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Quantum Internet Is Now Turning Into a Reality

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Quantum Internet Is Now Turning Into a Reality
 

Scientists across the globe are looking forward towards formulating new methods to realize ‘quantum internet’, an unhackable internet, which connects particles linked together by the principle of quantum entanglement. In simple terms, quantum internet will entail multiple particles striking information at each other in the form of quantum signals – but specialists are yet to figure out what it actually does beyond that. The term ‘quantum internet’ is quite sketchy at this moment. There’s no real definition of it as of now.

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Keep Pace with Automation: Emerging Data Science Jobs in India

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Indian IT market is not yet doomed. In fact, if you look at the larger picture, you will find India is expected to face a shortage of 200000 data scientists by 2020. Where traditional IT jobs are going through a rough patch, new age jobs are surfacing up, according to market reports. Big Data, Artificial Intelligence, the Internet of Things, Cloud Computing, and Cybersecurity are new digital domains that are replacing the old school jobs, like data entry and server maintenance, which are expected to reduce more over the next five years.
The next decade is going to witness most vacancies in these job posts:

However, just because there is a wide array of openings for a web services consultant doesn’t make it the most lucrative job position. Big Data architect job openings are much less in number, but offer handsome pays, according to reports.

A median salary of a web services consultant is Rs 9.27 lakh ($14,461) annually

A median salary of a big data architect is Rs 20.67 lakh ($32,234) annually

Now, tell me, which is better?

As technologies evolve so drastically, it becomes an absolute imperative for the techies to update their skills through short learning programs and crash courses. Data analyst courses will help them to sync in with the latest technological developments, which happens every day, something or the other. Moreover, it’s like a constant process, where they have to learn something every year to succeed in this rat race of technological superiority. Every employee needs to make some time, as well as the companies. The companies also need to facilitate these newer technologies in their systems to keep moving ahead of their tailing rivals.

Re-skill or perish – is the new slogan going around. The urgency to re-skill is creating a spur among employees with mid-level experience. If you check the surveys, you will find around 57% of the 7000 IT professionals looking forward to enroll for a short time learning course have at least 4 to 10 years of work experience. Meanwhile, a mere 11% of those who are under 4 years of experience are looking out for such online courses. It happens because, primary-stage employees are mostly fresh graduates, who receives in-house training from their respective companies, hence they don’t feel the urge to scrounge through myriad learning resources, unlike their experienced counterparts.

 

 

Today, all big companies across sectors are focusing their attention on data science and analytics, triggering major reinventions in the job profile of a data analyst. Owing to technology updates, “The role of a data analyst is itself undergoing a sea change, primarily because better technology is available now to aid in decision-making,” said Sumit Mitra, head of group human resources and corporate services at GILAC. To draw a closure, data science is the new kid in the block, and IT professionals are imbibing related skills to shine bright in this domain. Contact DexLab Analytics for data analyst course in Delhi. They offer high-in demand data analyst certification courses at the most affordable prices.

 

Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

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The fascinating world of Business Intelligence is expanding. The role of data scientists is evolving. The mysticism associated with data analytics is breaking off, making a way for non-technical background people to understand and dig deeper into the nuances and metrics of data science.
 
Business Intelligence: Now Every Person Can Use Data to Make Better Decisions
 

“Data democratization is about creating an environment where every person who can use data to make better decisions, has access to the data they need when they need it,” says Amir Orad, CEO of BI software company Sisense. Data is not to be limited only in the hands of data scientists, employees throughout the organization should have easy access to data, as and when required.

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Skills required during Interviews for a Data Scientist @ Facebook, Intel, Ebay. Square etc.

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Skills required during Interviews for a Data Scientist @ Facebook, Intel, Ebay. Square etc.

Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL

Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.

Machine Learning: You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python.  These algorithms show to employers that you have exposure to how data science can be used in more practical manners.

Data Wrangling: You should be able to clean up data. This basically means understanding that “California” and “CA” are the same thing – a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and and correcting/deleting them.

Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them)

Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.

