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As 2016 prepares to wrap up, inboxes are flooded with newsletters on ‘Big Data Trends for 2017’. In a recent 6096058506, CEOs and data experts have put forth their views on how AI is the future of big data.

AI will inform, not just perform, across industries ~ Alan O’Herlihy, CEO of 601-465-7790.

Following suit, even Quentin Gallivan, CEO of (801) 223-9623 opined in the same article-

The Early adopters of AI and machine learning in analytics will gain a huge first-mover advantage in the digitalization of business.

While a host of predictions and estimations are doing the rounds, we’ll tell you exactly where you can get all these information!

Yes! You guessed it right. Big Data summits, meetings, conferences and events are where you get to know the new trends, upcoming launches, and other such details. So, rather than telling you about the predictions, in this article, we invite you to be a part of the Top 5 Big Data Events of 2017.

Note: Many events below have bookings open, while some have already closed down. But worry not, even if you cannot get through the events this year, we promise you to bring updates from each of these events right here.

1. Global Artificial Intelligence Conference

Location: Santa Clara, California
Date: January 19, 2017 – January 21, 2017

This is a 260-729-1287 that brings together AI thought leaders of the industry. Artificial Intelligence is touted as the future of big data. This conference, riding on the back of this notion, will provide you with insights and solutions to several persistent AI issues. About 50 leading experts from AI space will grace this conference.

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You can register as Press, suggest a topic or ping for a Workshop, and also purchase tickets. For more details about the conference, and ticket bookings, visit the official website.

2. Big Data Innovation Summit

Location: Las Vegas
Date: January 25, 2017 – January 26, 2017

Looking to meet influential people from the big data industry? This event is where you should be. At LA, the (951) 523-1997 will bring you unprecedented opportunities. You can request to speak, and also request an invitation to be a part of this Summit. With some of the top notch sponsors, this LA-based summit is going to be all about machine learning, AI, market intelligence and data strategy. Here’s an executive summary for you:

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You can avail group discounts. To register, acholous.

3. Predictive Analytics World Manufacturing

Location: Dusseldorf, Germany
Date: February 2, 2017 – February 3, 2017

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The German Government coined the term for Hannover Messe 2011 (A German Word) – Industry 4.0. It is rare for a government to take such awesome initiatives. This term defined the ‘integration of industrial production with intelligence information technology. To take things a step ahead, Predictive Analytics World Manufacturing, led by industry experts will unveil the varied possibilities predictive analytics holds for the future. You can 646-466-4903 as a participant, speaker or a sponsor. This two-day event will be graced by eminent people like (647) 279-7114, a University professor of the 3162971683; and many others.

4. 3rd Annual Big Data and Analytics Summit

Location: Toronto, Canada
Date: February 13, 2017 – February 14, 2017

While common people are busy planning romantic outings, data nerds will be flocking to Toronto for the 3rd Annual Big Data and Analytics Summit. Once a buzzword, today big data is a trend that is evolving every day. It is reshaping businesses and society. As the competition gets fierce in the data world, big data experts will join together at Canada to discuss, decide and get ready to implement:

  • A data driven organisation
  • Analytics-based business decisions
  • Enhance customer experience
  • Unveil hidden opportunities that data analytics can provide
  • Monetise big data.

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You can register for the Summit 620-723-5030. Speakers include data experts like Jack Y. Chen, peachen, Bala Gopalakrishnan, Ted Maulucci, and many others. For all the latest updates, follow BIG DATA Summit on Twitter.

5. Gartner Data & Analytics Summit

Location: Grapevine, TX
Date: March 6, 2017 – March 9, 2017

Gartner, an American Research and advisory firm, is hosting its data summit in March 2017. The firm that delivers technology research data to businesses, is now gearing up to welcome data fanatics under one roof. Gartner has time and again emphasised that data has infinite opportunities. Thus, it is no surprise that the 7066054868 will be about unbounded information and limitless connections. Data and analytics will take the limelight, and discussions will range from building an effective and holistic data strategy to establishing information governance for enhanced quality, better security and tight privacy.

