Top 1200 Data Structures Quotes & Sayings

Bad programmers worry about the code. Good programmers worry about data structures and their relationships.

People think 'big data' avoids the problem of discrimination because you are dealing with big data sets, but, in fact, big data is being used for more and more precise forms of discrimination - a form of data redlining.

Learn when and how to use different data structures and their algorithms in your own code. This is harder as a student, as the problem assignments you'll work through just won't impart this knowledge. That's fine.

TIA was being used by real users, working on real data - foreign data. Data where privacy is not an issue.

Biases and blind spots exist in big data as much as they do in individual perceptions and experiences. Yet there is a problematic belief that bigger data is always better data and that correlation is as good as causation.

I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships.

With too little data, you won't be able to make any conclusions that you trust. With loads of data you will find relationships that aren't real... Big data isn't about bits, it's about talent.

The programmer's primary weapon in the never-ending battle against slow system is to change the intramodular structure. Our first response should be to reorganize the modules' data structures.

The story of Taliban recovery and resurgence begins in the places where they took refuge after 2001. And as long as those leadership structures and training structures operate outside of Afghanistan with relative impunity, the conflict will continue.

The USA Freedom Act does not propose that we abandon any and all efforts to analyze telephone data, what we're talking about here is a program that currently contemplates the collection of all data just as a routine matter and the aggregation of all that data in one database. That causes concerns for a lot of people... There's a lot of potential for abuse.

Big data is great when you want to verify and quantify small data - as big data is all about seeking a correlation - small data about seeking the causation.

Everywhere in the world, when you travel, you see structures that stand out. Historic structures and new structures. But in India, I don't see anything that stands out.

As words are not the things we speak about, and structure is the only link between them, structure becomes the only content of knowledge. If we gamble on verbal structures that have no observable empirical structures, such gambling can never give us any structural information about the world. Therefore such verbal structures are structurally obsolete, and if we believe in them, they induce delusions or other semantic disturbances.

With the old economics destroyed, organizational forms perfected for industrial production have to be replaced with structures optimized for digital data. It makes increasingly less sense even to talk about a publishing industry, because the core problem publishing solves — the incredible difficulty, complexity, and expense of making something available to the public — has stopped being a problem.

If we understood, as we do not, the physical bases for intellectual structures, I have little doubt that we would find structures in the brain for social interactions, or language, or analysis of personality - a whole variety of systems developed on the basis of a specific biological endowment.

Morphological information has provided the greatest single source of data in the formulation and development of the theory of evolution and that even now, when the preponderance of work is experimental, the basis for interpretation in many areas of study remains the form and relationships of structures.

Facebook collects a lot of data from people and admits it. And it also collects data which isn't admitted. And Google does too. As for Microsoft, I don't know. But I do know that Windows has features that send data about the user.

MapReduce has become the assembly language for big data processing, and SnapReduce employs sophisticated techniques to compile SnapLogic data integration pipelines into this new big data target language. Applying everything we know about the two worlds of integration and Hadoop, we built our technology to directly fit MapReduce, making the process of connectivity and large scale data integration seamless and simple.

One of the very basic ideas of Post-Modernism is rejection of arbitrary power structures. Different people are sensitive to different kinds of power structures.

One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn't always good enough, and it remains difficult to integrate multiple data sources.

The magic of life, of course, is not something that can be explained. Structures can only take us to the point where they begin or end. Beyond structures is the white light.

I will talk about two sets of things. One is how productivity and collaboration are reinventing the nature of work, and how this will be very important for the global economy. And two, data. In other words, the profound impact of digital technology that stems from data and the data feedback loop.

Generally, the craft of programming is the factoring of a set of requirements into a a set of functions and data structures.

Human physical structures and intellectual structures are generally studied in different ways. The assumption is that physical structures are genetically inherited and intellectual structures are learned. I think that this assumption is wrong. None of these structures is learned. They all grow; they grow in comparable ways; their ultimate forms are heavily dependent on genetic predispositions.

Realize you won't master data structures until you are working on a real-world problem and discover that a hash is the solution to your performance woes.

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.

I'm going to say something rather controversial. Big data, as people understand it today, is just a bigger version of small data. Fundamentally, what we're doing with data has not changed; there's just more of it.

Data!data!data!" he cried impatiently. "I can't make bricks without clay.

I think the structures of exclusion are more systematically built up in American society, for example, so that young girls interested in science eventually lose their confidence over time. The structures of exclusion work against them. We have other structures of exclusion in India, but not around modern scientific knowledge.

Basically political economy - that you have to look at how funding structures shape the media landscape. You have to look at commercial interests, consolidation - the economy structures are experience.