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What they look for? @ Mu-Sigma, Fractal Analytics

    • Most of the analytics and data science companies, including third party analytics companies such as Mu-sigma and Fractal hire fresher’s in big numbers (some time in hundreds every year).
    • You see one of the main reasons why they are able to survive in this industry is the “Cost Arbitrage” benefit between the US and other developed countries vs India.
    • Generally speaking, they normally pay significantly lower for India talent in India compared to the same talent in the USA. Furthermore, hiring fresh talent from the campuses is one of the key strategies for them to maintain the low cost structure.
    • If they are visiting your campuses for interview process, you should apply. In case if they are not visiting your campus, drop your resume to them using their corporate email id that you can find on their websites.
    • Better will be to find someone in your network (such as seniors) who are working for these companies and ask them to refer you. This is normally the most effective approach after the campus placements.

Key Skills that look for are-

  • Love for numbers and quantitative stuff
  • Grit to keep on learning
  • Some programming experience (preferred)
  • Structured thinking approach
  • Passion for solving problems
  • Willingness to learn statistical concepts

Technical Skills

  • Math (e.g. linear algebra, calculus and probability)
  • Statistics (e.g. hypothesis testing and summary statistics)
  • Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • Python / R and/or SAS languages
  • SQL databases and database querying languages
  • Python (most common), C/C++ Java, Perl
  • Big data platforms like Hadoop, Hive & Pig

Business Skills

  • Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
  • Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
  • Intellectual Curiosity: Exploring new territories and finding creative and unusual ways to solve problems.
  • Industry Knowledge: Understanding the way your chosen industryfunctions and how data are collected, analyzed and utilized.

 

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Curiosity is Vital: How Machine Inquisitiveness Improves the Ability to Perform Smartly

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Online Data Science Certification

What happens when a computer algorithm merges with a form of artificial curiosity – to solve precarious problems?

Meticulous researchers at the University of California, Berkeley framed an “intrinsic curiosity model” to make their learning algorithm function even when there is a lack of strong feedback signal. The pioneering model developed by this team visions the AI software controlling a virtual agent in video games in pursuit of maximising its understanding of its environment and related aspects affecting that environment. Previously, there have been numerous attempts to render AI agents’ curiosity, but this time the trick is simpler and rewarding.

The shortcomings of robust machine learning techniques can be solved with this mighty trick, and it could help us in making machines better at solving obscure real world problems.

Pulkit Agrawal, a PhD student at UC Berkeley, who pulled off the research with colleagues said, “Rewards in the real world are very sparse. Babies do all these random experiments, and you can think of that as a kind of curiosity. They are learning some sort of skills.”

Also read: Data Science – then and now!

Like several potent machine learning techniques rolled out in the past decade, Reinforcement Learning has brought in a phenomenal change in the way machine accomplish their things. It has been an intrinsic part of AlphaGo, a poster child of DeepMind; it helped playing and winning the complex board game GO with incredible skill and wit. As a result, the technique is now implemented to imbue machines with striking skills that might be impossible to code manually.

However, Reinforcement Learning comes with its own limitations. Agrawal pointed that sometimes it demands a huge amount of training in order to grasp a task, and the procedure can become troublesome, especially when the feedback is not immediately available. To simplify, the process doesn’t work for computer games where the advantages of specified behaviours is not just obvious. Hence, we call for curiosity!

Also read: After Chess, Draughts and Backgammon, How Google’s AlphaGo Win at Go

For quite some time now, a lot of research activity is going around on artificial curiosity. Pierre-Yves Oudeyer, a research director at the French Institute for Research in Computer Science and Automation, said, “What is very exciting right now is that these ideas, which were very much viewed as ‘exotic’ by both mainstream AI and neuroscience researchers, are now becoming a major topic in both AI and neuroscience,”. The best thing to watch now is how the UC Berkeley team is going to run it on robots that implement Reinforcement Learning to learn abstract stuffs. In context to above, Agrawal noted robots waste a nifty amount of time in fulfilling erratic gestures, but when properly equipped with innate curiosity, the same robot would quickly explore its environment and establish relationships with nearby objects.

Also read: CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANALYTICS

In support of the UC Berkeley team, Brenden Lake, a research scientist at New York University who lives by framing computational models of human cognitive capabilities said the work seemed promising. Developing machines to think like humans is an impressive and important step in the machine-building world. He added, “It’s very impressive that by using only curiosity-driven learning, the agents in a game can now learn to navigate through levels.”

