Amazon (AWS) QuickSight - Getting Started
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Amazon (AWS) QuickSight - Getting Started

Get started with Amazon QuickSight - AWS' Business Intelligence (BI) answer to Tableau and Power BI
4.8 (26 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
390 students enrolled
Last updated 6/2017
English
Curiosity Sale
Current price: $10 Original price: $110 Discount: 91% off
30-Day Money-Back Guarantee
Includes:
  • 5.5 hours on-demand video
  • 4 Articles
  • 9 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Assignments
  • Certificate of Completion
What Will I Learn?
  • At the end of this course students will be able to connect QuickSight to different data sources and create their own analyses
  • Students will understand the basic concepts behind QuickSight and its positioning within AWS
  • Students will be able to dive deeper into QuickSight and feel comfortable in working with the tool and its different functions
View Curriculum
Requirements
  • For this course, a credit card is required to create an AWS account
  • A basic understanding of data analysis is a plus but not required
Description

Working with modern Business Intelligence tools is exciting. Although the market offers a broad variety of tools, you may not have found the tool that meets all your requirements yet. This course might change that!

In this course, you will learn how to use one of the latest Business Intelligence tools released to the market: Amazon QuickSight, a tool which allows you to easily analyze and visualize data. But what makes QuickSight special? QuickSight is a cloud solution and completely integrated into Amazon Web Services (AWS). With that, it can be easily connected to a broad variety of services and sources which make QuickSight a highly scalable, easy-to-use and very flexible data analysis tool. 

This course will give you a first overview of QuickSight including the following topics:

  • How to use QuickSight and its different functions
  • Understand the workflow of QuickSight
  • How to connect QuickSight to different data sources within and outside of AWS
  • How to prepare your data in QuickSight, for example by adding filters and calculated fields
  • How to create your analysis by building multiple visuals
  • How to create dashboards and stories
  • Share your project results with people within and outside your organization
  • How to use the iOS mobile app
  • Understand the user management of QuickSight
  • And more!

These topics will be covered throughout this course, but is this your course?

If you...

  • ... never worked with QuickSight and want to get started with it
  • ... are looking for a cloud based Business Intelligence tool to quickly analyze your data
  • ...have worked with other Business Intelligence tools but want to take a look at new tools
  • ... already worked with AWS and now want to understand how to analyze and visualize your data using a service within the AWS universe
  • ...are generally interested into data analysis

...then this course is made for you!

I would be really happy to welcome you in this course!



Who is the target audience?
  • People who never heard of or worked with QuickSight and who want to get started with the tool
  • Anybody who is interested into analyzing and visualizing data with modern Business Intelligence tools
  • People who want to learn how to connect QuickSight to different services within the AWS universe
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Curriculum For This Course
73 Lectures
05:43:07
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Welcome & First Steps
8 Lectures 35:45

Welcome, great to have you on board! Let's take a quick look at the content of this course.

Preview 02:14

Let's understand what we can do with QuickSight and take a look at its specific components.

Preview 03:50

We now have a basic understanding of QuickSight. But was is AWS and why do we need it to use QuickSight?

Preview 02:36


We know what we need to start our first project, so let's create our AWS and our QuickSight accounts!

Preview 08:15

We created our accounts, now it's time to connect QuickSight to our source file and to prepare our data set.

Preview 08:05

After finishing our data set, let's now take a look at the analysis and see how easy it is to create our first chart!

Preview 08:03

Let's take a more detailed look at the structure of this course and at the different topics we will cover.

Preview 01:40
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QuickSight - Starting with the Basics
5 Lectures 17:44

QuickSight is a relatively new business intelligence tool. Let's take a look at the development of the last months and also see how we can ensure to always be up-to-date regarding new features and functions.

The Development of QuickSight
02:19

When working with data, structure is a really important topic. Therefore, understanding the workflow of QuickSight will help us to keep track of our project.

Understanding the Workflow
02:39

Let's take a closer look at the interface now to make sure that we always find our way when working in QuickSight.

Looking at the Interface
06:22

SPICE is an important part of QuickSight. Let's understand why this is the case and what SPICE is actually doing.

SPICE - What it that actually?
03:44

The availability of different editions and the pricing can be confusing when starting to work with a tool. Let's avoid that confusion and take a look at the Standard and the Enterprise edition of QuickSight.

Pricing and Editions of QuickSight
02:40
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Preparing our Data
17 Lectures 01:31:13

Time to dive deeper into our course project. But before we start: Where are we now in the workflow of QuickSight?

Module Introduction
01:49

Generating great results is highly dependent on the quality of the source data. What data preparation steps can be done in QuickSight and where might additional tools be required?

Before we Start: Understanding Data Preparation in QuickSight
03:55

We can connect QuickSight to a broad range of data sources. Let's learn why the data source connection type has an impact on our data preparation steps.

Loading Data: SPICE vs. Direct Query
03:53

QuickSight is a part of AWS, so why not using AWS' cloud storage to upload our project data? 

