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Finding Data on the Internet

Finding Data on the Internet
Skip to Content A Community Site for R – Sponsored by Revolution Analytics Home » How to » Finding Data on the Internet Finding Data on the Internet By RevoJoe on October 6, 2011 The following list of data sources has been modified as of 3/18/14. If an (R) appears after source this means that the data are already in R format or there exist R commands for directly importing the data from R. Economics American Economic Ass. Data Science Practice This section contains data sets used in the book "Doing Data Science" by Rachel Schutt and Cathy O'Neil (O'Reilly 2014) Datasets on the book site: Enron Email Dataset: GetGlue (time stamped events: users rating TV shows): Titanic Survival Data Set: Half a million Hubway rides: Finance Government Health Care Gapminder: Machine Learning Networks Science Comments Related:  Big Data / Analytics

Where can I find large datasets open to the public? Publicly Available Big Data Sets :: Hadoop Illuminated Public Data sets on Amazon AWS Amazon provides following data sets : ENSEMBL Annotated Gnome data, US Census data, UniGene, Freebase dump Data transfer is 'free' within Amazon eco system (within the same zone) AWS data sets InfoChimps InfoChimps has data marketplace with a wide variety of data sets. InfoChimps market place Comprehensive Knowledge Archive Network open source data portal platform data sets available on from Stanford network data collection Open Flights Crowd sourced flight data Flight arrival data

IT Operations Analytics In the fields of information technology and systems management, IT Operations Analytics (ITOA) is an approach or method applied to application software designed to retrieve, analyze and report data for IT operations. ITOA has been described as applying big data analytics to large datasets where IT operations can extract unique business insights.[1][2] In its Hype Cycle Report, Gartner rated the business impact of ITOA as being ‘high’, meaning that its use will see businesses enjoy significantly increased revenue or cost saving opportunities.[3] By 2017, Gartner predicts that 15% of enterprises will use IT operations analytics technologies to deliver intelligence for both business execution and IT operations.[2] Definition[edit] History[edit] Due the mainstream embrace of cloud computing and the increasing desire for businesses to adopt more Big Data practices, the ITOA industry has grown significantly since 2010. Applications[edit] Types[edit] Tools and ITOA Platforms[edit] See also[edit]

Data Visualisation: What's the big deal? | Career and Hiring Insights | Aquent The concept of using pictures to understand complex information — especially data — has been around for a very long time, centuries in fact. One of the most cited examples of statistical graphics is Napoleon’s invasion of Russia mapped by Charles Minard. The maps showed the size of the army and the path of Napoleon’s retreat from Moscow. It also included detailed information like temperature and time scales, providing the audience with an in-depth understanding of the event. However, as with most things, it’s technology that has truly allowed data visualisation to take the stage and get noticed. It’s no surprise that with big data there’s potential for BIG opportunity (someone pass me the shot glass), but many corporates are genuinely challenged when it comes to: understanding the data they have finding value in it getting the wider business to buy in and just GET IT!!! So how do you tackle this? How do you get people to comprehend this information quickly? One word — INSIGHT.

50 external machine learning / data science resources and articles Data Science Central 50 external machine learning / data science resources and articles by Vincent Granville Sep 24, 2015 Starred articles are candidates for the picture of the week. Resources Source: article #3, below Articles Check out our previous selection of articles. DSC Resources Additional Reading

Analytics: Turning a Flood of Data into Valuable Information The benefits that come from data analytics are many — it's helped reduce inmate populations, improve reliability of emergency medical services and reduce traffic fatalities, to name just a few. Though some government agencies are slow to embrace it due to limited capital or sheer intimidation in the face of disparate systems and fragmented technologies, others have taken hold of the proverbial horns and started the process of improving their daily operations by way of the data. And during the California Technology Forum held Aug. 11 in Sacramento, state and local officials delved into the insights gained from the exponential increase of data — and where teams need to focus their energy to turn this flood of data into valuable information. “From that, I understood that big data wasn’t just the amount of data we were talking about," he said. "There are many other things we need to consider.” Graham called the process employed in Los Angeles the collaborative leadership model.

Figuring Out How IT, Analytics, and Operations Should Work Together A new set of relationships is being formed within companies around how people working in data, analytics, IT, and operations teams work together. Is there a “right” way to structure these relationships? Data and analytics represent a blurring of the traditional lines of demarcation between the scope of IT and the responsibilities of operating divisions. Consider the core mission of the modern IT department: Taking in all the technology “mess” (often from several different divisions), developing the necessary competencies, and delivering savings and efficiency to the company. Enter data and analytics, which provide an opportunity for such innovation. Let’s look at four examples of how different corporations responded when faced with this question. The integrated operational data and analytics function. The stand-alone data and analytics service function. Unlike the first example, the data and analytics function here relies on corporate IT and follows established IT processes.

Big Data: Top 100 Influencers and Brands The Big Data technology and services market is one of the fastest growing, multi-billion dollar industries in the world. This market is expected to grow at a 26.4% compound annual growth rate to $41.5 billion through to 2018. Big Data has already become an essential part of our everyday lives. The collection, storage and analysis of enormous amounts of data allows us to track all of our online activity, look up and store our bank statements, shop efficiently, or engage in social media. Big data is also being used by companies to improve customer service, monitor the condition of individuals cars, or contribute to economic development. It has significantly enhanced our day to day lives and this trend will only continue as the capabilities of big data grows in the coming years. Companies see big data as a new method of gaining an edge in the market. We reached out to some of the top 20 influencers to ask them for their views on Big Data. Ronald van Loon - Director at Adversitement Bob E.

Machine Learning Explained: Algorithms Are Your Friend We hear the term “machine learning” a lot these days, usually in the context of predictive analysis and artificial intelligence. Machine learning is, more or less, a way for computers to learn things without being specifically programmed. But how does that actually happen? The answer is, in one word, algorithms. We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. What does machine learning look like? In machine learning, our goal is either prediction or clustering. Regression problems, where the variable to predict is numerical (e.g., the price of a house)Classification problems, where the variable to predict is a "Yes/No" answer (for example, predict whether a certain piece of equipment will experience a mechanical failure) With that in mind, today we’re not going to reveal a secret to do prediction better than the computers, or even how to be a data scientist! Linear Regression Logistic Regression Tree-Based Model Approach

What Led to the Recent Huge Buzz Around Analytics? Price discrimination and downward demand spiral are widely used analytical concepts and practices in the Airlines and Hospitality industries respectively, long before the term Big Data Analytics was even coined. Incidentally, these concepts have been taught in global elite b-schools for decades. So why are analytics, which has been there in practice for decades, experiencing a meteoric rise suddenly? To answer this question, we need to get the Big Picture. Below are key factors that led to the huge buzz around analytics today. The Proliferation of Data Sources – Every day we create 2.5 quintillion bytes of data. Open source distributed platforms coupled with massively discounted storage & compute costs (cloud) have given Start-ups equal footing with Enterprises in developing and launching innovative products leveraging Big Data and Analytics (which was possible only by firms with deep pockets until few years ago).