What is data science?
A groundbreaking 2013 study found that 90% of all global data was generated in the last two years. Let that sink in. In just two years we have collected and processed 9 times more information than in humanity's 92,000 years combined. And it doesn't slow down. We are forecast to have already created 2.7 zettabytes of data, and by 2020 that number will grow to a staggering 44 zettabytes.
What do we do with all this data? How do we make it useful to us? What are the real applications? These questions are the domain of data science.
Any company will say it is doing some kind of data science, but what exactly does that mean? The field is growing so fast and revolutionizing so many industries that it is difficult to delineate its capabilities with a formal definition, but in general, data science is dedicated to extracting clean insights from raw data to form actionable insights.
Commonly known as “the oil of the 21st century”, our digital data is of the utmost importance in this area. They have invaluable advantages in business, research, and our everyday lives. Your commute to work, your last Google search for the nearby cafe, your Instagram post about what you ate, and even your fitness tracker health data are important to different data scientists in different ways. Data science examines vast oceans of data, looks for connections and patterns and is responsible for bringing us new products, providing innovative information, and making our lives more convenient.
How does data science work?
Data Science spans a variety of disciplines and subject areas to provide a refined, comprehensive, and holistic view of raw data. Data scientists must be trained in all areas of data engineering, mathematics, statistics, advanced computing, and visualization in order to effectively examine disorderly amounts of information and communicate only the most important bits and pieces that help drive innovation and efficiency.
Data scientists also rely heavily on artificial intelligence, particularly its deep learning and machine learning subdomains, to build models and make predictions using algorithms and other techniques.
Data Science generally has a five-stage life cycle consisting of :
Capture: data acquisition, data entry, signal reception, data extraction
Maintenance: data storage, data cleaning, data storage, data processing, data architecture
Process: data mining, grouping, data modelling, data summarization
Communication: data reporting, data visualization, business intelligence, decision making
Analysis: exploration/confirmation, analysis predictive, regression, text mining, qualitative analysis.
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