Introducing AWS S3
06:13

Time to learn how to create a bucket in S3 and to upload our project file to this bucket. After our data is in S3 we need to import it to QuickSight. Let's understand how all this works.

Uploading Data and Importing Data to QuickSight
12:47

We now loaded our source data into QuickSight. Time to understand the interface of the data preparation section.

A Closer Look at the Interface
06:49

Let's learn how to work on columns and why we need fields in our data set.

Working with Columns and Fields
06:38

We already have data in our source file, but what if we need to add additional calculations? Let's understand how to do this using calculated fields.

Taking a First Look at Calculated Fields
05:04

We understood how to create simple calculated fields. But what additional functions do we have in QuickSight and how can we categorize these?

Understanding Functions and Operators
04:03

Let's apply different string functions to our project right now!

Adding Calculated Fields using Strings to our Project
11:11

We already worked with string functions. Time to understand how to extract specific information out of strings.

Extracting Information out of Strings
02:43

Time to add another calculated field to our project - This time we will add a conditional function and combine it with an operator.

Working with Conditional Functions
08:24

Let's take another look at a very important conditional function, the IF-Function, and use it in our project.

Another Look at the IF-Function
04:04

We understood various functions, but we didn't work with numeric values so far. Time to change that now!

Creating Calculated Fields with Numeric Values
03:11

We have a lot of data in our data set. Let's focus on the important information and understand the different filter types in QuickSight.

Adding Different Filters to our Project
06:08

Everything worked fine in our project - but what if we run into problems? Let's understand some general error sources and how we can avoid them.

A Little Helper: Dealing with Skipped Rows
02:49

We finished the data preparation, created our own data set and are now ready to start the analysis. Really great, but let's first summarize what we learned.

Module Summary
01:32

After finishing this module, it’s time to practice! Let's now test our knowledge regarding connecting QuickSight to our data source and how to work on that data to create our data set.
Time to Practice: Data Preparation
1 question
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Analyzing and Visualizing our Data
18 Lectures 01:27:14

Before we start our analysis: What did we achieve so far and what is the goal of this module?

Module Introduction
01:07

Let's understand why the data preparation and the data analysis are two separate steps in our project.

Preparing Data vs. Analyzing Data - Understanding the Differences
02:36

Until now we only worked on our data set. Time to change that by creating our analysis!

Creating the Analysis
02:50

Time to understand the interface of the analysis section. Let's take this chance and also create our first visual!

Understanding the Interface and Creating our First Visual
09:08

When creating our visual we saw that we have two different field types. Let's understand the differences between a dimension and a measure and the role of these items in our charts.

Understanding Dimensions and Measures
02:55

So far we worked on our course project data set. Let's now understand how we can add an additional data set to our analysis.

Adding Additional Data Sets to our Analysis
09:07

After creating our first visuals we will dive deeper now. Time to take a closer look at formatting, aggregation and granularity to improve the quality of our visuals!

Understanding Field Formatting, Aggregation and Granularity
05:25

We understand how to define what should be visualized. Let's now see how we can work on the formatting of our visuals.

Formatting our Visuals
06:04

Our data has different aggregation levels. Let's add a drill-down to our visual to be able to display different levels of detail in a single visual.

Adding Drill-Down
03:28

After finishing the first part of our analysis, it's time to start our story. Let's understand what stories are and how we can create them.

Creating our First Story
03:57

Let's add a treemap to our analysis - another great visual!

Creating a Treemap
01:43

We already talked about filters in the last module. Let's understand the differences between a filter in the data preparation module and in this module and see how we can apply filters to our analysis.

Applying Filters
13:09

Time to take a look at pivot tables, a special visual. Let's understand how we can create it and what calculations we can apply to it.

Understanding Pivot Tables
11:17

Our project keeps growing, time to add another scene to our story.

Continuing our Story
01:26

Let's add a heat map to our analysis and understand why this visual can enable great insights into our analysis.

Understanding Heat Maps
02:35

Time to create our last visual. Let's understand KPI's and take a look at the different analyses we can perform using this visual.

Adding a KPI visual
08:02

We created our last visual, so let's also finish our story now.

Adding the Final Scene to our Story
01:08

We finished the analysis and created a lot of great visuals. Time to summarize what we learned in this module.

Module Summary
01:17

We also finished the analysis and visualization module. Now that we know how to create beautiful visuals, let's practice that in our second assignment.
Time to Practice: Analysis and Visualization
1 question
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Refreshing, Exporting and Sharing our Project Data
13 Lectures 48:05

We finished the work in our project, so what now? 

Module Introduction
01:08

Depending on our goals, we have different options now. Let's understand how we can continue at this stage.

Our Ways to Continue
01:37

Let's take a look at refreshing to ensure our data set is always based on the most recent source data.

Understanding Refresh and Schedule Refresh
06:42

We created visuals, but what if we only need the raw data behind that visuals? Let's find out how we can export this data.

Exporting our Project Data as .csv Files
01:44

We want to share our project with other users in QuickSight. To do this, we need to add users to our QuickSight account first. Time to understand how we can manage users and their different roles in QuickSight.

