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What is data science? Like any emerging field, it hasn't been completely defined yet, but you know enough about it to be interested or else you wouldn't be reading this book.
I think of data science as lying at the intersection of computer science, statistics, and substantive application domains. From computer science comes ma-chine learning and high-performance computing technologies for dealing with scale. From statistics comes a long tradition of exploratory data analysis, significance testing, and visualization. From application domains in business and the sciences come challenges worthy of battle, and evaluation standards to assess when they have been adequately conquered.
But these are all well-established fields.
Why data science, and why now? I see three reasons for this sudden burst of activity:
New technology makes it possible to capture, annotate, and store vast amounts of social media, logging, and sensor data. After you have amassed all this data, you begin to wonder what you can do with it.
Computing advances make it possible to analyze data in novel ways and at ever increasing scales.
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Cloud computing architectures give even the little guy access to vast power when they need it. New approaches to machine learning have led to amazing advances in longstanding problems, like computer vision and natural language processing.
Prominent technology companies (like Google and Facebook) and quantitative hedge funds (like Renaissance Technologies and TwoSigma) have proven the power of modern data analytics. Success stories applying data to such diverse areas as sports management and election forecasting have served as role models to bring data science to a large popular audience.
Computer scientists, by nature, don't respect data.
They have traditionally been taught that the algorithm was the thing, and that data was just meat to be passed through a sausage grinder.
So to qualify as an effective data scientist, you must first learn to think like a real scientist. Real scientists strive to understand the natural world, which is a complicated and messy place. By contrast, computer scientists tend to build their own clean and organized virtual worlds and live comfortably within them. Scientists obsess about discovering things, while computer scientists invent rather than discover.
People's mindsets strongly color how they think and act, causing misunderstandings when we try to communicate outside our tribes. So fundamental are these biases that we are often unaware we have them. Examples of the cultural differences between computer science and real science include:
Data vs. method centrism: Scientists are data driven, while computer scientists are algorithm driven. Real scientists spend enormous amounts of effort collecting data to answer their question of interest. They invent fancy measuring devices, stay up all night tending to experiments, and devote most of their thinking to how to get the data they need.
By contrast, computer scientists obsess about methods: which algorithm is better than which other algorithm, which programming language is best for a job, which program is better than which other program. The details of the data set they are working on seem comparably unexciting.
Concern about results: Real scientists care about answers. They analyze data to discover something about how the world works. Good scientists care about whether the results make sense, because they care about what the answers mean.
By contrast, bad computer scientists worry about producing plausible-looking numbers. As soon as the numbers stop looking grossly wrong, they are presumed to be right. This is because they are personally less invested in what can be learned from a computation, as opposed to getting it done quickly and efficiently.
Computer Science, Data Science And Real Science. (2024, Feb 27). Retrieved from https://studymoose.com/computer-science-data-science-and-real-science-essay
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