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The hot topics include:

  • IoT
  • Machine learning
  • Customer analytics
  • Real-time predictive analytics
  • Monetising of data
  • Data governance and quality
  • Blockchain
  • Chief data officer and Chief analytics officer.

Click here to register. For other details, you can check the official site.

So, get your tickets booked and travel plans ready, because 2017 will be all about big data and analytics. If you are looking for an event in and around your region, you can check this comprehensive list from KDnuggets: (614) 787-2799

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In 2009 data governance got a new perspective with the book The Data Governance Imperative by Steve Sarsfield. A leading expert in data quality and governance, Steve Sarsfield is a renowned name in the data industry. His focus has always been on the business perspective of data. Steve believes that this practical approach is very vital to data champions, front office employees and executives.

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Steve Sarsfield owns the globally recognised blog Data Governance and Data Quality Insider. He is a prolific speaker on the same topic and has graced countless presentations at industry conferences and college campuses. Steve Sarsfield is also a member of the organising committee for the 2009 MIT Information Quality Industry Symposium (IQIS). IQIS encourages discussions amidst practitioners and academicians to discover better ways to improve the data quality.

In an interview with Data Quality Pro, Steve Sarsfield mentioned:

Since data governance is a business strategy, selling it to the business involves understanding the company’s pain, understanding the procedures that are currently in place, good or bad, and making changes to alleviate the pain and make processes more efficient.

Saying so, he had cited commercials from IBM. According to Steve Sarsfield, these commercial effectively illustrated the above points.

Steve Sarsfield used the above commercial ad to highlight one simple fact-

Selling data governance needs to reflect economic gains and should never get into the fine details of database sizes, metadata management nor nulls and duplicates. Leave that type of discussion for the technical team meetings.

Steve Sarsfield is said to derive his wisdom and inspirations from his colleagues at Talend. This is very much evident from his paper The Butterfly Effect of Data Quality. In this paper, Steve Sarsfield compared the growth of a butterfly with the nurturing of data.

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Not just data, Steve is a man of words too. His Twitter profile is a proof of that. While his tweets are informative, they are engaging as well. For instance, this tweet where the term #datafabric caught his attention!

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Steve Sarsfield has been associated with Harte-Hanks Trillium Software as Product Marketing Manager prior to joining Talend. Currently, he is associated with HPE Vertica.

Steve is a very social person. You can connect with him on LinkedIn and Twitter.

Influencer of the Week: Annie Pettit, a Market Research Methodogist

Annie Pettit is a market research methodologist for the last 15 years. Annie Pettit holds a bachelor’s degree in Psychology and has a keen interest in market research. Her research involves understanding how the market research industry will branch out into data science, AI, facial coding, and IoT, to be brief.

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Annie Pettit is a prolific writer with a quirky sense of humour. Her recent book, “People aren’t Robots” is a practical guide on how to design a questionnaire. We often complain about cliched questions being asked. Annie Pettit’s book aims to provide deeper insights into the psychology and techniques of designing a questionnaire.

Her credits as an author don’t end here. Annie Pettit has co-authored several publications. For instance, Social Media in Social Research: Blogs on Blurring the Boundaries. Annie Pettit also contributed to 10 Answers to Contemporary Market Research Questions along with 20 other experts.

straw sedge is another masterpiece from Annie Pettit. If one says research cannot be fun and interesting, The Listen Lady will prove her/him wrong. This book is a combination of lucid storytelling and market research education. It will take its readers through the processes, pros and cons of social media listening research.

Annie Pettit blogs at The Love Stats, where she pens down her opinions about surveys, big data, charts, statistics, and so much more. At times, she does spend her time in experimenting with her baking and gardening skills. Her 918-683-6417 timeline is a living proof!

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Her blogs have been published in two volumes: The Love Stats: 8184271350 and Volume 2. Each book has a compilation of her year’s blogs.

