5 courses every Big Data geek should take

As we move forward in the informational age, the amount of pervasive data and technology in our lives can be overwhelming. To add to the digital mayhem, data and technology continues to grow at an exponential rate.

Since technological advancement has far surpassed what most of us had imagined, we are still all wondering what to do with this vast pot of knowledge. Progression as we know it will rely on this big data dissemination. To become one of the pioneers of this great field, here are five courses every aspiring big data analytics student should take:

1. Data structures and algorithms

2. Legal, ethical, and management issues in technology

3. Introduction to philosophy

4. Engineering and technology project management

5. Business intelligence (BI)

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Turning Big Data into Useful Information

The agile organization needs knowledge to act on, quickly and effectively. Though many organizations are clamouring for "Big Data", not nearly as many know what to do with it.

Download this eBook to better understand the ways to improve organizational agility with useful Big Data and better support the demands of an ever-changing enterprise.

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Every step you take

Analytics in sport has taken a giant leap, with advanced statistics, sophisticated video analysis and player tracking at the forefront.

On the eve of the 2012 UEFA Champions League final between Chelsea and Bayern Munich, Petr Cech, the Chelsea goalkeeper, was given a two-hour DVD of every penalty kick that Bayern had taken since 2007. It was enough to calculate the probability of which way the ball would be placed when a penalty was taken. Chelsea went on to win the match following a penalty shoot-out and thereafter Cech said, “Six penalties the right way and I saved three, so basically the homework was very good.”

This March, at the MIT Sloan Sports Analytics Conference in Boston, Michael Niemeyer, Bayern’s head of match analysis, said of Pep Guardiola, the famed former manager of Barcelona and the current Bayern boss, “As he came to Bayern, the first thing he said was: ‘The match analysis department is the most important department for me.’ The second thing was: ‘I see a big part of my work in the auditorium.’ The auditorium is the place where he has video sessions.”

Read the article published in The Hindu dt. 5th Jul 2015

An executive’s guide to machine learning

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.

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Going Deep on Big Data

Big data is transitioning from one of the most hyped and anticipated tech trends of recent years into one of the biggest challenges that IT is now trying to wrestle and harness.

ZDNet in this special report (consisting of 10 articles) examines the technologies and best practices for taking advantage of big data and provide a look at organizations that are putting it to good use.

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ZDNet Special Features

The Evolution of Enterprise Software
The 21st Century Data Center
Cloud: How to Do SaaS Right
BYOD and the Consumerization of IT
Tapping M2M: The Internet of Things
Making the Business Case For Big Data
Windows 8 in Business


ZDNet Special features Link

Big data: The next frontier for innovation, competition, and productivity

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.

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Six Types Of Analyses Every Data Scientist Should Know

Jeffrey Leek, Assistant Professor of Biostatistics at John Hopkins Bloomberg School of Public Health, has identified six(6) archetypical analyses. As presented, they range from the least to most complex, in terms of knowledge, costs, and time. In summary,

Descriptive
Exploratory
Inferential
Predictive
Causal
Mechanistic

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eBook: Making the business case for big data

Big data offers IT one of its best opportunities to bring value to the business. This ebook is aimed at helping your organization make good decisions about big data.

This guide offers a roundup of the latest information, predictions, advice, and answers on some of the toughest issues surrounding Big Data, such as:

-- Choosing a Big Data vendor
-- Developing Big Data expertise within your organization
-- Roadblocks to implementing Big Data analytics
-- What the future holds for Big Data
-- How to develop an ROI for Big Data

It also includes original research that provides insights into how your peers view the challenges and opportunities of Big Data, along with their companies’ current strategies, plans, and expectations.

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10 roadblocks to implementing Big Data analytics

Big Data and business analytics are two of the most exciting areas in business and IT these days — but for most enterprises, they are still developmental. Although the opportunities are boundless, the road to an effective Big Data operation is fraught with challenges. Here are some of the obstacles companies are encountering — and some ways to get around them.

