- 1.5 hours on-demand video
- 1 article
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Get your team access to 4,000+ top Udemy courses anytime, anywhere.Try Udemy for Business
- 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
- Discover data tools – which are the most popular, how they work together, and why some are preferred over others
- Demystify how data moves from collection to analysis, and what people, processes and technologies are involved
- Get a step-by-step learning roadmap to becoming a practitioner of Data Analytics, and insight in to career paths that are most relevant
- Context gives each of us the grounding we need to think about data more meaningfully and know it better. Learn to break down some of data's most prized concepts and terms
- No requirements or experience necessary, just an interest to learn more about the world of data
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.
- People who want to learn more about data, but don't know where to start
- Anyone who believes that learning to work with data will change the way they do business, live their lives and help others
- Someone who wants to ultimately work with data tools and learn how to make data-driven decisions
- This course is the first step in learning how to work with data, building the context needed to understand the big picture
- This course is NOT an Excel or SQL tutorial
Finding a straightforward explanations for basic, yet fundamental questions about data are surprisingly difficult to find. In this lesson we'll share our perspective on five (5) critical topics:
Why data, why now?
What is data?
Where does data come from?
Who's using data?
What is data used for?
One of the biggest challenges in getting started with data is understanding how everything learned fits in to the big picture — this lesson introduces a framework for understanding just that. By the end, you’ll be able to clearly articulate what the differences are between Data Analytics, Data Science, and Data Engineering, and how each of these roles provide value in their own way.
The landscape of data tools, in general, is massive. It's important to be able to navigate this landscape efficiently, and understand associated terminology. This lesson will bring clarity to the Data Analytics tool landscape via a homegrown classification system — ultimately helping you find your bearings as fast as possible.
At the heart of Data Analytics lies three tools: Spreadsheets, Databases & Query Languages (i.e. SQL), and Business Intelligence (BI) Software. This lesson will deep dive on each tools’ functionality, compare the similarities and differences between them, uncover the main use cases for each tool, and most important reveal how they work together.
The attributes and properties of data have a big, and very real impact on our ability to leverage data tools and maximize their capabilities. In this lesson, we’ll define Data Types, Files, and Formats, explain their relation to the use of data tools, and provide real world examples of their importance.
Being able to understand how data is collected, moves amongst technology, and ultimately land in your possession for analysis is an incredibly empowering skill. By the end of this lesson you'll internalize the concept of a Data Pipeline, and start building-up a lexicon and literacy for how data moves from collection to analysis.
Context gives each of us the grounding we need to think about data more meaningfully and know it better. Sometimes this fits perfectly in to a broader lesson, and sometimes it doesn't. Either way, we wanted to build a flashcard-style lesson to quickly and thoroughly break down some of data's most prized concepts and terms.
Where to go from here? Here at The San Francisco Data School we're building a step-by-step path to becoming a practitioner of Data Analytics. In this lesson we'll share our learning roadmap, the details behind its construction, and discuss career paths that are most relevant to what we're building