In June 2016, FMRIA honoured Annie Pettit with 6187089192. She has a string of awards and honours to her name, all well deserved. She also received the 517-809-7216 in June’16.

Annie Pettit is a regular speaker and doesn’t flinch from stating the facts. Her contribution to market research industry has earned her accolades and praises. She is an inspiration.  

Top 10 TED Talks on Data Science You Cannot Miss

So data science has the power to take humans from Earth to Mars? That’s how data science and rocket science is growing. In a time when data science is solving complex issues, here are ten TED talks that every data scientist or an aspiring data scientist should listen.

8722561568 is a nonprofit organisation that brings into limelight ideas that can change the world for the better. The annual conferences and local TEDx events are inspiring as well as informative. They bring notable personalities to speak. These are not mere speeches, but an indication to break conventions when needed, and contribute to the best abilities in transforming the world for us all.

Here are the top ten TED talks on data science that will change your notions and inspire you thoroughly.

1. The Best Stats you’ve ever seen

Hans Rosling breaks the myths of the so-called ‘developing world’ with linear data representation. Rosling specifically mentions how the “data of what’s happening in the world and the child health of every country is very well aware.” He talks about the economic conditions of each country and the world in the whole. What we think about the “developing worlds” is just a facade. Statistics Guru Rosling debunks the myths in this TED talk: The Best Stats You’ve Ever Seen.

2. How Not To Be Ignorant About the World

Another masterpiece from Hans Rosling, this time accompanied by his son Ola Rosling. Hans Rosling opens the TED talk with three MCQs for the audience. The TED talk deals with the global environmental issues and the impact on living entities. Hans Rosling, with his famous charts on global population, income data and health, points out that we have put our faith into some very wrong information. Ola Rosling then takes ahead the talk How Not To Be Ignorant About the World by sharing 4 quick ways to be less ignorant.

3. What makes a good life? Lessons from the longest study on happiness

Psychiatrist Robert Waldinger says fame and money are not what fuels our happiness. These are momentary and will pass. But for a healthy and happy life, you will need to learn these three important lessons that Waldinger talks about.

…What if we could watch entire lives as they unfold through time? What if we could study people from the time that they were teenagers all the way into old age to see what really keeps people happy and healthy?

We did that. The Harvard Study of Adult Development may be the longest study of adult life that’s ever been done. For 75 years, we’ve tracked the lives of 724 men, year after year, asking about their work, their home lives, their health, and of course asking all along the way without knowing how their life stories were going to turn out.

With unprecedented data at his disposal, Waldinger explains the practical facets of a goof life. Listen to What makes a good life? Lessons from the longest study on happiness now.

4. How I Hacked Online Dating

I’m going to keep using these online dating sites, but I’m going to treat them as databases, and rather than waiting for an algorithm to set me up, I think I’m going to try reverse-engineering this entire system. So knowing that there was superficial data that was being used to match me up with other people, I decided instead to ask my own questions. What was every single possible thing that I could think of that I was looking for in a mate?

Now this is one of the fascinating topics! Amy Webb had a bad luck with dates, and like any other data enthusiasts, she started maintaining a spreadsheet. Listen to her findings right form her spreadsheets. How I Hacked Online dating will nudge you on how data can help in almost anything you can think of.

5. Who Controls The World?

Complexity fascinates James Glattfelder. In this TED talk on Who Controls the World, Glattfelder exposes how a small section exercises control. It is a groundbreaking study on where the power lies and how it controls the global economy.

The world is interconnected, but sometimes we fail to realise how deeply interconnected we are. Glattfelder supports his arguments with interesting data, and it is marvellous.

6. Global Population Growth, Box by Box

Hans Rosling, a global health expert, raises an important question in this TED talk. How can we check the population growth? As the world’s population is all set to touch 9 billion in another 50 years’ time, Global Population Growth, Box by Box puts together all the data. Rosling and his extensive research indicate that only by raising the living standards of the poor can we control population.

