Beginner's Guide to Data & Data Analytics, by SF Data School

Beginner's Guide to Data & Data Analytics, by SF Data School

The inspiration for building this course is right in the title – it's the Analytics Context We Wish We Had, When We First Started.

This course now includes free access to our Data Fundamentals Handbook, which compliments all the video content in this course in written form.

This course starts with an introduction to the world of data. Context is critical, and it most definitely applies to learning how to work with data. Before even touching a data tool, amongst many other things, we believe it's vital that one fully understands the context surrounding data.

From there you'll delve deep in to the differences between Data Analytics, Data Science, and Data Engineering, and how each of these roles provide value in their own way. In addition, you'll gather a deep understanding of the tools used by professionals – which are the most popular, when one would be preferred over another, and how they can be used in collaboration.

Next, you'll learn about the technical processes that encompass the lineage of data. This section will enable you to internalize the concept of a Data Pipeline, and start building-up a lexicon and literacy for how data moves from collection to analysis.

Finally, you'll see a step-by-step learning roadmap to become a practitioner of Data Analytics. In this section you'll gain access to recommended steps to take after this course, and career paths that are most relevant.

One of the biggest challenges in getting started with data is finding the right place to start, we believe this is it. You are 90 minutes away from truly understanding the world of data – a perspective we've built over a decade of experience.

The Data Analytics Context We Wish We Had, When We First Started: Concepts, Tools, Roles, Processes, and Terms Explained

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What you will learn

• Free access to our Data Fundamentals Handbook, which compliments the video content in this course in written form
• The world of data is massive, but that doesn't mean it has to be complicated. Cut through the noise and get a clear vision of the "Big Picture"
• Learn the distinguishing factors between Data Analytics, Data Science, and Data Engineering

Rating: 4.4959

Level: All Levels

Duration: 1.5 hours

Instructor: Colby Schrauth