Data Scientists do play a key role in building the organization. Many believe that the responsibilities of data scientists are limited to performing job roles like data visualization, data processing, data munging, data mining, etc, while these responsibilities are present, this does not present the complete picture.
Let us clearly understand what data scientists do on a daily basis.
1. One of the major roles of a data scientist is understanding business requirements and solving business problems by accessing the given set of data.
2. Data collection is considered as another major responsibility of a data scientist. This is the process of sorting out the historical data that is required to perform the desired operations.
3. The next phase is the cleaning of data. This is another important responsibility of data scientists which requires the evaluation of the collected data and eliminating unwanted data. This task reduces the complexity of the data to deal with and makes it easy to derive the right solution.
4. After cleaning the data, data scientists need to perform various operations such as data exploration and data analysis. This is considered as an integral procedure performed by a data scientist. Data exploration is more like a brainstorming session to data analysis as this involves the employment of several techniques to the given set of data in order to derive meaningful insights. Understanding the patterns of data helps you derive the most reliable results to solve specific business problems.
5. Data modeling is the next crucial phase to be carried out where a data scientist performs the application of several machine learning algorithms to the given data after deriving the essential insights and identifying the patterns of the data. The data modeling phase gives the most accurate predictions and the best solutions to define any given problem.
6. The next phase is Data validation. In this phase, the selected model is tested to discover if there exists any errors or mismatch or inconsistencies. This phase is important as this helps to identify errors, false predictions, and undesirable insights retrieved in the above stages.
7. After performing all the above mentioned operations, the data scientist now possesses an understanding of the efficiency of the chosen model and he gets ready to deploy the results acquired.
8. After the deployment, the data scientists receive feedback and make necessary corrections considering the comments received.