Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Exploratory Data Analysis and Data Quality
Understanding and preparing an unfamiliar dataset before analysis or modeling. Covers systematic profiling through summary statistics, distribution and outlier inspection, and relationships between variables to form initial hypotheses, alongside turning raw data into a trustworthy base: handling missing values, deduplication, outlier treatment, type and consistency checks, and validation. Includes critical thinking about sampling and measurement bias and what a dataset can and cannot support.
Python and Pandas for Data Analysis
Programmatic data manipulation and analysis in Python and R. Covers pandas transformations, joins and reshaping, aggregation, working with PySpark for larger data, and using R for statistical analysis. Emphasizes clean, reproducible analytical code.
Causal Inference
Establishing cause-and-effect from observational and experimental data. Covers correlation versus causation, confounding, treatment-effect estimation, and quasi-experimental methods such as difference-in-differences, matching, and instrumental variables. Includes incrementality reasoning when true randomization is not possible.
Optimization and Operations Research Methods
Prescriptive analytics: formulating decisions as optimization problems — linear and integer programming, constraint-based modeling, objective functions, and trade-offs between optimality and tractability. Applied to allocation, scheduling, routing, pricing, and supply/demand problems.
Working with Large-Scale Datasets
Analyzing data that does not fit comfortably in memory or a single query. Covers distributed processing concepts, partitioning and sampling strategies, query and pipeline performance, and trade-offs when scaling an analysis. Focuses on getting correct answers efficiently at scale.
Statistical Inference and Hypothesis Testing
Reasoning about uncertainty in data and drawing formal conclusions from samples. Covers probability rules and common distributions, the Central Limit Theorem, sampling, standard error, confidence intervals, and Bayesian reasoning, together with the significance-testing framework: null and alternative hypotheses, p-values, statistical power, Type I and Type II errors, effect sizes, and choosing the right test (t-test, chi-square, non-parametric). Emphasizes correctly interpreting statistical results and avoiding common misreadings of significance in business and product contexts rather than memorizing formulas.
Metrics and KPI Design
Defining, selecting, and monitoring the metrics that measure a business or product. Covers north-star and supporting metrics, guardrails, metric decomposition, segmentation, and operational monitoring and alerting. Emphasizes choosing metrics that are actionable and hard to game.
Forecasting and Time-Series Analysis
Analyzing and projecting data that moves over time. Covers trend and seasonality decomposition, forecasting approaches, demand modeling, and anomaly detection on time series. Emphasizes reasoning about baselines, drivers, and forecast reliability.
Predictive Modeling and Machine Learning Fundamentals
Applying core modeling techniques to analytical problems. Covers regression and classification basics, clustering and unsupervised methods such as k-means, feature thinking, model evaluation, and judging when a machine-learning approach is warranted over simpler analysis. Framed at the applied data-science level rather than deep ML engineering.