A data scientist is a person who is tasked with the role of identifying data patterns and trying to analyze real-time data while at the same time providing the meaning of the trends detected. Recent reports suggest that the demand for data scientists is increasing with suggestions that by 2020, the United States will require more than 2.7 million data scientists. Here are some common mistakes that individuals intending to become professional data scientists can avoid.
More Learning with Less Application
Most of the newbies in the field of data science have the habit of accumulating a lot of concepts without applying them. This means that a person has significant knowledge about data science concepts but cannot apply them in a real-life scenario. Theoretical learning is not enough, and upcoming data scientists need to apply the acquired skills so that they can understand their strengths and limitations.
Ignoring Maths and Statistics
This is a common mistake among most of the data scientists. Most of them forget that data science deals predominantly with figures and facts. Deep learning and machine learning cannot be understood by a person who does not have skills in calculus and linear algebra. Knowledge in statistics is a fundamental aspect for any data scientists as it helps in determining the relationship between data entities while at the same time helping in avoiding the confusion between causation and correlation.
Choosing Wrong Visualization Tools
Most of the data scientists continuously make the mistake of selecting the wrong data visualization tool to present the results of the analysis. This means that most of the individuals who are supposed to make decisions using the analyzed data cannot understand it. Choosing the right visualization tools will help other people, especially those who do not know data analysis to understand the whole concept.
Using the Wrong Population when Building Models
Mostly, data scientists are required to build a model based on the behavior of a particular population. The data scientists might only use a specific group of the community due to inadequate sampling techniques which will skew the model in a specific direction. Due to the fact that the data analyst has ignored specific groups in the population, the model cannot be used to explain or predict the exact behavior of the whole community.