Top 1200 Data Quotes & Sayings - Page 2

The key to a solid foundation in data structures and algorithms is not an exhaustive survey of every conceivable data structure and its subforms, with memorization of each's Big-O value and amortized cost.

To make a vehicle autonomous, you need to gather massive streams of data from loads of sensors and cameras and process that data on the fly so that the car can 'see' what's around it.

The banking industry has traditionally been characterized by physical branches, privileged access to financial data, and distinct expertise in analyzing such data.

We should always be suspicious when machine-learning systems are described as free from bias if it's been trained on human-generated data. Our biases are built into that training data.

There is so much information that our ability to focus on any piece of it is interrupted by other information, so that we bathe in information but hardly absorb or analyse it. Data are interrupted by other data before we've thought about the first round, and contemplating three streams of data at once may be a way to think about none of them.

Government and businesses cannot function without enormous amounts of data, and many people have to have access to that data.

There are corporate private investigators, companies doing very forensic background checks on people. They buy data, they get their own data... They don't want their industry publicised.

The Europeans have lots of data on the use of adjuvanted flu vaccine in the elderly, but I don't think anybody has really good data on adjuvants in children.

Rule 1. Original data should be presented in a way that will preserve the evidence in the original data for all the predictions assumed to be useful.

It is not a medicine. You don't know what's in it. If there were compelling scientific and medical data supporting marijuana's medical benefits that would be one thing. But the data is not there.

To be honest, more than what I prepare, it's the directors who do the bulk of the work, researching, collecting data and all that. I like to see myself as a processor: they feed me with the data, I give the output.

I believe that it's fine if the university wants to regulate, for example, bandwidth access, but they should treat the students data as private data.

Search engines generally treat personal names as search terms like any others: Data is data.

The data are what matter in economics, and the more ruthlessness that an economist can summon to make sense of the data, the more useful his findings will be.

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.

As we become so visible in the digital world and leave an endless trail of data behind us, exactly who has our data and what they do with it becomes increasingly important.

Data are just summaries of thousands of stories - tell a few of those stories to help make the data meaningful.

They made it very clear that I shouldn't try to be Data because there is only one Data, you can't ever recreate the magic that Brent Spiner made.

There's a project that I started at HHS called the Health Data Initiative. The whole idea was to take a page from what the government had done to make weather data and GPS available back in the day.

More data beats clever algorithms, but better data beats more data.

The first wave of the Internet was really about data transport. And we didn't worry much about how much power we were consuming, how much cooling requirements were needed in the data centers, how big the data center is in terms of real estate. Those were almost afterthoughts.

I was always a data guy, not a theorist. Theorists can maintain total purity. The data are always messy.

In the blockchain world, each user can and should own their data, and 'central' players are less vulnerable to data losses and breaches.

Despite the value of open data, most labs make no systematic effort to share data with other scientists.

Outside the U.S., most data plans have a data limit.

Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.

... negative feelings are not true feelings at all; rather, they are your thoughts about something, based always on the previous experience of yourself and others. You will not find Truth in your past data, only past data that is based on other past data that is based on other past data, and so forth. Forget your "past experience" and look directly at the experience you are having. Right Here, Right Now. There is your Truth.

While many big-data providers do their best to de-identify individuals from human-subject data sets, the risk of re-identification is very real.

It amazes me how people are often more willing to act based on little or no data than to use data that is a challenge to assemble.

Evolving technologies that allow economists to gather new types of data and to manipulate millions of data points are just one factor among several that are likely to transform the field in coming years.

Data, data everywhere, but not a thought to think.

Data is the fabric of the modern world: just like we walk down pavements, so we trace routes through data, and build knowledge and products out of it.

Data scientists are statisticians because being a statistician is awesome and anyone who does cool things with data is a statistician.

The computer is here to stay, therefore it must be kept in its proper place as a tool and a slave, or we will become sorcerer's apprentices, with data data everywhere and not a thought to think.