1: Budget
2: IT know-how
3: Business know-how
4: Data cleanup
5: The storage bulge
6: New data center workloads
7: Data retention
8: Vendor role clarification
9: Business and IT alignment
10: Developing new talent

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Gartner: How Big Trends in Security, Mobile, Big data and Cloud will Change IT

When you go to a Gartner conference one of he main things you'll notice is the sheer volume of data they can generate on just about any IT topic. The recent Gartner Symposium/ITxpo in Orlando, Fla., was no different. The conference, attended by some 9,000 executives focused on the changes security challenges, mobile computing, big data and cloud will be bringing to IT in the near future.

Trying to get through it all can be daunting so we've tried to simplify that process by distilling a variety of Gartner ITxpo presentations and coming up with the most salient information.

So here goes. From the Gartner analysts, presentations on:

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Checklist for Leveraging Big Data Decisions

The Big Data evolution is changing the way we conduct business on many fronts. For decades, companies pursued real-time customer information to better capitalize on providing what the customer needed; CIOs looked to lower costs for faster analysis; companies sought to un-cuff their hands from IT costs and restrictions; and end users yearned for custom solutions and answers in real time. All wanted a voice in making their companies respond faster, serve better, and grow profits. Utopia is reality with the rise of Big Data!

There are many questions to consider: Are we ready to handle Big Data? Can companies ingest data and present options? Will CIOs leapfrog functionality and integrate the right options? How will organizations manage tiered systems for analytics? Will customers embrace the power of Big Data to extract best options? Let’s examine the impact on IT professionals.

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Forty (information management technology) Vendors We’re Watching: 2012

For a third year we (http://www.information-management.com) bring you our list of up and coming vendors on our radar that are doing their part to shape the groundswell in information management technology in the 21st Century.

View the complete list

Ten Common Hadoopable Problems - Real-World Hadoop Use Cases

Apache Hadoop, the popular data storage and analysis platform, has generated a great deal of interest recently. Large and successful companies are using it to do powerful analyses of the data they collect. Hadoop offers two important services: It can store any kind of data from any source, inexpensively and at very large scale, and it can do very sophisticated analysis of that data easily and quickly. Hadoop is different from older database and data warehousing systems, and those differences can be confusing to users. What data belongs in a Hadoop cluster? What kinds of questions can the system answer? Understanding how to take advantage of Hadoop requires a deeper knowledge of how others have applied it to real-world problems that they face. This paper presents ten real-world Hadoop use cases.

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Big Data: The Management Revolution

“You can’t manage what you don’t measure.” There’s much wisdom in that saying, which has been attributed to both W. Edwards Deming and Peter Drucker, and it explains why the recent explosion of digital data is so important. Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.

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Big Data for the Masses

Big data represents the significant shift in the way that companies receive and used data to fuel business. As the volume, velocity and variety of data increases, we must ready ourselves for managing it with big data strategies.

Download this white paper to read more about the current state of big data and what we can anticipate as it begins to gain mass appeal.

Are you ready for the era of ‘big data’?

Radical customization, constant experimentation, and novel business models will be new hallmarks of competition as companies capture and analyze huge volumes of data. Here’s what you should know.

Over time, we believe big data may well become a new type of corporate asset that will cut across business units and function much as a powerful brand does, representing a key basis for competition. If that’s right, companies need to start thinking in earnest about whether they are organized to exploit big data’s potential and to manage the threats it can pose. Success will demand not only new skills but also new perspectives on how the era of big data could evolve—the widening circle of management practices it may affect and the foundation it represents for new, potentially disruptive business models.

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Clouds, big data, and smart assets: Ten tech-enabled business trends to watch

Advancing technologies and their swift adoption are upending traditional business models. Senior executives need to think strategically about how to prepare their organizations for the challenging new environment.

The trends to watch include:

Trend 1: Distributed cocreation moves into the mainstream
Trend 2: Making the network the organization
Trend 3: Collaboration at scale
Trend 4: The growing ‘Internet of Things’
Trend 5: Experimentation and big data
Trend 6: Wiring for a sustainable world
Trend 7: Imagining anything as a service
Trend 8: The age of the multisided business model
Trend 9: Innovating from the bottom of the pyramid
Trend 10: Producing public good on the grid

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