7. The Beauty of Data Visualisation

It feels like we’re all suffering from information overload or data glut. And the good news is there might be an easy solution to that, and that’s using our eyes more.

David McCandless talks about the impact of visual data. In The Beauty of Data Visualisation TED talk, McCandless says that designing information makes more sense. Patterns help us in understanding data better, making way for well-informed decisions. The complex data sets can take a backseat now, as we shift to a more appealing way of analysing and understanding data.

8. Social maps that reveal a city’s intersections — and separations

Social maps that reveal a city’s intersections — and separations is an interesting take on data. What you share on your social profiles can actually give a sneak-peak into the kind of life your city has, the type of people that resides in your city and other such facets. Dave Troy has been visualising the tweets in his own hometown of Baltimore, and he has enough reasons to believe that data can give you more insights than you can think of.

Social maps is what businesses are looking for. Marketers would readily exchange their fortune for data like this! If you are into data science or wish to be a market analyst, this TED talk is a MUST for you.

9. Battling Bad Science

Science has progressed but then there are the adversities as well. Every day we hear of a new health advice. We don’t know if its good or bad. But Ben Goldacre has put together a host of data to show how an evidence can be distorted and manipulated. A doctor and epidemiologist himself, Goldacre brings into limelight how misconceptions can cause severe damages.

Here’s one such example that he gives in this talk- Battling Bad Science:

An Australian study in 2001 found that olive oil in combination with fruits, vegetables and pulses offers measurable protection against skin wrinklings.” And then they give you advice: “If you eat olive oil and vegetables, you’ll have fewer skin wrinkles.” And they very helpfully tell you how to go and find the paper. So you go and find the paper, and what you find is an observational study. Obviously, nobody has been able to go back to 1930, get all the people born in one maternity unit, and half of them eat lots of fruit and veg and olive oil, and then half of them eat McDonald’s, and then we see how many wrinkles you’ve got later.

10. The Curly Fry Conundrum: Why Social Media “likes” Say More Than You Might Think

Your random likes and shares on social networking platform Facebook are giving out a lot more information than you can think of. Remember that curly fries you recently liked on Facebook? Well, Computer Scientist Jennifer Golbeck reveals in (785) 435-6752 how the application is technology is not all that mushy-mushy, and why exactly should the control of information be handed over to its rightful owners.

Expand your horizons as Golbeck shares mind-boggling stats on how information is being controlled while you are unaware of the “how” and “what”.

Data has the potential to change the way the world works. With the advancement in research and focus on data science, it isn’t long when we will have answers to the most complicated questions; and those answers will be strictly data-driven. Listen to these TED talks and get inspired- Data can do wonders, and it is just the beginning.

5 EBooks to Read before starting a career in Machine Learning

If you want to bolster your understanding of machine learning or looking to take a plunge into a machine learning career, you have to start somewhere. Either you could go for a formal education by enrolling yourself in a course or you could take an online course. But if certain constraints prevent you from doing either of those, you could get your hands on these 5 best eBooks and augment your understanding of machine learning. Even if you have enrolled yourself into a course (offline or online) these books will serve as good supplementary materials.

Continue reading “5 EBooks to Read before starting a career in Machine Learning”

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“Everything we do in our increasingly digitised world leaves a data trail. This means the amount of data available is literally exploding” ~ Says Bernard Marr in his recent book Big Data in Practice: 479-651-8998.

Bernard Marr has consulted some of the biggest names in the business world, like Accenture, Gartner, Toyota, Barclays, Ministry of Defence, Microsoft, and so many more. Today, he is the founder and CEO of the Advanced Performance Institute. He is held at the highest altar in the big data world. Bernard Marr’s innate capability to understand data and extract information capable to turn the fortunes of a company made him what he is today.

He has to his credits more than 500 articles and reports, including internationally acclaimed ‘Key performance Indicators‘, ‘The Intelligent Company‘ and ‘865-232-8583‘.