There are a number of fascinating stories included in 'The Human Face of Big Data' that represent some of the most innovative applications of data that are shaping our future.

The weaker the data available upon which to base one's conclusion, the greater the precision which should be quoted in order to give the data authenticity.

Uncontrolled access to data, with no audit trail of activity and no oversight would be going too far. This applies to both commercial and government use of data about people.

We face two overlapping challenges. The first concerns real-time court-ordered interception of what we call 'data in motion,' such as phone calls, e-mail, and live chat sessions. The second challenge concerns court-ordered access to data stored on our devices, such as e-mail, text messages, photos, and videos - or what we call 'data at rest.'

If you consider any set of data without a preconceived viewpoint, then a viewpoint will emerge from the data.

I think there's data, and then there's information that comes from data, and then there's knowledge that comes from information. And then, after knowledge, there is wisdom. I am interested in how to get from data to wisdom.

Scientists do not collect data randomly and utterly comprehensively. The data they collect are only those that they consider *relevant* to some hypothesis or theory.

Connectivity offers a great opportunity for General Motors. When you look at the investment we have made in OnStar and putting 4GLT in and the access you have to not only put data in, and we haven't really tapped into the data you can use from the vehicle.

Data, I think, is one of the most powerful mechanisms for telling stories. I take a huge pile of data and I try to get it to tell stories.

EMA research evidences strong and growing interest in leveraging log data across multiple infrastructure planning and operations management use cases. But to fully realize the potential complementary value of unstructured log data, it must be aligned and integrated with structured management data, and manual analysis must be replaced with automated approaches. By combining the RapidEngines capabilities with its existing solution, SevOne will be the first to truly integrate log data into an enterprise-class, carrier-grade performance management system.

If you have a lot of data and you want to create value from that data, one of the things you might consider is building up an AI team.

If we have data, let's look at data. If all we have are opinions, let's go with mine.

Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations.

Emotion AI uses massive amounts of data. In fact, Affectiva has built the world's largest emotion data repository.

People treat citizens like they're some kind of unreliable source, but citizens are data. They are a data set.

The Europeans have lots of data on the use of adjuvanted flu vaccine in the elderly, but I dont think anybody has really good data on adjuvants in children.

Data will always bear the marks of its history. That is human history held in those data sets.

Let's look at lending, where they're using big data for the credit side. And it's just credit data enhanced, by the way, which we do, too. It's nothing mystical. But they're very good at reducing the pain points. They can underwrite it quicker using - I'm just going to call it big data, for lack of a better term: "Why does it take two weeks? Why can't you do it in 15 minutes?"

Here is a guiding principle: If a business collects data on consumers electronically, it should provide them with a version of that data that is easy to download and export to another Web site.

My study is NOT as a climatologist, but from a completely different perspective in which I am an expert … For decades, as a professional experimental test engineer, I have analyzed experimental data and watched others massage and present data. I became a cynic; My conclusion - 'if someone is aggressively selling a technical product who's merits are dependent on complex experimental data, he is likely lying'. That is true whether the product is an airplane or a Carbon Credit.

People should have to opt in for any kind of data sharing, and they should know what the data is being used for.

Yelp is in a very nice spot: local data, and especially review data, is one of the killer apps on mobile phones.

There is not substantial data that AZT stops the transmission of HIV from mother to child. There is too much conflicting data to make concrete policy.

Disruptive technology is a theory. It says this will happen and this is why; it's a statement of cause and effect. In our teaching we have so exalted the virtues of data-driven decision making that in many ways we condemn managers only to be able to take action after the data is clear and the game is over. In many ways a good theory is more accurate than data. It allows you to see into the future more clearly.

Data isn't information. ... Information, unlike data, is useful. While there's a gulf between data and information, there's a wide ocean between information and knowledge. What turns the gears in our brains isn't information, but ideas, inventions, and inspiration. Knowledge-not information-implies understanding. And beyond knowledge lies what we should be seeking: wisdom.

I like to say I've been working on big data for so long, it used to be small data when I started working on it.