With so much digitalization in recent years, most organizations is in constant need of data science professionals. The escalation of big data in 2010 led to the growth of data science. It was required to support the need of businesses to draw insights from vast unstructured data sets. The abundance of data allows for a more data-driven approach to train machines rather than a knowledge-based approach.
Data science is described as anything related to data, including modeling, analyzing, and collecting. But the most crucial part is its all sorts of applications like machine learning.
The misconception of a data scientist
The general masses have a popular misconception about data scientists. We think a data scientist is only involved in learning AI (Artificial Intelligence) or machine learning. However, most organizations hire data scientists as analysts. Undeniably, they can solve technical problems, but the companies hire them to solve the problems relating to data.
So, what does a data scientist do?
One of the primary duties of a data scientist is collecting data. While collecting data, there will also be involvement of business stakeholders. The stakeholders will have domain knowledge about the project. Through them, we can extract data, whereby they offer lots of references and sources. It might be from a third party or web scraping etc. Also, note that the data collected are raw and not clean.
Preparation of data
After the collection of data, the team will start preparing data. They will clean the data and put it in the proper format. Cleaning of data is vital as it helps to produce a tremendous analytical report and avoids incorrect conclusion. With the help of software programs, they clean lots of raw data and put it in the right order.
Exploratory data analysis
In the exploratory analysis, a data scientist will try to include statistical analysis of the data. Doing statistical analysis helps them understand the data, which is very important while solving machine learning use cases. A data scientist tries to study the behavior of data by involving lots of diagrams or diagram visualization. Because of its thorough analysis, it helps companies their customer behavior and optimized plans according to it.
Evaluation and interpreting exploratory data analysis outcome
After identifying the trend and the pattern, a data scientist has to present the result to the stakeholders. The task can be challenging because a data scientist will have to submit a report to marketing professionals. They may have limited knowledge of data science; hence a data scientist must give the result in a simpler term.
Model testing and building
After sorting out everything, a data scientist will choose potentials models and algorithms. So, in a model building, the data scientists will select one algorithm and perform high parameter optimization or cross-validation to determine the accuracy.
Apart from the accuracy, they also look upon various factors like the confusion matrix or determining the score of ROC AUC. They have to find out if those accuracies are good or not. Once the accuracy is good, they will move to the next stage.
Deployment of model
After the positive outcome on the accuracy, the next step is model deployment. There are various tools for the deployment of the model. One of the tools is Flask. It is a web framework that helps create a REST API and can consume from any front-end application.
Optimization of the model
Once there is the deployment of the model, the next step is to optimize the model. Here, the data scientists will set a month or days and see if the accuracy is good or not with actual test data. They will know the outcome of the accuracy after the model is being applied in the production.
If the model is not providing a good outcome, then a data scientist has to start the cycle again. The process continues until it finds the perfect model.
These are what a data scientist does. They will work closely with the stakeholders to understand their requirements. The data scientists design models or develop algorithms to extract data for business needs. It involves a lot of collecting and analyzing data.