The Evolution of a Data Scientist

By ridhigrg |Email | Jan 20, 2020 | 6117 Views

Data scientists are people who always have an exquisite mindset and want to develop stuff. There is definitely a lack of data scientists in the block with all the essential skills which makes it difficult for companies to meet the demand and hence due to this reason data science course has attracted people. Data Science gives you an opportunity to work with big brands due to their capability to make decisions regarding product features, pricing, and changes. This is one of the safest careers to pursue right now due to the revolution of data science in the tech world. Whichever industry that you are working in, always has different unattended data surrounding it waiting to be explored and give meaning to.

Established as well as startup companies resort to data scientists because of its rising popularity now. This has led to huge job opportunities and data scientists can apply to various jobs which can be as a Statistician as you get well acquainted in math, Software programming analyst, Data engineer, Quality analyst, Spatial data scientist, etc and many others. But the important point is to understand the domain that you are interested in as the skill set of data science is huge with knowledge about many fields included.

Data science is basically a detailed description or prediction for the betterment of the future, wherein you apply these three major skills in a systematic manner which are understanding of mathematics, statistics and algorithms, programming and hacking as well as develop communication skills which are necessary for business. The process of data science is as follows: first, you need to collect the correct raw data which are required for problem-solving, but the data that you acquire cannot be used as you need to process and clean the data to remove all the corrupt records which is known as data wrangling. The next important step is to analyze the data to granular levels and identify the trends and patterns. Then perform an in-depth analysis of the data by all the techniques such as machine learning, statistical models, etc to make the data usage from an extreme level. And finally, the most important step is to be able to communicate your results to the stakeholders in a way that is easy for anyone to understand.

Every good thing always has a hindrance associated with it; data science to has the same problem. You should be able to explain your research and findings to nontechnical audiences who have no idea regarding the concepts. Getting equipped enough to handle raw data and be able to perform all the nitty-gritty stuff like cleaning, extracting, processing, etc. Having specific domain expertise is also tough for some also answering questions and doubts of the audience is of prime importance which sometimes people are not able to do. Privacy and security are also major concern topics for data scientists.

Source: HOB