Bernard Marr believes that the business industry is making a mistake in shifting to individual reporting. According to his recent article on Forbes on kedlock

“If companies only offer self-service analytics they run the risk that people miss key insights, misinterpret the data or perform the wrong analysis.”

Bernard Marr is a keynote speaker, an author, a consultant in big data, analytics and enterprise performance, a frequent contributor to the World Economic Forum and Forbes. LinkedIn recognises Bernard Marr as one of the top 5 business influencers in the World.

According to a report from Transparency Market Research, big data market is slated to reach US$48.3 billion by 2018. With the growing penetration of data science, it isn’t surprising that more and more people want to learn data science. Since not everyone can learn from Bernard Barr in-person, recently Data Science Central has put together few of the best data science articles from the man himself: 10 Great Data Science Articles by Bernard Marr.

Bernard Marr keeps sharing free tips and articles for all those who want to learn. His approach towards data and research surrounding it makes all his works immensely informative. Bernard Marr has a vivid social presence. He is active on Twitter, has a Youtube channel and Slideshare channel, and regularly contributes to renowned journals over the web. All his books are 843-575-7629 (along with Kindle Editions).

Why are companies hiring more data scientists ?

A data scientist should be having a combination of analytics, machine learning, data mining and statistical skills and at the same time he is also has experience with algorithms and coding. But the most important skill of a data scientist is the ability to explain the significance of data in a way which can be easily understood by others. “Maybe not for next year, but that is the direction that business is going. The complexity of managing any large organization effectively has escalated dramatically, and success in the future is going to depend on how “pique ”

There is an ambiguity around the title of data scientist and hence sometimes it is disparaged because it lacks specificity and can be perceived as a superior synonym for data analyst.  Regardless, the position is gaining acceptance with large enterprises which are interested in gaining meaningful insights from big data, the voluminous amount of structured, semi structured and unstructured  data that a large enterprise produces.

The incident of hiring data scientist is very high there are 7,500 companies in US hiring data scientist. It is not limited to a particular sector or industry , search engines like (Google, Microsoft), social networks such as (Twitter, Facebook, LinkedIn), financial institutions, Amazon, Apple, eBay, also the health care industry, engineering companies (Boeing, Intel, Oil industry), retail analytics, mobile analytics, marketing agencies, data science vendors (for instance, Pivotal, Teradata, Tableau, SAS, Alpine Labs), environment. It is not only limited to private player government and defence that routinely hire data scientists. Sometimes the job title is different like in traditional companies such as manufacturing companies; they tend to call them operations research analysts.

Initially there was a very high demand for data scientist but with time many people have been able to acquire skills and are able to meet up the existing requirement. According to a study conducted by RJMetrics and reported in Forbes, the number of people using data science titles in LinkedIn profiles has doubled in the past four years. Simultaneously the listings for data science job listings have also increased — by 57% between the first quarter of 2014 and the first quarter of 2015 according to Indeed. Several of those job listings are listed with a terminology change; hence there has been a considerable increase. The title data scientist being replaced by labels such as statistician, analyst, or a researcher, changing the titles for jobs but, still drawing on similar underlying skills. Some of the growth is certainly real in terms of number of data scientist jobs, and also there has been growth in the number of academic degrees in data science and related fields and in the number of students pursuing those degrees.

There are definite signs that the number of data scientists is growing this will be able to meet up the increasing demand in the market. Looking on at another pressing requirement as pointed out in the McKinsey report that, it is estimated about ten times more data-savvy managers as compared to data scientists would be needed to take advantage of the data revolution. And it seems that there is not much progress is being made in growing the data competency of managers. Some of the managers might be aware of the growing importance of data, but they believe that they address the issue by hiring data scientists or even whole teams of data scientists. But the real difficulty is how effective those people and teams can be if the data competency of who manages them and whose work is interdependent with theirs is not that different than it was back when data was small and scarce.

