COMPONENTS OF THE DATA SCIENCE TECHNOLOGY

INTRODUCTION
We all hear about data science technology frequently. No doubt, data science technology is the most overwhelming technology for the present time. But data science technology is the blender of different technologies. Some of the most common technologies are artificial intelligence, machine learning, IoT, data analytics, big data, etc. These technologies contribute to data science technology in different ways. Moreover, there are many sub-processes and components in data science technology.
COLLECTION OF THE DATA
Data science is also known as the data-driven approach. As it is very clear from its name, the most important requirement is the data. The companies have to gather the data of the customers from different sources. Generally, the data is gathered from the database of the customers. As mentioned above, the big data is a part of the data science technology. Then, what does big data stands for? Many people think that big data is the kind of data with huge volume. But this is not the exact definition of the big data. Big data is identified by the following properties.
Velocity
Veracity
Volume
CLEANING THE DATA
Cleaning the data is the most important process. In this process, the data is clarified. In such process missing values are filled, duplicate data is removed, improper values are removed and the data is arranged in the correct way. All these tasks take place under this process. From this process, all the processes are considered under the data analytics until and unless the useful information is squeezed out from the data. Data analytics is also a part of the data science technology. The processes taking place under data analytics are extraction, transformation, visualization and modeling of the data. All these processes are done to extract useful information from an ample amount of the data.
ANALYSIS OF THE DATA
The data analysis is performed by the methodology mentioned above. There are different types of data analysis and the type of analysis to be used is based on the type of problem you are working on. Data analysis includes some methodologies like prescriptive analysis, descriptive analysis, and predictive analysis. For this reason, a better understanding of the problem is necessary so that the type of analysis can be decided. Data cleansing operations are performed to get data structured. Moreover, the data mining process is applied to the data to discover the hidden patterns from huge amount of the data.
BUSINESS INTELLIGENCE REPORTS
The results can be obtained after analyzing the data. Then the necessary actions can be taken accordingly. The results are based on the data mining procedures applied. Moreover, the results also depend on mathematical models. These mathematical models accept the data as an input and provide predicted values accordingly. The technology, which comes into light at this stage is machine learning.
CONCLUSION
Data science is a very effective technology. This technology has a great demand. Students can choose this technology as a career option and can take a course on it.
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