Whenever the word data comes up, the first thought that comes to mind is some type of information. Some information which is related to some software. This assumption is true, but there is so much more to it. Data is the information which is collected every second by our technological devices. This information can be collected without any digital technology, but since times have changed this information is mostly in a digital form. This data has become so important that there is an entire stream dedicated to it. This stream is called data science.
Data science, as the name suggests, is a field where this data is extracted and edited using various scientific methods, processes and algorithms. Day by day the demand for data science is increasing and is one of the most demanded fields. The reason for this is due to increasing technology. Because of this technology, the amount of this data generated is increasing and you need more people to segregate this data.
The drastically increasing amount of data is referred to as the big data for all the unstructured data which is generated. This machine-fed data is used for business but needs to be segregated to only get the required information and throw the excess unwanted data. Here, a data scientist comes into the picture. He is the one who segregates this huge amount of data using different algorithms. The amount of data here exceeds 1000 terabytes, which is a lot of data.
A data scientist processes this huge amount of data. This data needs to be processed as it helps to come up with collective dai solutions and better business strategies. First, the data collected is sorted by using various algorithms. This is called data transformation. After this the sorted data is processed and reduced to smaller data which only consists of the required data and the excess data is eliminated. This is called data cleansing or data debugging. Once this data is smaller and shorter, it is used for data mining where this data is studied and visualized for possible solutions. Note that these visualizations are nothing, but data arranged in a better manner for other people to study it.
Many programming languages are used in processing this data. Python is the most common language to be used followed by R, Tableau, SQL etc. To be a good data scientist, it is essential to know these programming languages. Along with programming languages, you should be good with statistics, good visualizing ability and some business nature. It is also vital to have a presence of mind since it is a tedious job. Another important quality is passion. In order to become a good data scientist, passion towards it is important along with the required skills. The eagerness and sharpness is the key to ace the job.
Due to developing technology, data is also changing its form and growing at a drastic rate. It is responsible for bringing up different business solutions. Pursuing data science is beneficial if you are passionate enough about it and possess the required skills.
To pursue data as a career, there are numerous courses available. Gaining popularity, data science courses are easily available in all the major cities. data science course in bangalore are popular as they provide great training.