What Process Does A Data Scientist Follow?
Based on his knowledge in the field, the data scientist poses a question that he believes can be answered by large databases. To answer it, data science certification in Hyderabad follow the following process, which can be summarized in 8 steps:
1) Obtaining the data: Massive data usually come from multiple sources (Variety), they can be of different volumes (Volume), they are generated quickly (Speed) and, since there are so many, it is necessary to check that they are correct (Veracity). They are the four “vees” of Big Data.
2) Preprocessing of the data: An initial treatment of the data is carried out, where those data that do not meet quality criteria, are not of interest to the study, contain errors.
3) Transformation and integration: Homogenize the data from multiple sources so that they are comparable between them. This may be due to the structuring (data in table format) or the data restructuring (data in any other format such as text, images).
4) Data analysis: Process data using different algorithms and statistical methods to obtain results that answer the questions posed by data scientists.
5) Interpretation of the data: It is at this point where the data scientist evaluates the result of the analysis and applies the experience he has in the field to understand, complete, and correct the information obtained by the computer.
6) Data validation: See if these data are robust or change due to biases inherent to the data. It can be validated in multiple ways: using data external to the process, using techniques different from those used in the study. But they must always obtain a result similar to those initially obtained to affirm that the results are accurate and not due to chance or bias.
7) Design new analyzes or experiments if necessary: In the scientific procedure, this part is defined as “Validate the hypothesis.” In case the data has not been validated, or more information is needed to obtain conclusive results to the data scientists’ questions, more data is included in the analyses, or the algorithms are reformulated to ask other questions to the data scientists. Data.
8) Visualize and present the data graphically results: It is a fundamental process in any work with large databases to graph the resulting information entirely and with as many layers as possible. The graphs are quick ways to interpret the data to make decisions. The tendency in all scientific articles and everyday life, in general, is to complicate and complete the amount of information obtained in a single image.