the latest few that grabbed our attention:

Apache Drill:

This open source analytics tool has had quite good impact on the analytics realm, so much so that companies like MapR have even included it into their Hadoop distribution systems. This project is a top-level one at Apache and is being leveraged along with the star Apache Spark in many streaming data analytics scenarios.

Like at the New York Apache Drill meeting in January this year, the engineers at MapR system showed how Apache Spark and Drill could be used in tandem in a use cases that involve packet capture and almost real-time search and query.

But Drill is not ideal for streaming data application because it is a distributed schema free SQL engine. People like IT personnel and developers can use Drill to interactively explore data in Hadoop and NoSQL databases for things such as HBase and MongoDB. There is no need to explicitly describe the schemas or maintain them because the Drill has the ability to automatically leverage the structure which is embedded in the data. It is capable of streaming the data in memory between operators and minimizes the use of disks unless you need to complete a query.

Grappa:

Both big and small organizations are constantly working on new ways to cull actionable insights from their data streaming in constantly. Most of them are working with data that are generated in clusters and are relying on commodity hardware. This puts a premium label on affordable data centric work processes. This will do wonders to enhance the functionality and performance of tools such as MapReduce and even Spark. With the open source project Grappa that helps to scale the data intensive applications on commodity clusters and will provide a new type of abstraction which will trump the existing distributed shared memory (DSM) systems.

Grappa is available for free on the GitHub under a BSD license. And to use Grappa one can refer to its quick start guide that is available readily on the README file to build and execute it on a cluster.

These were the latest open source data analytics tools of 2017. For more such interesting news on Big Data analytics and information about analytics training institute follow our daily uploads from DexLab Analytics.

 

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Understanding Time Series Method of Forecasting

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The dictionary meaning of the word forecasting is to estimate what could possibly be the future outcomes within a business or operation. But when it comes to the sector of data analysis this method is used for translating the past data or experiences into future possible outcomes. This is a highly useful analytics tool that helps any company management to cope with uncertainty of the future. For both short term and long term decisions forecasts are highly important.

 
Understanding Time Series Method of Forecasting
 

Forecasting can be used by businesses in several areas, which may include: economic forecasts, technological forecasts, and also demand forecasts. Forecasting techniques can be classified into 2 broad techniques: quantitative analysis (objective approach) and qualitative analysis (subjective approach). For the quantitative method of forecasting technique an analysis of historical data is conducted and the past patterns in data are assumed to predict future data points. While on the other hand in the qualitative forecasting technique, the judgment of experts is employed in the specific field to generate probable forecasts.  These are mostly educated guesses or opinions of experts in that specific area of expertise. Continue reading “Understanding Time Series Method of Forecasting”

India Will Lead in Analytics Services:

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Today is a time when each day is witnessing the field of analytics gets more and more pervasive. It is helping other fields and sectors to achieve more. At a time like this our nation is expected to maintain its ground over other major offshore destinations such as Philippines, China, Eastern Europe and Latin America as per a recent survey.

 

A host of factors will drive the demand for this service from India. They are – availability of talent pool, industry maturity and a wide spectrum of services which was reported by the survey that was conducted by Avendus Capital which is a financial services company. Continue reading “India Will Lead in Analytics Services:”

Understanding the Difference Between Factor and Cluster Analysis

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Understanding the Difference Between Factor and Cluster Analysis

Cluster analysis and factor analysis are two different statistical methods in data analytics which are used heavily in analytical methods of subjects like natural sciences and behavioural sciences. The names of these analytical methods are so because both these methods allow the users to divide the data into either clusters or into factors.

Most newly established data analysts have this common confusion that both these methods are almost similar. But while these two methods may look similar on the surface but they differ in several ways including their applications and objectives.

Difference in objectives between cluster analysis and factor analysis:

One key difference between cluster analysis and factor analysis is the fact that they have distinguished objectives. For factor analysis the usual objective is to explain the correlation with a data set and understand how the variables relate to each other. But on the other hand the objective of cluster analysis is to address the heterogeneity in the individual data sets.

Put in simpler words the spirit of cluster analysis is to help in categorization but that of factor analysis are a form of simplification.

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Difference is solutions:

This is not an easy section for drawing a line of separation in between cluster and factor analysis. That is because the results or solutions obtainable from both these analysis is subjective to their application. But still one could say that with factor analysis provides in a way the ‘best’ solutions to the researcher. This best solution is in the sense that the researcher can optimize a certain aspect of the solution this is known as orthogonality which offers ease of interpretation for the analysts.

But in case of cluster analysis this is not the case. The reasons behind that being all algorithms which can yield the best solutions for cluster analysis are usually computationally incompetent. Thus, researchers cannot trust this method of cluster analysis as it does not guarantee an optimal solution.

Difference in applications:

Cluster analysis and factor analysis differ in how they are applied to data, especially when it comes to applying them to real data. This is because factor analysis can reduce the unwieldy variables sets and boil them down to a smaller set of factors. This makes it suitable for simplifying otherwise complex models of analysis. Moreover, factor analysis also comes with a sort of confirmatory use researchers can use this method to develop a set of hypotheses based on how the variables in the data set are related.  After that the researcher can run a factor analysis to further confirm these hypotheses.

But cluster analysis on the other hand is suitable only for categorizing objects as per certain predetermined criteria. In cluster analysis a researcher can measure selected aspects of say a group of newly discovered plants and then place these plants into categories of species grouped by employing cluster analysis.

Here is an infographic to better explain the difference between cluster analysis and factor analysis: 

 

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The 1st Clinical Trial Predictive Model By Pfizer is Here

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Joining the data analytics bandwagon, the pharmaceutical giant Pfizer has launched their first clinical trial predictive modelling system which is aimed at reducing study risk during protocol design and to better study execution phases. In a recent interview Jonathan Rowe, the Executive Director and Head of Clinical Development Quality Performance and Risk Management of Pfizer shed some light on these predictive modelling systems.

 

The 1st Clinical Trial Predictive Model By Pfizer is Here

 

When asked in the interview about the purpose of their predictive model and what it is meant to achieve, Rowe responded as follows…

 

It is true that there are quite a few models in the realm of GCP quality performance which we have developed and continue to refine. A relatively straightforward one is the correlation model where we correlate our clinical trial process performance to select the results of the GCP as is defined in the ICH E6. Continue reading “The 1st Clinical Trial Predictive Model By Pfizer is Here”

Aspiring Data Analysts Must Know the Answer to These Interview Questions

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Aspiring data analysts must know the answer to these interview questions

You have recently completed Data analyst certification and are hunting vigorously for a job as a data scientist. But the prospect of sitting for such an important job role at a corporate firm in front of a room full of C-suite interviewers is an intimidating prospect. But fear not as we at DexLab Analytics have got you covered both inside the class room as well out.

This megatrend on Big Data analysts started first in 2013, when the leading universities of the world began to realize the gap in between the demand and supply of Big Data professionals. And soon several , Data analyst training institutes cropped up here and there and rooms transformed into classrooms with several students being keen to learn about the steps to handle Big Data  and to join the ranks of data scientists which is a highly sought after profession of these days. Continue reading “Aspiring Data Analysts Must Know the Answer to These Interview Questions”

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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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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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.

For more details on Online Certificate in Business Analytics, visit DexLab Analytics. Their online courses in data science are up to the mark as per industry standards. Check out the course module today.

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