With the advent of the online culture and the amount of data being generated, most of the companies will need data scientist to understand and make valuable decisions based on the data. There are also big manufacturing companies like Ford and GM which are integrating huge quantities of data – from internal and external sources, from sensors and processors – to reduce energy costs, improve production times and boost profits. Application of big data is not limited to the private sector even government can make use of it to reduce costs. A 2013 Meritalk survey found that federal IT experts believe Big Data could help the government free up nearly $500 billion per year. Big data is here to stay and so are the data scientist jobs.

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The role of IT in business-led Data Governance

Data Governance is well defined by many research companies and a wider perspective brings into focus data quality, data lifecycle management, metadata management, master data management, privacy, security and all aspects of enterprise data asset.

In some organizations it might be due to lack of business involvement and sponsorship, whereas in some other organisations it would be due to inability to identify data owners or reluctance to share data.

Many companies are organized along lines of business, system and application complexity have increased steadily, the decision making process has become decentralised and data has become silced. Important business information is generally not synchronized and spread across silos and may lead to conflicts across systems. In many cases, there is no single data steward or even enterprise-level data governance.

Data management cannot be done by business or IT alone. it is imperative that both the business front as well as the IT front work in coordination and partner in all data management efforts. It is considered that business side should lead and guide the data management programs, but for gaining productive outputs from data it is utmost to take both the domains along.

The most effective way to use IT as an effective tool is by centralizing it. IT is being centralized by many organizations, this helps in managing data centrally and achieves economies of scale, improve productivity and effectively manage information. By the use of this structure IT is able to identify issues or problems which span line of business and geographies, like enterprise data issue. Generally IT is the first to recognize issues.

Most of the times IT is not in the position to suggest changes to processes to improve data quality. To fix data quality and data issues resources needs to be assigned, it is not a responsibility of the IT department alone, but it is a responsibility which is to be shared equally between the two partners i.e. IT and business. Defining a proper role for both the fronts as it leads to delegation of responsibility, ownership of data and maintains quality of data.

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For better understanding of role of IT in data governance it is important to understand aims and objectives of a data governance program.

  • Maintain integrity of information
  • Keeping information secure, at the same time providing easy access
  • Facilitate decision making
  • Improve services provider to internal as well as external customers and partners
  • Help in collaborating information
  • Remove technical and business obstacles
  • Bring standardization in corporate data definitions, policies and processes, which helps in sharing, interpreting and using data more easily
  • Manage data as an asset
  • Through coordination of efforts improve efficiency and reduce costs
  • Ensure transparency

IT will provide tools and technology needed to achieve them, whereas the business side needs to own the data governance program and needs to consider data as an corporate asset and not only a IT function. There has to be a set of well documented guidelines for defining the role of both the domains so as to minimize the conflicts and aid collaborations.

Data governance program needs many roles such as, Steering Committee, Governance Lead (DGL and Data Governance Working Group (DGWG), each has representative from business and IT. IT needs to ensure the future state of information and application architectures meet the needs of data governance. It should align program to business goals and objectives. It also has to ensure that the organization information technology is sustained and extended, at the same time maintaining a consistent view of end to end view of business processes.

To be able to meets its goals, IT needs to provide leadership, technical infrastructure and resources that collaborate with the business to identify data issues, provide solutions to any unseen problems and implement solutions.  A few of the roles are, Data Architect, Data Custodian, Enterprise Architect, Metadata Lead, Data Quality Lead, IT Partner and IT Leadership to provide leadership, vision and oversight.

The main function of IT is to provide technology and technical infrastructure for the management, storage, access, security, navigation, movement and transformation of data. IT needs to also ensure the program’s strategy aligns with the IT design methodology, development process methodology and best practices. By working together with business side IT plays an important role in root cause analysis on data issues and building remediation plans. It is also the role of the IT to measure, monitor and report on data quality and deliver stakeholder service based on service level agreements.

To run a successful data governance program in any organization, IT has to understand its importance to each business stakeholder, what are the benefits and how best to work together with the business side. When IT clearly articulates the benefits for each business function, sponsorship, ownership and partnership are more easily achieved.

