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Common Pitfalls an Amateur Data Scientist Faces

The world of business is rapidly becoming data-driven and data analytics is helping the companies to improve efficiency. Therefore, there is no denying the fact that there is a huge demand for data scientists to analyse the data generated and give the required feedback to the companies. However, the job of data scientists requires various technical skills, knowledge of coding, aptitude for problem-solving and structural thinking.

In this article, we will take a look at the 4 common pitfalls an amateur data scientist should be careful of and how they can be avoided.

Focusing less on data and more on algorithms

In the initial rush to use technical skills, an amateur data scientist can easily fall into the trap of using fancy algorithms of machine learning, thus ignoring basic domain knowledge. This leads to biases in models and flawed conclusions. While building a good model, it is important to understand the data that is used, the purpose behind the model, and the domain knowledge.

Making complex algorithms

Most of the time, amateur data scientists forget that a simple machine learning model with good data can beat the complex algorithms. It is not necessary to use fancy algorithms to build a robust model. Thus using complex algorithms with little knowledge of exploring data fails to meet the goal in the long run.

Always keep in mind the main purpose and then start building a simple model with a simple algorithm.

Spending less time on exploring and visualising data

An amateur data scientist often prefers to build a model without properly visualizing and exploring the data. They overlook the importance of spending more time on understanding the data and this can cause serious damage to the model.

Gap in communication

An amateur data scientist often hesitates to seek help regarding the complexities and issues they face as they are afraid of being criticized. They forget that without drawing any feedback one tends to stagnate.

To be a keen data scientist one needs to be an effective communicator. One should always keep in mind that data scientists are meant to solve other people's issues and without communicating they are unlikely to get the desired model.

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