To learn more about the boons of artificial intelligence, and what new realms, it’s traversing across, follow us on DexLab Analytics. We are a leading Online Data Science Certification provider, excelling on online certificate course in credit analysis. Visit our site to enroll for high-end data analytics courses!

 

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Drawing a Bigger Picture: FAQs about Data Analytics

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Drawing a Bigger Picture: FAQs about Data Analytics

When the whole world is going crazy about business analytics, you might be sitting in a corner and wondering what does it all mean? With so many explanations, notions run a gamut of options.

It’s TIME to be acquainted with all the imperceptible jargons of data science; let’s get things moving with these elementary FAQs.

What is data analytics?

Data analytics is all about understanding the data and implementing the derived knowledge to direct actions. It is a technical way to transform raw data into meaningful information, which makes integral decision-making easier and effective. To perform data analytics, a handful number of statistical tools and software is used and et voila, you are right on your way to success!

How will analytics help businesses grow?

The rippling effects of data analytics are evident, from the moment you introduce it in your business network. And stop rattling! The effects are largely on the positive side, letting your business unravel opportunities, which it ignored before owing to lack of accurate analytical lens. By parsing latest trends, conventions and relationships within data, analytics help predict the future tendencies of the market.

Moreover, it throws light on these following questions:

  • What is going on and what will happen next?
  • Why is it happening?
  • What strategy would be the best to implement?

Also read: Tigers will be safe in the hands of Big Data Analytics

How do analytics projects look like?

A conventional analytics strategy is segregated into the following 4 steps:

Research – Analysts need to identify and get through the heart of the matter to help business address issues that it is facing now or will encounter in the future.

Plan – What type of data is used? What are the sources from where the data is to be secured? How the data is prepared for implementation? What are the methods used to analyse data? Professional analysts will assess the above-mentioned questions and find relevant solutions.

Execute – This is an important step, where analysts explores and analyses data from different perspectives.

Evaluate – In this stage, analysts evaluate the strategies and execute them.

How predictive modelling is implemented through business domains?

In business analytics, there are chiefly two models, descriptive and predictive. Descriptive models explain what has already happened and what is happening now, while Predictive models decipher what would happen along with stating the underlying reason.

Also read: Data Analytics for the Big Screen

One can now solve issues related to marketing, finance, human resource, operations and any other business operations without a hitch with predictive analytics modelling. By integrating past with present data, this strategy aims to anticipate the future before it arrives.

When should I deploy analytics in business?

An Intrinsic Revelation – Analytics is not a one-time event; it is a continuous process once undertaken. No one can say when will be the right time to introduce data analytics in your business. However, most of the businesses resort to analytics in their not-up-par days, when they face problems and lags behind in devising any possible solution.

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So, now that you understand the data analytics sphere and the significance attached, take up business analytics training in Delhi. From a career perspective, the field of data science is burgeoning. DexLab Analytics is a premier data science training institute, headquartered in Gurgaon. Check out our services and get one for yourself!

 

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Sherlock Holmes Has Always Been a Data Analyst. Here’s Why

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The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then using them to build a case through deduction and logic. Finally, following that you will reach a conclusion after analysis.

Sherlock Holmes Has Always Been a Data Analyst. Here's Why

Below quote from Sherlock Holmes is relevant –

“When you have eliminated the impossible whatever remains, no matter how Improbable it is must be the truth.”​

tumblr_mdorpe1mnr1qf5zmno1_500

He always started each case by focusing on the problem.

The problem would sometimes arrive in the form of a letter, sometimes as an item in the newspaper, but most often, it would announce itself by a knock at the door. The client would then present the mystery to Holmes and he would probe the client for salient information. Holmes never relied on guesswork or on assumptions. For Holmes, each new case was unique, and what mattered were reliable and verifiable facts about the case. These gave the investigation an initial focus and direction.

