Big Data Mining Analytics

0 or 1.5 Credits · Level: Graduate

Big data is a relative term—data today are “big” compared with the past and with the methods and devices we have to handle them. The challenge is often summarized by the Four V’s: volume (how much), velocity (how fast data arrives and changes), variety (types and formats such as currency, dates, numbers, text), and veracity (quality and trustworthiness when data is generated organically at scale). Most organizations now generate and retain data from routine processes that can, in principle, be analyzed. To visualize scale: if a traditional statistical study (e.g., 15 variables and 5,000 records) were the size of a period at the end of a sentence, a modern retail database would be the size of a football field—and that still leaves out unstructured sources like social media text. When tackled with the right tools, the rewards are real. For example: a telecom reduced churn by targeting customers most likely to leave; an insurer significantly improved injury‐liability predictions by enriching vehicle‐level information. In finance and business contexts, data mining and big data go hand in hand—from credit scoring and fraud detection to demand forecasting, CLV, and portfolio signals. , This course equips you to turn large, complex data into clear, defensible business decisions. You’ll practice the full analytics lifecycle—problem framing, data preparation, modeling, validation, and communication—using real finance and business cases.