Free Demo Session on Credit Risk Modelling with SAS

DexLab Analytics, the eminent data analytics training institute is conducting a free live demo session on Credit Risk Modelling with SAS on the 15th of October at 5PM. Credit risk modeling is that part of credit risk management that develops a framework for calculating risks associated with classical crediting products like financial letters of credit and loans. This is a must have economic tool for maintaining equilibrium within the fiscal arena. So register quickly for this free demo session and get informed on the latest trends of credit risk management. Just send us a mail at hello@dexlabanalytics.com to register for this event or you can visit our website and fill up the contact us form.

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Growing Impact of Hadoop

In this age of data-driven technology huge amount of data are generated daily from a wide range of digital sources. Volume, velocity and veracity are the main features of this kind of data. Various data analytics tools are required to manage this massive amount of data. Hadoop, a data analysis tool is widely used for low cost data storage and ETL.

The distributed storage and parallel processing aspect of Hadoop provides us with reliable, accurate, data storage and evaluating mechanism. That’s why Hadoop has been adopted by industries of various fields. The market of Hadoop is growing constantly but the application of Hadoop in organizations is surprisingly limited. Very few organizations are utilizing the amazing power of Hadoop to predict business behavior; instead it is being used for cost efficient storage and ETL.

The full potential of Hadoop is being exploited by only a handful of tech giants for complex analysis of collected data. The reason behind this reluctant use of Hadoop cannot be explained. Business organizations are treading carefully to avoid any risks that may occur due to early adoption of Hadoop. Data discovery and pattern recognition is a time-consuming process and requires a flexible and unbound environment. Enterprises are using Hadoop for known cases like storage and ETL before going deeper to analytics project. They are mainly getting acquainted with the various aspects of this new technology before using it full fledged.

For now, we have to give the industries some time before they start to exploit the full potential of Hadoop.

 So, enroll in a Big Data Hadoop certification program and become a skilled professional in the field of data analytics. For more information on the potential of Hadoop please visit our blog at the given link: http://www.dexlabanalytics.com/blog/hadoop-adopted-but-not-for-analytics

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Are You Using Ample Data in Your Data Scientist Resume?

Whenever you apply for a job, your first fear is that the resume does not get thrown into the reject pile. Being a data analyst most candidates only list their job history as a long and dry catalogue describing in brief their curriculum vitae (story of your life) of the various positions they held at previous employer’s organizations. Some even outrageously create a table of marks that they scored on various important exams in life. But being a data person, you must not make this rookie mistake of listing unnecessary data.

Things can get harder if you have never held a job role as a business or data analyst before and you have no idea about what to write as the headline of your resume and how to justify your sudden change in career.

If you want to find out an honest strategy for creating an effective business analyst or data scientist/analyst resume without involving petty gimmicks or trickery or unhealthy exaggeration so, that you can avoid the reject pile for sure.

Here are a few tricks that will help you showcase your data analyst like job roles even if you have never had one.

Tip no.1:

A good data analyst resume does not need a catalogue of all your work history. You must learn to optimize even irrelevant job roles as data-driven roles. This is a common misconception among most job seekers who feel that all types of odd job roles must be listed in their resumes. So, the first secret you must know when creating a resume is that it is not a catalogue but rather a sales document.

And what is on sale? Your candidature!

Thus, this means that you must be selectively honest when writing down job roles, skills and experiences that you choose to include into your resume. The key is to optimize and present your resume in a way that presents your qualifications and skills in the best possible way for the job title at hand.

Tip no. 2:

Summarize with specific details so that your resume stays away from the reject pile. This is essential because most recruiters will only scan your resume and not really read it. So, there will be a few handful of materials that will come to their attention.

The first element of your resume that meets the eyes of the recruiter is the summary or objective statement. One of the best kept secrets of the job world is completely opposite to what most people believe. It may seem to the rookie data analyst job seeker, that a vague or generalist statement will work to open up more possibilities, but the reality is starkly in contrast. For a data analyst job profile highlight your analytical abilities and statistical knowledge in your resume objective or statement and get the message through crystal clear that you are qualified for the type of jobs you are applying.

