Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Probability and Statistical Inference
Covers fundamental probability theory and statistical inference from first principles to practical applications. Core probability concepts include sample spaces and events, independence, conditional probability, Bayes theorem, expected value, variance, and standard deviation. Reviews common probability distributions such as normal, binomial, Poisson, uniform, and exponential, their parameters, typical use cases, computation of probabilities, and approximation methods. Explains sampling distributions and the Central Limit Theorem and their implications for estimation and confidence intervals. Presents descriptive statistics and data summary measures including mean, median, variance, and standard deviation. Details the hypothesis testing workflow including null and alternative hypotheses, p values, statistical significance, type one and type two errors, power, effect size, and interpretation of results. Reviews commonly used tests and methods and guidance for selection and assumptions checking, including z tests, t tests, chi square tests, analysis of variance, and basic nonparametric alternatives. Emphasizes practical issues such as correlation versus causation, impact of sample size and data quality, assumptions validation, reasoning about rare events and tail risks, and communicating uncertainty. At more advanced levels expect experimental design and interpretation at scale including A B tests, sample size and power calculations, multiple testing and false discovery rate adjustment, and design choices for robust inference in real world systems.
Statistical Analysis and Interpretation
Demonstrates sound statistical methods for analyzing quantitative data and translating findings into actionable business insight. Core skills include descriptive statistics and distributional analysis (percentiles, median, mean, standard deviation, measures of dispersion), outlier detection and handling, correlation assessment, and basic to multivariable regression to control for confounding variables. Also covers hypothesis testing with interpretation of p-values and confidence intervals, evaluation of sample size and statistical power, and awareness of pitfalls such as confounding, multiple comparisons, and violations of model assumptions. Candidates should be able to choose appropriate statistical tests for a given analysis, validate models, and present statistical results clearly to nontechnical stakeholders.
Analytical Background
The candidate's approach to analytical, evidence-based problem solving: how they take an ambiguous question, break it into testable pieces, gather and examine relevant information or data, choose appropriate methods to reach a conclusion, and turn that conclusion into a concrete recommendation or decision. This can show up as quantitative work (statistics, data analysis, experimentation, dashboards) or as qualitative and domain-specific analysis (reviewing logs or incidents, case or contract research, market or process analysis, root-cause investigation). Draw on academic projects, internships, or professional work. Focus on the end-to-end path: how the question or hypothesis was framed, what evidence was examined and with what tools or methods, what trade-offs were considered, and how the resulting insight changed a real decision or outcome.