Competencies: Mathematics > Probability & Statistics

Descriptive Statistics

Measures of central tendency (mean, median, mode), dispersion (standard deviation, variance), and distribution analysis. Foundation for data analysis and machine learning.

High

Data Scientist, Data Analyst, ML Engineer

Probability Theory

Sample spaces, conditional probability, Bayes' theorem, independence

Critical

Data Scientist, ML Engineer, Security Analyst

Probability Distributions

Normal, binomial, Poisson, exponential distributions, PDF, CDF

Critical

Data Scientist, ML Engineer, Quantitative Analyst

Hypothesis Testing

Null/alternative hypotheses, p-values, confidence intervals, statistical significance

High

Data Scientist, Data Analyst, Research Scientist

Regression Analysis

Linear regression, multiple regression, logistic regression, model evaluation

Critical

Data Scientist, ML Engineer, Quantitative Analyst

Bayesian Statistics

Prior/posterior distributions, Bayesian inference, MCMC, probabilistic programming

High

Data Scientist, ML Engineer

Statistical Sampling

Random sampling, stratified sampling, sample size, bias, variance

High

Data Scientist, Data Analyst, Research Scientist

Time Series Analysis

Trend, seasonality, autocorrelation, ARIMA, forecasting

High

Data Scientist, Quantitative Analyst, ML Engineer

A/B Testing

Experimental design, control groups, statistical power, effect size

High

Data Scientist, Product Analyst

Descriptive Statistics

Beginner

Mean, median, mode, standard deviation from general education; understand confidence intervals and p-values conceptually

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No hypothesis testing, no regression, no Bayesian inference; significant gap