What is Actually Data Science?

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What is Actually Data Science

Data science can be referred to as the study of information, its source, what it can represent and how can it be used as a vital resource in the business world and strategies in the corporate world. It involves the mining of the huge amount of data, structured as well as unstructured, to find a pattern that can be helpful for an organization and increase the efficiencies to give a competitive advantage and give remarkable opportunities in the market.

What are the skills requirements to be a data scientist?

The skill set required for Data Science can vary with requirements of different organizations. Let us have a look at the fundamental needs.

  1. To work with all kinds of algorithms, knowledge of linear algebra is required.
  2. The need of mathematics does not end at linear algebra only. Calculus of multiple variables is also necessary for the development of algorithms.
  3. Along with the calculus and linear algebra, understanding of probability and statistics is utterly essential for the predictive analysis as well as modification of the used algorithms.
  4. As a data scientist, the scripting and developing of the data make it vital to have good knowledge in the coding language. The preferable language can be Python or R.
  5. As for the usage of tools that are mostly used which are Excel and SQL, the knowledge of the same makes it essential as well.
  6. Last but not least, the knowledge of machine learning is required for the processing of data in various forms.

Who are Data Scientists?

Data Scientists are the ones who work on making the data useful in numerous ways with the help of statistics and coding.

What are the kinds of Data Scientists?

Let us now have a look at the types of data scientists. Basically, there are two types of data scientist.

The Type A:

This type is called A for the analysis it involves. This type is mostly concerned with making the data more sensible in a statistical manner. These kinds of data scientists are like the statistician and have the practical knowledge of data that does not fall under curriculum provided by the statistics. These scientists need not be experts in coding, but can code well enough to work with algorithms. They may be experts in designing, modelling, forecasting and other statistical domains.

The type A scientist may be also called as a Decision Support Engineer, Quantitative Analyst, Statistician and a few more.

The Type B:

This type B is known for building. These type B scientists share some ground with the type A scientist, but have expertise in coding knowledge and could be professional software engineers. They usually engage themselves in building models which interact directly with the users.

The Type B data scientists like to call themselves Software Engineers. Although they are also called just Data Scientists.

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