Statistical Concepts Every Data Scientist Should Know.

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Here are the top 10 statistical concepts every data scientist should know:

1) Probability - understanding the basics of probability theory is essential for a data scientist.

2) Descriptive Statistics - summarizing, organizing, and presenting data through measures of central tendency, dispersion, and distribution.

3) Inferential Statistics - making predictions and drawing conclusions from data, taking into account the uncertainty inherent in the data.

4) Hypothesis Testing - testing claims about a population based on a sample of data, including t-tests, ANOVA, and chi-squared tests.

5) Regression Analysis - modeling the relationship between variables, including linear and logistic regression.

6) Bayesian Statistics - a branch of statistics that deals with updating probabilities based on new data, using Bayes’ Theorem.

7) Time Series Analysis - analyzing and modeling time-dependent data, including trend analysis and forecasting.

8) Sampling - understanding the principles of random sampling, sampling distribution, and sampling error.

9) Machine Learning - using algorithms to identify patterns and make predictions based on data.

10) Experimental Design - understanding the principles of designing experiments, including controlling variables, randomization, and blinding.

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