What is Data Science Exactly ? How does it work ?
What Is Data Science?
According to breakthrough research published in 2013, 90 percent of the world's data was produced in the past two years. Allow it to sink in. We've acquired and processed 9x the quantity of data in two years than humanity had in the preceding 92,000 years combined. And it shows no signs of slowing down. It's estimated that we've already generated 2.7 zettabytes of data, with that figure expected to skyrocket to 44 zettabytes by 2020.
What are we going to do with all of this information? How can we make it work for us? What are its practical applications? Data scientific discipline is concerned with these issues.
Every organization will claim to be conducting data science, but what exactly does it actually involve? Because the discipline is expanding so quickly and transforming so many sectors, it's impossible to confine its capabilities with a formal definition, but in general, data science is dedicated to the extraction of clean information from raw data in order to formulate useful insights.
Our digital data, often known as the "oil of the twenty-first century," is the most important in the sector. It offers tremendous advantages in business, science, and our daily lives. Your commute to work, your most recent Google search for the nearest coffee shop, your Instagram post about what you ate, and even your fitness tracker's health data are all useful to various data scientists in different ways. Data science is responsible for giving us new goods, generating breakthrough insights, and making our lives more convenient by sifting through huge lakes of data in search of connections and patterns.
How Does Data Science Work?
Data science entails a wide range of disciplines and specialist areas in order to generate a comprehensive, thorough, and sophisticated examination of raw data. To efficiently filter through confusing volumes of information and communicate just the most critical portions that will help drive innovation and efficiency, data scientists must be adept in everything from data engineering, arithmetic, statistics, sophisticated computers, and graphics.
Artificial intelligence, particularly its subfields of machine learning and deep learning, is also widely used by data scientists to develop models and make predictions using algorithms and other approaches.
In general, data science has a five-stage life cycle that includes:
1. Data collection, data input, signal receiving, and data extraction are all examples of capture.
2. Data warehousing, data cleansing, data staging, data processing, and data architecture must all be maintained.
3. Data mining, clustering/classification, data modeling, and data summarization are all steps in the process.
4. Data reporting, data visualization, business intelligence, and decision-making are all ways to communicate.
5. Analyze: exploratory/confirmatory, predictive, regression, text mining, and qualitative
1: Source UC Berkeley
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