Some good reads if you want to know more in Data Governance & Stewardship

Career Path for a Java Developer in Hadoop or Big Data

To progress, to grow and to be a step ahead of the growing competition a professional needs to constantly update himself/herself. This can be done by acquiring new skill sets, learning new technologies and by identifying the new hotpots in the domain. An analysis of the latest trends and technology space in the industry can help an individual to identify the growing niche in the sector.  Big data is the buzzing area in the IT industry, which is growing at a rapid pace. Big data is crucial for businesses; there is a dire need to collect all the data generated for the fear of missing out on important information. Big data on its own will not be of any help to anyone, but deciphering using inference by using analytics in big data has led to improving business, decision makings and providing edge over the competitors.

There is an increasing demand for Hadoop professionals as Hadoop is the basic platform used in big data. Also the growth of hadoop professionals is much higher than the Java counterparts, hadoop professionals gets a 250% salary hike according to an Industry analytics report.  Google jobs trends data also indicates that hadoop job trend is much better than java job trend. Hence many java professionals are updating and migrating or have already migrated towards Hadoop for better growth opportunities. Hadoop is written entirely in Java hence it will be easy for any java developer to learn hadoop. You can get more insights on Java to learn Hadoop.

There are lot of developers specializing in java, but to differentiate from the competition and to advance in a career, it is imperative for Java professionals to learn hadoop. There is a huge demand for java based hadoop professionals all over the world. Businesses around the world have to shift to hadoop to gain maximum output from big data. Some of the advance features of hadoop are presently available only via Java API. A Java professional, with expertise in Hadoop will find it easier to go deeply into the Hadoop codes and it would lead to a better understanding of the functionality of a particular module and this gives an edge to Java professionals over other professionals.

There are many opportunities generated because of big data, traditional software professionals can learn big data and advance in their careers.  Once a java professional learns hadoop it opens up many opportunities, such as big data tester, big data engineer, big data scientist, big data architecture, business intelligence engineer and data analyst.  According to a big Data executive survey in 2013, around 90% of organizations have introduced hadoop related projects and hence there is a huge demand for hadoop related skills.

The switch from java to hadoop not only increases the job option but also gives better earnings.  It’s a fact that nine out of the top ten highest paying IT salaries are for programming languages, databases and Big Data skills.  A Java Hadoop developer is likely to earn an average salary of $150,000 annually, whereas a Senior Hadoop developer in the New York area can average up to $180,000 annually. Having skills – both Java and Hadoop will place you in the $110,000 pay bracket. Sumit Arora, a Full Stack Software Engineer With Cloud,Networks and Web says, “If you would like to design your career path like your target to reach to a dream employer of yours, as different employers have different business cases hence they have different implementation requirements So they need varied skill set from Hadoop eco system. ”

Java will definitely be an advantageous for learning hadoop, but it is not a mandate. Hadoop is no magic beans, even after learning hadoop a professional has to put in hard work, dedication and constantly update skill in hadoop as well to progress.

10 Interview questions every aspiring Data Analyst must be able to answer

Big Data has been around for more than a decade and the amount of data being generated is only increasing exponentially. Wal-Mart, world’s large company by revenue, handles more than 1 million customer transactions every hour, feeding the databases with an estimated 2.5 petabytes and more. With the social networking boom sites such as Twitter, Facebook, and others are data magnets with people pouring on data in every format, Facebook only has 40 billion photos.  Decoding the human genome which involves analysis of 3 billion base pairs and it took ten years the first time it was done in the year 2003, but with the advancement of technology can now be achieved in one week.

