Data science
Introduction
Some time ago, data science was a buzzword. Today, however, it has become a necessity for every business. Data is the fuel of your business and if you don't have it or know how to process it properly you will be left behind. In this article we'll discuss what data science is and why it matters for businesses today.
Big Data
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Big Data is the term used to describe sets of data so large and complex that they become difficult to process using traditional computer algorithms and software tools.
Big Data is generated by many sources such as social media, mobile devices, sensors and other Internet-connected devices.
Data Processing
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Data processing is the conversion of raw data into a form that can be used by the data analysis tools. It can be done using a number of tools, including programming languages, databases and data mining tools.
Data processing in parallel increases the speed at which you analyze your data set and allows for more accurate results because each step in the process is executed independently from other steps.
Data Analysis
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Data analysis is the process of examining data and drawing conclusions from it. It’s an important part of any data scientist’s job, but also one that can be intimidating if you don’t know what to expect.
In this section we'll go over some basic concepts for understanding how to analyze your own data sets using R or Python scripts (or other languages).
Machine Learning
Machine learning is a subfield of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
In this section, we will discuss some of the fundamental concepts behind machine learning and how they can be applied in various fields such as commerce, finance and healthcare.
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Data Science is a branch of computer science that deals with the analysis, processing and modeling of data. Data Scientists use their skills in mathematics, statistics, physics, biology and other disciplines to aid in solving problems related to business analytics.
The term “data scientist” was coined by Eric Schmidt in a Wired article about his new company Moonshot AI. The term has been adopted by many companies as it relates to creating solutions based on machine learning technology where people can learn from large sets of information gathered from different sources such as social media sites like Facebook or Twitter; web crawlers that extract information such as emails sent between individuals; sensor networks embedded within industrial equipment like cars or tanks (for example) which collect temperature readings every hour; etcetera...
Conclusion
Data science is a very broad and diverse field with many different sub-specialties. A data scientist can come from any background and have a number of different skills, providing them with the opportunity to work on a wide range of projects. With so many fields to choose from, it’s important for students to find out what career path they want before starting their studies in order to maximize their chances of getting into one that matches their interests best (more than likely). By exploring all these options before making any decisions about what type of job they want after graduation, graduates will be able to make sure they get into an industry that suits them most.