could you elaborate more on this topic
Skewness occurs when there’s no symmetry distribution! (a long tail on one side)
To detect skewness in the data, several methods can be used like:
- Visualization: Skewed distributions will exhibit asymmetry and an uneven concentration of data points on one side. eg in histograms
- Skewness Coefficient: Skewness can be quantified using a skewness coefficient. Commonly used coefficients include Pearson’s skewness coefficient and Fisher-Pearson standardized moment coefficient. A positive value indicates right skewness, while a negative value indicates left skewness.
- Kurtosis: Kurtosis is a measure of the tail of a distribution. High kurtosis values indicate heavy tails, which can be associated with skewness.
Data Transformation: Applying transformations such as logarithm, square root, or cube root can reduce the impact of skewness and make the data more symmetric.
Outlier Removal: Removing extreme outliers can help mitigate the effect of skewness on the data distribution.
Data Scaling: Scaling the data can normalize the distribution and improve model performance.
If you’d like to dive deeper into the topic checkout this new specialization: Mathematics for Machine Learning and Data Science Specialization
Of note, one can immediately learn the third course of this specialization without having taken the previous two.
The fact that you can start with the third course without taking the previous two offers some flexibility to learners who might already have a background in certain areas of mathematics.
If you don’t have a solid background in mathematics I would advise you to follow the sequence due to the interdependence of mathematical concepts, as it assumes knowledge from the preceding linear algebra and calculus courses.