Statistics Basics Every Data Science Fresher Should Know

Machine learning gets the attention, but most data science interviews start with statistics. If you are a fresher, these six ideas are worth learning properly before you touch a complex model.

1. Mean, median and mode

The mean is sensitive to extreme values, the median is not. When salaries or prices have a few very large values, the median describes the typical case better.

2. Variance and standard deviation

They tell you how spread out the data is. Two datasets can share a mean and still behave very differently.

3. Probability and distributions

Know the normal distribution, and when data is skewed. Many tests and models assume roughly normal data, so check before you trust the result.

4. Correlation is not causation

Two columns moving together does not mean one causes the other. Interviewers like to ask for an example, so keep one ready.

5. Hypothesis testing and p-values

Be able to explain, in plain words, what a p-value says and what it does not say.

6. Sampling and bias

A model is only as good as the data it learned from. Ask who is missing from the sample.

How to practise

Take one small public dataset, compute these numbers in Python with pandas, and write three sentences about what you found. Employers value a clear explanation more than a long list of tools.

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