Go out and collect data and, instead of having the answer, just look at the data and see if the data tells you anything. When we're allowed to do this with companies, it's almost magical.

Wholeness is sort of a dubious concept. Because in terms of the human body and literal wholeness and structures, you think: "here are the structures that help make me whole." Family, or school, or the city I live in. When those structures are dysfunctional or decaying, you end up kind of Frankensteining pieces from everywhere in order to make yourself sated and comfortable and alive.

When dealing with data, scientists have often struggled to account for the risks and harms using it might inflict. One primary concern has been privacy - the disclosure of sensitive data about individuals, either directly to the public or indirectly from anonymised data sets through computational processes of re-identification.

Scientific data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to provide a basis for action or a recommendation for action. The step intermediate between the collection of data and the action is prediction.

In my view, our approach to global warming exemplifies everything that is wrong with our approach to the environment. We are basing our decisions on speculation, not evidence. Proponents are pressing their views with more PR than scientific data. Indeed, we have allowed the whole issue to be politicized-red vs blue, Republican vs Democrat. This is in my view absurd. Data aren't political. Data are data. Politics leads you in the direction of a belief. Data, if you follow them, lead you to truth.

We are ... led to a somewhat vague distinction between what we may call "hard" data and "soft" data. This distinction is a matter of degree, and must not be pressed; but if not taken too seriously it may help to make the situation clear. I mean by "hard" data those which resist the solvent influence of critical reflection, and by "soft" data those which, under the operation of this process, become to our minds more or less doubtful.

I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.

You can be bound by physical things, as I am by certain sicknesses, but nevertheless you can still be free to recognize that all initiatives really come from yourself if you don't depend upon structures of government or structures of any kind.

We get more data about people than any other data company gets about people, about anything - and it's not even close. We're looking at what you know, what you don't know, how you learn best. The big difference between us and other big data companies is that we're not ever marketing your data to a third party for any reason.

I'm kind of fascinated by this idea that we can surround ourselves with information: we can just pile up data after data after data and arm ourselves with facts and yet still not be able to answer the questions that we have.

There's a lot of power in executing data - generating data and executing data.

You have to imagine a world in which there's this abundance of data, with all of these connected devices generating tons and tons of data. And you're able to reason over the data with new computer science and make your product and service better. What does your business look like then? That's the question every CEO should be asking.

I don't believe in data-driven anything, it's the most stupid phrase. Data should always serve people, people should never serve data.

In C there are no data structures: there are pointers and pointer arithmetic. So you have a pointer into a data structure.

It is better to have 100 functions operate on one data structure than to have 10 functions operate on 10 data structures.

Smart data structures and dumb code works a lot better than the other way around.

I had some of the students in my finance class actually do some empirical work on capital structures, to see if we could find any obvious patterns in the data, but we couldn't see any.

We use nearly 5 thousand different data points about you to craft and target a message. The data points are not just a representative model of you. The data points are about you, specifically.

We bless the organized church structures and their meetings. But if there are 10,000 others that meet outside of these ecclesiastical structures, that's wonderful, too. The kingdom of God moves forward in lots and lots of ways.

A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.

The data set of proxies of past climate used in Mann...for the estimation of temperature from 1400 to 1980 contains collation errors, unjustifiable truncation or extrapolation of source data, obsolete data, geographical location errors, incorrect calculation of principal components and other quality control defects.

Tape with LTFS has several advantages over the other external storage devices it would typically be compared to. First, tape has been designed from Day 1 to be an offline device and to sit on a shelf. An LTFS-formatted LTO-6 tape can store 2.5 TB of uncompressed data and almost 6 TB with compression. That means many data centers could fit their entire data set into a small FedEx box. With LTFS the sending and receiving data centers no longer need to be running the same application to access the data on the tape.

People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.

Fully "biodegradable" structures are nowadays the ideal and the standards to which most, if not all structures, struggle to measure up.

We all say data is the next white oil. [Owning the oil field is not as important as owning the refinery because what will make the big money is in refining the oil. Same goes with data, and making sure you extract the real value out of the data.]

Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.

The biggest mistake is an over-reliance on data. Managers will say if there are no data they can take no action. However, data only exist about the past. By the time data become conclusive, it is too late to take actions based on those conclusions.

I should not like to leave an impression that all structural problems can be settled by X-ray analysis or that all crystal structures are easy to solve. I seem to have spent much more of my life not solving structures than solving them.

Any time scientists disagree, it's because we have insufficient data. Then we can agree on what kind of data to get; we get the data; and the data solves the problem. Either I'm right, or you're right, or we're both wrong. And we move on. That kind of conflict resolution does not exist in politics or religion.

We're in this period where we're getting good data rates. I would say we're getting data rates that are like the data rates we got when we launched RealAudio in 1995.