5 common mistakes in data science

With the boom of big data, organizations began hiring data scientists and adopting new technologies to obtain valuable information from data analysis. Being a data researcher requires high precision and responsibilities since people with this role have a very small margin for errors. So, in this post, we describe the most common errors that data scientists make.

Confusion between correlation and causation

Even if these terms seem very similar, data researchers must recognize the difference. While these two aspects may exist at the same time, the correlation doesn’t imply causation. Causality always applies to cases in which action A causes result B. On the contrary, correlation is only a relationship where action A relates to action B.

These two aspects are generally confused since people love to find patterns even though they don't exist. Individuals generally create these patterns when two variables appear to be so closely related that one depends on the other. This association would imply a causal relationship and effect where the dependent event is the result of an independent event.

Choose the wrong display tool

Different visualization techniques allow scientists to obtain data values. However, most analysts don´t focus on understanding the data using different visualization techniques. They usually don´t know what visualization methodology to use to shape development, monitor exploratory data analysis or show results. Instead, they use graphics most of the time without focusing on the main features of their data set.

Analyze without having a plan

Data science is a discipline characterized by a structured process that begins with clear objectives and questions followed by hypotheses to achieve the objectives. However, most of the time, analysts consider the data without thinking about their objectives or questions that they need to answer through the analysis. Therefore, they collect data that they don’t want.

Consider only the data

Most analysts are excited to collect data from different sources and begin to generate graphs and reports without developing the required business acumen. This situation can be dangerous for companies since data scientists don't give enough importance to understanding how analysis can benefit the organization.

Ignore the odds

Most of the time, data researchers don't consider sufficient possibilities for a solution, convinced that action X will reach objective Y. However, the scenario planning and probability theory are two characteristics of data science that they shouldn't be ignored when making decisions.

In data science, scientists must make sure to reduce the number of mistakes at a minimum. However, making mistakes is part of human nature and some of them are very common in this industry. Above, we describe the most frequent ones, so you can easily recognize and avoid them. 

on March 5, 2020