4 Lesser-known concepts that every data scientist should be aware of
Data Science is the hottest tech field right now. Penetration of the internet has further increased the popularity of data science.
Concepts such as multicollinearity, one-hot encoding, underdamping, error metrics, and overdamping are the everyday concepts used by data scientists.
Here are four complex concepts that every data scientist should know.
Multicollinearity is a linearly related concept. It can be described as the situation when two or more variables explain similar information.
As the data become redundant, there are ways to find out which feature should be removed that constitutes multicollinearity. For some data modelling, this concept can be overfitting and lead to reduced performance.
2. Probability distributions
This is a statistical concept that every data science practitioner should be aware of.
It gives the probability of occurrences for every outcome of an experiment.
3. Error metrics
This is an important concept as there are plenty of error metrics used for classification and regression in data science models.
Some of the popular metrics used in regression model include metrics.explained_variance_score, metrics.max_error, metrics.r2_score, metrics.mean_absolute_error, metrics.mean_gamma_deviance, metrics.r2_score, metrics.mean_poisson_deviance etc. However the most popular error metrics for regression are MSE (mean absolute error) adn RMSE (mean squared error).
This is a unique and neglected concept in data science. Storytelling can be seen as a concept or skill. Most data scientists focus on model accuracy but they fail to understand the business process.
The focus should be on how to use data to solve the company’s problems. It is essential for every data scientist to master the storytelling skill.