Top 1200 Data Science Quotes & Sayings

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.

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 reason I spend so much of my time doing science is that the whole point of science is to help people resolve conflicting claims by saying: 'Show me the data.'

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.

I think of 'data science' as a flag that was planted at the intersection of several different disciplines that have not always existed in the same place. Statistics, computer science, domain expertise, and what I usually call 'hacking,' though I don't mean the 'evil' kind of hacking.

Data is the new science. Big Data holds the answers. Are you asking the right questions?

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.

I think philosophers can do things akin to theoretical scientists, in that, having read about empirical data, they too can think of what hypotheses and theories might account for that data. So there's a continuity between philosophy and science in that way.

Data-intensive graph problems abound in the Life Science drug discovery and development process.

Chemistry is necessarily an experimental science: its conclusions are drawn from data, and its principles supported by evidence from facts.

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

The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.

Belief Systems contradict both science and ordinary "common sense." B.S. contradicts science, because it claims certitude and science can never achieve certitude: it can only say, "This model"- or theory, or interpretation of the data- "fits more of the facts known at this date than any rival model." We can never know if the model will fit the facts that might come to light in the next millennium or even in the next week.

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 scientists Heartland works with demanded we host a ninth conference this year to foster a much-needed frank, honest, and open discussion of the current state of climate science and we just couldn't refuse. The public, the press, and the scientific community will all benefit from learning about the latest research and observational data that indicate climate science is anything but 'settled.

The big thing that's happened is, in the time since the Affordable Care Act has been going on, our medical science has been advancing. We have now genomic data. We have the power of big data about what your living patterns are, what's happening in your body. Even your smartphone can collect data about your walking or your pulse or other things that could be incredibly meaningful in being able to predict whether you have disease coming in the future and help avert those problems.

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 science requires having that cultural space to experiment and work on things that might fail.

In the next 10 years, data science and software will do more for medicine than all of the biological sciences together.

The term "informatics" was first defined by Saul Gorn of University of Pennsylvania in 1983 (Gorn, 1983) as computer science plus information science used in conjunction with the name of a discipline such as business administration or biology. It denotes an application of computer science and information science to the management and processing of data, information and knowledge in the named discipline.

Data science is the combination of analytics and the development of new algorithms.

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.

When science and the Bible differ, science has obviously misinterpreted its 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.

A lot of people seem to think that data science is just a process of adding up a bunch of data and looking at the results, but that's actually not at all what the process is.

Computer science only indicates the retrospective omnipotence of our technologies. In other words, an infinite capacity to process data (but only data -- i.e. the already given) and in no sense a new vision. With that science, we are entering an era of exhaustivity, which is also an era of exhaustion.

Either data supports the observations or they don't. Voting doesn't work in science.

Scientific knowledge is, by its nature, provisional. This is due to the fact that as time goes on, with the invention of better instruments, more data and better data hone our understanding further. Social, cultural, economic, and political context are relevant to our understanding of how science works.

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 think actively promoting women in science is very important because the data has certainly shown that there has been an underrepresentation.

We are going to completely change what it means to do advanced analytics with our data solutions. We have machine-learning stuff that is about really bringing advanced analytics and statistical machine learning into data-science departments everywhere.

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

Science is a way of getting knowledge. It's a method. It's a method that really relies on making mistakes. We propose ideas, they are usually wrong, and we test them against the data. Scientists do this in a formal way. It's a way that everyone can go through life; that's how we should be teaching science from a very young age.

A lot of environmental and biological science depends on technology to progress. Partly I'm talking about massive server farms that help people crunch genetic data - or atmospheric data. But I also mean the scientific collaborations that the Internet makes possible, where scientists in India and Africa can work with people in Europe and the Americas to come up with solutions to what are, after all, global problems.

The company started in the early 90s or late 80s. We were a behavioural science company. We didn't pivot into data analytics till 2012. So, all the data that we collected pre-2012, which was done by the British company SBL group, was collected through quantitive and qualitative research on the ground.

Philosophers of science have repeatedly demonstrated that more than one theoretical construction can always be placed upon a given collection of data.

Economics is not an exact science. It's a combination of an art and elements of science. And that's almost the first and last lesson to be learned about economics: that in my judgment, we are not converging toward exactitude, but we're improving our data bases and our ways of reasoning about them.

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.

When you analyze all the data, there is a warming trend according to science. But the jury is out on the degree of how much is manmade.

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.

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.

Quality without science and research is absurd. You can't make inferences that something works when you have 60 percent missing data.

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.

Every day we go over data and use science and data to drive policy and decision-making.

When I look at the next set of technologies that we have to build in Salesforce, it's all data-science-based technology. We don't need more cloud. We don't need more mobile. We don't need more social. We need more data science.

Alternative explanations are always welcome in science, if they are better and explain more. Alternative explanations that explain nothing are not welcome... Note how science changed those beliefs when new data became available. Religions stick to the same ancient beliefs regardless of the data.

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.

Just because you're right-wing shouldn't mean you don't believe climate science data. They're unrelated.

The whole concept of data science is that the software becomes the expert, and you, as the average user, are able to understand what's going on.

What distinguishes the language of science from language as we ordinarily understand the word? ... What science strives for is an utmost acuteness and clarity of concepts as regards their mutual relation and their correspondence to sensory data.

You need to use data science and machine learning to get the ground truth of what's happening inside of a company.

The paradigm of physics - with its interplay of data, theory and prediction - is the most powerful in science.

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.

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.

Trust science, believe that innovation and discoveries are good for us, and make decisions based on data and evidence.

If we study learning as a data science, we can reverse engineer the human brain and tailor learning techniques to maximize the chances of student success. This is the biggest revolution that could happen in education, turning it into a data-driven science, and not such a medieval set of rumors professors tend to carry on.

I wrote an editorial piece in 'Science' about the nightly data release and how I thought it was bad for science as a field, I think a few years before Celera was formed.

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.

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.

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.