Deduction, Reasoning & Analytics

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

Similarly a data analyst is expected not to assume or formulate theories, which can make the reasoning biased. In his stories, Sherlock Holmes demonstrates his keen powers of observation and deduction from data in front of him. He can decipher how the light enters in Watson’s bathroom based on how his beard is shaved; he attests one person has lived in China from one of his tattoos; he discovers previous financial situation of a man who he had never seen before just looking to the hat the man had just used.

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A data scientist has powerful computational and statistics tools that help him finding patterns amid so much data.

 

In the end, a data analyst’s introduction can be similar to what Sherlock said:

“My name is Sherlock Holmes. It is my business to know what other people do not

know.”

–Team Cosmos

You can learn more about Data analysis by taking up Data analyst certification courses. DexLab Analytics also offers Business analyst training courses.

 

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Go Harder, Longer, Faster, And Stronger With Impressive Corporate Training Programs

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Let’s acknowledge, we are living in a digital world. Whether you attend a business dinner, work in the oil fields or inspect warehouse records, the claws of digital technology grips you daily. Today’s digital world revolves around communications, and Avaya is a pioneer in delivering brilliant communications experiences.

 
Go Harder, Longer, Faster, And Stronger With Impressive Corporate Training Programs
 

The expert consultants at DexLab Analytics – a top-notch big data training institute in India is conducting a three-month long training program for selected officials of Avaya at the company’s Pune branch. The consummate team of Business Intelligence, Data Warehousing and Analytics representatives from Avaya will stay in Pune, till the session is completed.

 


 

Headquartered in Delhi, DexLab Analytics feels extremely honoured in heading such an inspiring event with an acute vision of imparting knowledge and skills to individuals. The diligent team of consultants is going to share deeper insights on subjects, like R Programming, Data Science using R, Statistical Modeling using R, Advance Microsoft Excel – VBA, Macros, Dashboards and Tableau BI & Visualization. The sole purpose of this training is to equip the team of Avaya with modern state-of-the-art data technology so as to give them a certain edge over their rival tailing companies.  

 

In this age of digitisation, and when Modijee is in his endeavour to make India Digital India, how can we ignore the reverberating importance of analytical skills! One of the prime advantages of great analytical skills is that you can take crucial decisions to fulfil your organization’s aims and objectives. The vast amount of real time data is at your disposal, and with them, you can easily achieve success and growth in the future.  Therefore, it is evident that the need for analytical skills is going to swell in the coming years, and DexLab Analytics is a reputable business-analytics training institute, which strongly believes in the growing significance of digitisation using data science and analytics.

 


 

In the context of the above discussion, the spokesperson from DexLab Analytics has this to say –


 

“DexLab Analytics with its team of seasoned corporate trainers offering valuable insights about the high-in-demand skills, like Big Data Hadoop, Business Analytics, R Programming, Machine Learning, SAS Programming, Data Science, Visualization using Tableau and Excel are seeking ways to fabricate a path towards corporate training excellence in the wide-encompassing field of Big Data and Data Analytics. Our intensive training module will help officials confer an exhaustive analysis of a newer domain of data science, which will make them more data-efficient and data-friendly.”

 

Recently, the expertise in big data has been recognised as a major component for achieving success in the advanced digital world and the concerned representatives are acknowledging this impressive view. So, let’s hope this take on data analytics motivates more people, paving new roads for data-centric ideas and modules in the near future.

 

Are you looking for intensive SAS courses in Pune? Visit DexLab Analytics and scan through a list of encompassing SAS training courses in Pune.

 

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Shadowing a Data Architect for a Day!

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Shadowing a Data Architect for a Day!

A data architect is a noteworthy role in the present analytics industry. One can naturally evolve from a data analyst or a database designer to a data architect after gathering sufficient experience in the field. The prominence of this role showcases the emergence of the online websites and other internet avenues which require the integration of data from several unrelated data sources.

These data sources can be anything from:

  • External sources, like market feeds (for e.g. Bloomberg) or other News Agencies (like, Reuters)
  • Or they could be internal sources like exiting systems that collect data, for instance HR operations that gather employee data

Here is a depiction of a day in the life of a successful data architect:

Data analyst certification from a reputable analytics-training institute can help to speed up your process of evolution from being a data analyst to becoming a successful data architect!

 

Shadowing a Data Architect for a Day! from Infographics


 

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