As the amount of data generated has increased many folds, there is a need for well trained data analyst or data scientist to extract meaningfulness from the large amount of data. The main objectives of a data analyst are to obtain, analyze, and report data insights ranging from business metrics to user behaviour, based on the available data. Data science has been adopted by all companies across all industries. It is a very lucrative career option for an aspiring person. Among the various industries the top five industries hiring big data-related expertise/experts, are Professional, Scientific and Technical Services (30%), Information Technologies (19%), Manufacturing (18%), Finance and Insurance (10%) and Retail Trade (8%). The requirement for computer analysts with big data expertise has increased to 89.9% in the over the last year, and 85.40% for Computer and Information Research Scientists.
As data analysis is a very challenging profile which needs multiple skills, the interview can determine the future of the candidate.  There are 10 probable questions which can assist a aspiring data analyst to clear most of the interview successfully.

What do you understand by Big Data?

The phrase Big data is one of the recent buzz word which is often misunderstood, so its better to have a good grasp on exactly what big data means. Giving a thorough outline of the fundamental aspects first, followed by an example, illustrations to which both you and your interviewer can relate to easily.

Do you have prior experience in data gathering and data analytics?

Having a prior experience in the analytics domain is always an advantage over the competition and if the experience is relevant and applicable to post applied for, the interviewer would like to hear the details about the responsibilities and the daily schedule of activities carried out. Even there is no experience at all then a person can elaborate on the summer projects/internship carried out and how they have worked on data analytics.

What are some of the biggest challenges faced by you while handling big data?

These question is asked in any interview process, as this gives an understanding of how a person can handle a difficult situation when need arises.  As big data analysis is a challenging role where the analyst has to face demanding situation on a daily basis it is imperative to have experience in handling and dealing with problems. The best way to tackle the question is to prepare one or more case studies, where you have encountered a problem, how did you handled it should be explained in details and what was the benefit to the company form the entire process. You can also add your personal learning.

How would you ensure data quality and data validity?

The biggest problem faced by big data analyst is the data quality. The data might be inaccurate, incomplete, not easily understandable, inconsistent, and unpredictable or is it beneficial to meeting a company’s goals. Hence there the interviewer will definitely like to know how you will solve this particular problem. To tackle this question you can discuss the statistical measures such as taking averages, find medians, and/or double-checking questionable entries, finding alternative research to support your findings or consult specialists.

Will you be comfortable working on any domain or do you have any preference?

Interviewer would prefer a person who has experience across domains and will be able to pick up the key niches particular of any domain.  But even if the experience is limited candidate can emphasis on the various tools and techniques handled and show an aptitude to learn.

What is your technical expertise and how will you rate yourself?

To analyse such huge amount of data, the data analyst uses certain software. The type of software will wary from most basic Excel and access to handle data, querying languages like SQL or MySQL, awareness about big data tools (like Hive or Pig), statistical programming languages like R or python.  A sound knowledge about the various tools and at least an awareness of what other tools are used are preferred for big data analytics.

How will you report and present data?

A good reply will encompass the use of graphs, tables and charts and along with that a short report on the data available, highlighting a few important trends.

What are the steps to be followed for an effective data analysis procedure?

A good analyst will first decipher the type of data which is present, its lineage, where it’s coming from, he/she may use excel for a basic level analysis after cleaning the data. Then the data analyst can use data analysis tools such as Minitab, SAS, SPSS, advance Excel, etc. and able to come up with a proper answer to the question/problem being studied. After this step whatever results are generated needs to be interpreted and presented in an easy understandable format. .

Can you describe a big data project you have worked on?

The interviewer wants to know how much in-depth knowledge you have gained and how you can apply it to the new role. Giving the details to a technical team and avoiding the technical aspect for a business person will be the key to the answer.

If there is some problem at the clients end how will you communicate and solve the problem?

Data scientist generally are considered to be short at interpersonal and communication skills. Hence it is really imperative to answer the question demonstrating how effectively he/she is able to communicate and over the problem scenario. It is necessary to make the data simple and under stable to the higher management without causing confusion and misunderstanding.

These are the most common question faced by data scientist. But the candidate also has to face the basic interview questions such as where do you see yourself in five years?, Why do you want to work for our company? Etc. A little research on the company you want to work can be beneficial in the interview.