Adding Users to our Account - Some Theory
04:02

AWS - More about IAM (Optional)
01:03

We understood the theory behind the user management - Time to apply our knowledge in the project now.

Adding a User to our Account - Continuing with the Project
08:07

We added the user, but this is it not enough. Let's learn how we can invite this user now to our data set.

Sharing our Data Set
04:44

After giving the user access to our data set, we want to share more. Time to understand how to share our analysis.

Sharing our Analysis
04:59

Sharing of data sets and analyses is clear now, but what are dashboards? Let's take a closer look at these and also understand how we can share dashboards with other users.

Creating and Sharing Dashboards
04:27

After learning how to export and share our data, it's time to take a closer look at the specific features behind our QuickSight account.

Managing Capacity and Understanding Subscriptions
05:55

We now know how to share our data on the computer. Let's now understand how the iOS app works.

Taking a Quick Look at the Mobile App
01:59

Data export, data sharing and account management is clear for us now. Let's recap what we learned about these topics.

Module Summary
01:38
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Databases as Data Sources
11 Lectures 37:39

Let me introduce you to this module and what you're going to learn in it!

Module Introduction
00:54

In order to connect to a database, we of course need one! Don't have one? Let's set one up together!

Setting up a Database (Optional)
06:04

Want to learn more about AWS RDS? This lecture is for you!

More Information about AWS RDS
00:19

We got a database but of course we also need data for that to be of any use. Time to add such data!

Preparing Dummy Data (Optional)
04:28

With a database (with data) being available, this lecture will now teach you how to connect Quicksight to a (relational) database.

Connecting Quicksight to a Database
08:32

Need more information about connecting to databases (from Quicksight)? This lecture helps!

More Resources about Connecting Quicksight to Databases
00:11

We set up a connection to the database, now the question is how to get the data into Quicksight. There are two options, let's start with the SPICE option in this lecture.

Importing Data into SPICE
05:42

After importing data into SPICE, let's now take a closer look at option number 2 - direct queries.

Importing Data as a Query
05:58

Direct queries got one other "special thing". Learn which one that is in this lecture.

Calculated Fields & Query Imports
02:02

So far, we only considered SQL databases. What about NoSQL though? This lecture explores this question.

What about NoSQL Databases?
01:35

Let me wrap this module up and summarize what we learned thus far.

Wrap Up
01:53
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Course Roundup
1 Lecture 01:58

You finished this course, well done! Let's now recap what we learned and how we can continue with this knowledge.

Congratulations
01:58
About the Instructor
Manuel Lorenz
4.6 Average rating
320 Reviews
2,031 Students
2 Courses
Professional Business Analyst

Having worked as a business analyst in both a major consultancy and an investment bank, I always found myself confronted with both various and complex datasets and challenging client demands. Therefore, the increasing amount of data required constant adaption of new methodologies to analyze data efficiently and to make the move from basic Excel-driven analyses over VBA-driven automation to more elaborate business intelligence tools. 

Being an early adopter of new and quickly evolving tools, I always enjoyed both learning these tools and passing on my knowledge to my colleagues and fellow students. It's that combination of self-taught data analysis experience and its application in a highly competitive consulting environment for various clients which gave me the ability to evaluate tools from an industry perspective as well as from a learner's perspective. The latter also allows me to identify the pain points students might hit when learning these tools.

Since I always found it hard to find high quality, understandable and comprehensive learning materials focusing on the key capabilities of the specific tools, I decided to take a shot and to create such materials on my own.

And of course I am not only passionate about creating these materials but also about passing on my knowledge using these materials to you.

Maximilian Schwarzmüller
4.7 Average rating
43,175 Reviews
132,772 Students
15 Courses
Professional Web Developer and Instructor

Experience as Web Developer

Starting out at the age of 13 I never stopped learning new programming skills and languages. Early I started creating websites for friends and just for fun as well. This passion has since lasted and lead to my decision of working as a freelance web developer and consultant. The success and fun I have in this job is immense and really keeps that passion burningly alive.

Starting web development on the backend (PHP with Laravel, NodeJS) I also became more and more of a frontend developer using modern frameworks like Angular or VueJS 2 in a lot of projects. I love both worlds nowadays!

As a self-taught developer I had the chance to broaden my horizon by studying Business Administration where I hold a Master's degree. That enabled me to work in a major strategy consultancy as well as a bank. While learning, that I enjoy development more than these fields, the time in this sector greatly improved my overall experience and skills.

Experience as Instructor

As a self-taught professional I really know the hard parts and the difficult topics when learning new or improving on already-known languages. This background and experience enables me to focus on the most relevant key concepts and topics. My track record of many 5-star rated courses, more than 100.000 students on Udemy as well as a successful YouTube channel is the best proof for that.

Whether working as development instructor or teaching Business Administration I always received great feedback. The most rewarding experience is to see how people find new, better jobs, build awesome web applications, acquire amazing projects or simply enjoy their hobby with the help of my content.