Think You’re a Data Science Expert? Answer These 7 Questions to Find Out

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

Review the fundamentals, sharpen your skills, and ace that interview with this data science pop quiz!Header created by the author using Canva.

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Data science might seem accessible, given the abundance of online tutorials and libraries that make coding and modeling straightforward, but true expertise goes beyond— it requires a deep understanding of data intricacies, statistical theory, and the nuanced decision-making that comes with building data-driven solutions.

Below are seven questions to test your depth of knowledge in data science. If you cannot answer these confidently, you may need to think twice before calling yourself an expert 😉

Linear regression is often the first algorithm taught in data science courses, but do you understand the assumptions that underpin its validity? Here are some of them.

Linearity: The relationship between the independent and dependent variables must be linear. One could verify this using scatter plots, residual plots, or correlation coefficients (i.e., Pearson’s r values).Independence: Observations should be independent of each other. An autocorrelation function can check this in time series data.Homoscedasticity: The variance of residuals should remain constant across all levels of the independent variable. You can visualize this with a residual vs. fitted plot.Normality: Residuals should be normally distributed, often verified using a Q-Q plot or by calculating skewness… Read the full blog for free on Medium.

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Published via Towards AI