Data Science: A Field To Choose Or Lose?

27Jul - by aiuniverse - 0 - In Uncategorized


With the rapid growth of this world, there are so many fields to choose from. It’s not just engineering or medicine, there are many more options. With so many options given, there is one particular field which is quite interesting – data science. The term sounds familiar, but many people are confused about what exactly data science is. It is the most trending field in the tech industry and is highly demanded.

To begin with, data science is a mixture of many fields and concerns with data and is a huge part of artificial intelligence. It is a mixture of software, statistics and business analysis. It mostly comes under technology, but many other skills are needed to choose it as a career. The main job of a data scientist is to load the data, structure it, and find possible solutions.

The work of a data scientist is to first collect data, which is mostly machine fed data from various sources. He then sorts them and processes them, or rather segregates the data into categories. Generally, the data collected from various sources is common for many things and needs to be segregated and cleaned. The process is similar to cleaning your room. You remove the excess and useless garbage and keep the required things for further use. The process of segregation is called data cleaning. This cleaned data is then processed and is where the statistics come into the picture. The processed data is assembled into proper algorithms for further study and analysis. After this process, the data is used to come up with solutions to solve complex problems, which helps in maximizing the profits of that particular company.

As this field is multidisciplinary, there are many aspects which it involves like mathematics, technology and business. First comes mathematics, this mostly includes statistics and algebra and much more. Many people think that data science is only about statistics and that is false. Statistics is an important part of data science, but not the only thing required. Linear algebra is also important as it uses the algorithms used to process the data.

Next is technical knowledge, such as coding and hacking. It is a vital skill for a data scientist as most of the time he is in contact with data which can be processed by this very skill. To process this data, a data scientist must know his technical stuff as the data provided is enormous and dealing with the data is a tedious process where having great technical skills is a must. When it comes to hacking, it does not mean the word ‘hacking’ that we all have heard about like breaking into computers. It means the creativity in using those technical skills. This is the key quality a good data scientist must have. Python, R, SQL etc are mostly the coding languages used for data science.

And the last comes business acumen, which is also a vital skill for a good data scientist. The data processed needs to be used to solve complex problems to maximize profits. Mostly these problems are core business problems. Therefore, having a good business tactic is important.

Since data science is an emerging field, it has a huge demand. If you have all the above skills, data design is meant for you.

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