Beginner's Guide to Data & Data Analytics, by SF Data School
What you'll 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
- 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.
Who this course is for:
- 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
Co-Founder of The San Francisco Data School, and Analytics Team Lead at Square. Previously, a Lead Instructor for Data Analytics at General Assembly San Francisco, and an Instructional Associate for Columbia University’s MS in Applied Analytics.
The world of data is incredibly massive. For anyone looking to start their journey in a data profession, it can quickly become an overwhelming endeavor. No one knows this better than Colby Schrauth.
Colby took a non-linear path to becoming a data professional. His educational background is Finance, but he’s spent the majority of his professional career in Business Development roles – selling payroll to small businesses for ADP, helping large enterprises understand the value of online communities with Lithium Technologies, and more. However, the goal of becoming a practitioner of data persisted throughout these experiences.
Along the path to becoming a data professional, Colby was constantly battling internal thoughts of discouragement:
• I don’t have a Computer Science background, will I ever be taken seriously?
• What data tools should I learn, and how do I get started?
• The data ecosystem is complicated and evolving quickly, will I be able to keep up?
The list goes on, and on. Here’s the good news, this real-world experience is what makes him the ideal teacher for those who wish to forge their own path in the world of data. Colby believes that anyone with patience and a deep desire to learn can develop a concrete understanding of data and how to work with it. He’s on an entrepreneurial mission to share the epiphany moments he’s had along the way, and create a linear learning path so that others don’t have to learn the hard way, like he did.
Co-Founder of The San Francisco Data School, and Manager of Product Analytics at Nerdwallet, with Previous Experience as Lead Analytics Instructor at General Assembly.
Having always been a teacher at heart, Serge started his career in physics, completing a Ph.D. in the field with the intention of becoming a professor in academia.
When Serge discovered that the world of academia wasn’t for him, he looked for other career paths where he could use his analysis and problem solving skills learned as a physicist, which led to his career in analytics.
After having to learn the concepts and skills in the world of data the hard way, Serge developed a passion for helping others interested in the field accelerate their growth path.