Beginner's Guide to Data & Data Analytics, by SF Data School (Udemy.com)

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

( 267 Reviews )

Created by: Colby Schrauth

Produced in 2019

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
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

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 88 / 100

Course Description

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 manyother 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 practitionerof 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 startAnyone who believes that learning to work with data will change the way they do business, live their lives and help othersSomeone who wants to ultimately work with data tools and learn how to make data-driven decisionsThis course is the first step in learning how to work with data, building the context needed to understand the big pictureThis course is NOT an Excel or SQL tutorial

Time: 1.5h

Instructor Details

Colby Schrauth

4.4 Rating
267 Reviews

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 Universitys 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 hes 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 dont 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. Heres 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 c