Data Science is a buzzword in today’s world. Data engineers, data scientists, and data programmers often talk about data science. To put it in simple words, Data Science is an interdisciplinary field where we explore, research, and extract some knowledge out of the structured and unstructured data.

The process of exploration, research, and extraction involves a significant scientific method or principle, relative algorithms, and various statistical mathematics to perform on vast amounts of data to get meaningful insights from it. This data that is extracted and further used by companies or organizations to draw insights for their business goals or solutions.

Every organization today uses data science directly or indirectly, be it giant conglomerates across industries ranging from aerospace to banking and even government bodies.

Applications of Data Science

**Data Science Components  **

• Statistics: Statistics is a field of Mathematics, which helps in quantifying a large amount of numerical data and helps in analyzing meaningful outcomes.
• Visualization: Visualization is the graphical representation of data in a graphical format like Line chart, Pie chart, and many more so that it’s easy to understand the trends and patterns which are also used for the purpose of building predictive models.
• Algorithms: There are many algorithms which support various business problems like predictions, classifications, segmentations, recommendations, object detection and image classifications.
• Data engineering: Data engineering is a separate field, but the work of Data Engineers helps Data Scientists get structure and filtered data. Extraction, Load and Transformation (ETL) or Extractions, Transformation & Load (ETL) forms a key activity under data engineering.

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