Beginner's Guide to Data & Data Analytics, by SF Data School
4.4 (2,010 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,548 students enrolled

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

The Data Analytics Context We Wish We Had, When We First Started: Concepts, Tools, Roles, Processes, and Terms Explained
4.4 (2,010 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,548 students enrolled
Last updated 2/2019
English
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Current price: $34.99 Original price: $49.99 Discount: 30% off
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This course includes
  • 1.5 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
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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
Requirements
  • No requirements or experience necessary, just an interest to learn more about the world of data
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 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
Course content
Expand all 9 lectures 01:32:17
+ Setting the Stage: An Introduction to Data
1 lecture 12:26

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?​

Preview 12:26
Make sure to check out our free Data Fundamentals Handbook! This resource compliments all the video content in this course, and more, in written form: https://go.sfdataschool.com/data-fundamentals-handbook
Data Fundamentals Handbook
1 question
+ Roles and Skills of Data Professionals
1 lecture 18:11

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.

Preview 18:11
+ Classification of Data Analytics Tools
1 lecture 09:52

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.

Classification of Data Analytics Tools
09:52
+ Deep Dive: The Data Analytics "Tool Triangle"
1 lecture 14:10

At the heart of Data Analytics lies three tools: SpreadsheetsDatabases & 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.

Deep Dive: The Data Analytics "Tool Triangle"
14:10
+ Data Types, Files, and Formats
1 lecture 11:36

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 TypesFiles, and Formats, explain their relation to the use of data tools, and provide real world examples of their importance.

Data Types, Files, and Formats
11:36
+ Data Pipelines: How Data Moves From Collection to Analysis
1 lecture 10:00

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.

Data Pipelines: How Data Moves From Collection to Analysis
10:00
+ Data Flashcards: Key Concepts and Terminology
1 lecture 08:16

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.

Data Flashcards: Key Concepts and Terminology
08:16
+ Data Analytics Learning Roadmap and Career Paths
1 lecture 07:35

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

Data Analytics Learning Roadmap and Career Paths
07:35