Data Science

Introduction to Data Science

Data Science is the study of data. It is about extracting, analyzing, visualizing, managing and storing data to create insights. These insights help the companies to make powerful data-driven decisions. Data Science requires the usage of both unstructured and structured data. It is a multidisciplinary field that has its roots in statistics, math and computer science. It is one of the most highly sought after jobs due to the abundance of data science position and a lucrative pay-scale. So, this was brief to data science, now let’s explore the pros and cons of data science.

Why Choose Data Science for Your Career?

Data Science has become a revolutionary technology that everyone seems to talk about. Hailed as the ‘sexiest job of the 21st century’, Data Science is a buzzword with very few people knowing about the technology in its true sense. While many people wish to become Data Scientists, it is essential to weigh the pros and cons of data science and give out a real picture. In this article, we will discuss these points in detail and provide you with the necessary insights about Data Science.

What is data science?

Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.

Data preparation can involve cleansing, aggregating, and manipulating it to be ready for specific types of processing. Analysis requires the development and use of algorithms, analytics and AI models. It’s driven by software that combs through data to find patterns within to transform these patterns into predictions that support business decision-making. The accuracy of these predictions must be validated through scientifically designed tests and experiments. And the results should be shared through the skillful use of data visualization tools that make it possible for anyone to see the patterns and understand trends.

As a result, data scientists (as data science practitioners are called) require computer science and pure science skills beyond those of a typical data analyst. A data scientist must be able to do the following:

• Apply mathematics, statistics, and the scientific method

• Use a wide range of tools and techniques for evaluating and preparing data—everything from SQL to data mining to data integration methods

• Extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models

• Write applications that automate data processing and calculations

• Tell—and illustrate—stories that clearly convey the meaning of results to decision-makers and stakeholders at every level of technical knowledge and understanding

• Explain how these results can be used to solve business problems.

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