Data science, which is also known as data-driven science is a field of Computer Science which has scientific methods, processes, and systems to extract knowledge from data which can be structured or unstructured. A data scientist works to extract knowledge and insights from large volumes of data in various forms. With large data being generated every minute, extracting value from data is going to become more intricate and demanding as time passes.
As a part of the consumer economy, whenever we connect to a website or any online service, we are mined for data. A data scientist then collects, cleans, analyzes and makes predictions of this data by using a combination of computer science, statistical analysis, and business knowledge.
A data scientist is more mathematically focused, concentrating majorly on providing insights into future patterns identified from the past and the current data. Data is a set of quantitative and qualitative variables and science implies knowledge gained through a systematic study. Hence, a data scientist is the one who systematically studies data and derives useful information from it.
The success of business networking site LinkedIn is a perfect coupling of Business Intelligence with Data Science. LinkedIn, along with Facebook and Google, is utilizing Data Scientists to bring structure to large quantities of data, determining significance in its value and relationship between its variables.
In an age where the daily data generation is estimated to rise to 240 exabytes by 2020, the demand for data science skills will multiply manifolds in the near future. An article by Travis Wright for Venture Beat suggests that the United States alone would require 140,000 – 190,000 data scientists if they want to keep up with the growing data.
But, this said, what exactly is the difference between a data analyst and a data scientist? A data analyst extracts information from large sets of data. A data scientist uses machine learning, deep learning, and statistical approach to extract knowledge and insights from this data. A data analyst typically looks at the past while a data scientist focuses on the present as well as the future. Data Science is a combination of scientific background with computational and analytical skills.
The different tools used by Data Scientists at various levels include:
- Python, R and SQL for data analysis
- MySQL as a data warehouse for structured data and Hive or Redshift for Big Data.
- D3.js and Tableau for Data Visualizations
- Python’s Scikit-learn and SparkMLlib for machine learning.
Data is useless if it is not mined. Mining is optimally collecting, analyzing, organizing the data. A data scientist has an ability to handle data using latest techniques and technologies, can perform necessary analysis and can present acquired knowledge to the associates in an informative way. The different roles of a data scientist include developing and planning analytic projects, develop data models and new analytical tools, work with application developers to extract data, provide and apply quality assurance practices for data mining and analysis, among others.
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