Data Science & Analytics Topics
Statistical analysis, data analytics, big data technologies, and data visualization. Covers statistical methods, exploratory analysis, and data storytelling.
Data Storytelling and Insight Communication
Turning analysis into a persuasive, decision-ready narrative for non-technical stakeholders. Covers structuring an insight, translating statistical results into plain language, framing recommendations and trade-offs, and tailoring the message to the audience. Emphasizes driving action, not just presenting numbers.
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.
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.