Colby Schrauth - Beginner's Guide to Data & Data Analytics, by SF Data School (Download)

The data world feels overwhelmingly complex. Analytics, data science, data engineering, SQL, Python, Tableau—the terminology alone creates paralysis. Where do you even start? Most beginners dive straight into tool tutorials without understanding the bigger picture, leading to confusion about how pieces fit together or why certain tools matter.

The real challenge isn’t learning tools—it’s understanding the context that makes those tools meaningful. Before touching Excel or SQL, you need to grasp how data professionals work, how data moves through organizations, and where different roles and tools fit within the ecosystem.

Course Overview

Beginner’s Guide to Data & Data Analytics is taught by Colby Schrauth and Serge LeBlanc from SF Data School, who distilled over a decade of combined data experience into the context they wish they’d had when starting. This isn’t a tool tutorial—it’s the foundational understanding that makes everything else make sense.

The course includes free access to the Data Fundamentals Handbook, providing written reinforcement of all video content. In 90 minutes, you’ll gain the perspective that typically takes years to build through trial and error.

What You’ll Learn

The Data Landscape

You’ll start with a clear introduction to the data world that cuts through noise and hype. Rather than overwhelming detail, you’ll understand the big picture: how data analytics fits within modern organizations and why context matters before technical skills.

Roles and Responsibilities

The course clarifies critical distinctions between data analytics, data science, and data engineering—three terms often confused but representing fundamentally different skill sets and value propositions. You’ll understand what each role does, how they collaborate, and which path might align with your interests.

Tool Classification

Discover the most popular data tools, understand why certain tools are preferred for specific tasks, and learn how tools work together rather than in isolation. The “Tool Triangle” framework provides structure for understanding the data analytics tool ecosystem logically.

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Data Movement and Pipelines

You’ll demystify how data moves from initial collection through analysis—the people, processes, and technologies involved at each stage. Understanding data pipelines builds the technical literacy needed to work effectively with data professionals and systems.

Key Concepts and Terminology

Through data flashcards, you’ll learn essential concepts and terms that form the foundation of data literacy. This lexicon enables you to communicate effectively in data-driven environments and understand technical conversations.

Learning Roadmap

Finally, you’ll receive a step-by-step roadmap for becoming a data analytics practitioner, including recommended next steps after this course and insight into relevant career paths. This guidance eliminates the confusion about what to learn next.

Who This Course Serves

This training benefits anyone wanting to work with data but unsure where to start, professionals believing data skills will transform how they work, aspiring data practitioners seeking foundational understanding before tool training, and business professionals needing data literacy without becoming technical experts.

The Bottom Line

Most data education starts with tools: here’s SQL, here’s Python, here’s Tableau. But without context, these skills remain disconnected and their application unclear. This course inverts that approach by building the conceptual foundation first.

You won’t finish as an Excel expert or SQL developer—that’s not the goal. You’ll finish understanding how data professionals think, how data ecosystems function, and where to direct your learning energy effectively. This context transforms subsequent tool training from confusing technical exercises into purposeful skill building.

For anyone serious about entering the data field, this course provides the essential starting point that most beginners skip—and later wish they hadn’t.