From Data to Decisions: Understanding Data Science, Machine Learning, and Big Data

Your dashboards can tell you exactly what happened last quarter, but not what's about to happen next. That gap, between reporting and prediction, is where big data, data science, and machine learning come in: three connected capabilities, not three separate technologies to evaluate on their own. The Data Literacy Guide to Data Science, Machine Learning, and Big Data breaks down the three characteristics that define big data at scale, the three core machine learning approaches, and the shift from traditional ETL to ELT that big data environments require. By the end, you'll have the vocabulary to ask sharper questions and engage productively with the teams building these capabilities in your organization.

Purpose

To build a working understanding of big data, data science, and machine learning as a connected capability stack, not three separate topics.

Outcome

• Understand how big data, data science, and machine learning connect as a progression, from raw material to prediction
• Learn the three characteristics that define big data, and the three core machine learning approaches
• Recognize why big data environments require a different approach to storing and processing data

This Course Includes

• An interactive, self-paced e-learning module (5–10 minutes)
• Real-world scenarios and hands-on interactions: accordions, flashcards, and stepped processes built around the topic

Access

This course is included in the full Data Literacy Guide (DLG) collection: one purchase gets you all available DLG courses, including any new releases, for a full year, only $99.