But note that most recruiters also skip right through the objective part of your resume, so, make sure that the other parts of their resume also lures them in.

Let us discuss us how you can catch their eye with those parts...

Tip no.3:

Have your resume read by tweaking the old job titles. Chances are an average recruiter will skip right through your objective section and even the accomplishments part. So, where will their eyes stick? To your recent job titles! So, make sure they are in bold t help them stand out.

Another key point to be kept in mind is that your previous job titles must showcase your skills as the right ones for the types of jobs you are applying. So, if your previous job title reads something like this – Software Developer, Customer Support or Network Engineer, then chances are you will only be hired in such job roles. So, instead of being blunt tweak your recent titles within the bounds of honesty and accuracy. To do this, you must be more creatively honest.

For more tips and articles on how to apply to business and data analyst job roles and on Analytics training institutes in Pune for sharpening your analytical mind, visit DexLab Analytics.

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SAS Still Dominates the Market After Decades of its Inception

According to the 2016 February report by Gartner, the top slot in execution ability was bagged by SAS and SAS was once again placed in the quadrant of leaders in the Magic Quadrant for Advanced Analytics Platforms. Advanced analytics, according to Gartner, involves various sorts of data analysis through the use of quantitative methods of great sophistication like machine learning, statistics, simulation, data-mining. The report compiled by Gartner further states that the advanced platforms for analytics provide a comprehensive environment which develops and deploys models which include:

• ·         Access to data generated from multiple sources
• ·         Preparing, exploring and visualizing data
• ·         Building and developing models for analytics
• ·         Putting in place models while integrating them with the applications and processes of                   business
• ·         Managing of projects, platforms and models
• ·         Ability to scale up as required and high performance while both deploying and developing

SAS Covers All the Bases-

SAS is widely known for its continuous evolution over a period of time and its unique ability regarding analysis. It is also compatible with various open source tools and is effective in deep learning and machine learning abilities.

According to the majority of experts, SAS provides the best in-depth and advanced analysis of products. SAS has proved to be efficient for specific purposes of business as well as for applications that are wider in range. Its advanced analytics applications are used by customers to increase their revenue and lower the costs incurred by their firm.

SAS has also made important investments in emerging architectures like Hadoop, integration with the cloud, in-memory processing and requirements for modeling on a large scale. It has also solved the lack of skilled professionals by integrating the advanced analytics with intuitive and smart user interface. Because of this, ordinary people can become data scientists with the help of tools like Visual Statistics and Visual Analytics.

It also lays stress on applications with specific purposes like SAS Fraud Management, SAS Asset Performance Analytics, SAS Cyber security, SAS Demand Planning and SAS Energy forecasting among others. Therefore, it can be said that despite its age SAS has managed to remain as effective as ever with the software suite provided by it.

For more details on this topic please visit our blog at: http://www.dexlabanalytics.com/blog/decades-on-sas-is-still-the-market-leader

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Installation guide from DexLab Analytics

Install Hadoop in your PC with this guide from DexLab Analytics. Hadoop requires a Linux operating system but can also do with a virtual alternative setup, to understand the nitty gritties of this complex software, acquaint yourself with the main concepts involved with a course from DexLab. Happy learning!

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How Much Do Big Data Professionals in India Earn?

India is not lacking behind when it comes to the prevalence of professionals properly trained in Big Data and analytics and there is room for more. If you are thinking of choosing this field as your career option, this is a must view presentation as we bring to you the pay packages of Big Data professionals working in India.

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13 Advantages of R over Excel in Data Analytics

R holds a significant edge over Excel as far as analysis of data is concerned. Though indeed the number of uses of R programming is less than the utilities of MS Excel it outshines the latter when it comes to the sheer power of conducting analysis and the handling and manipulation of data. Watch this presentation to know more why R holds sway over Excel in the world of Big Data and analytics.

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