Analytics dashboard using python and plotly

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Andreas Miller

3:29:03

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1. Introduction

1. Introduction.mp4

04:59

2. Layout design

1. Layout Design and App Layout.mp4

12:11

2. App Layout in Dash.mp4

44:56

3. Creating Test Data and Visuals

1. Creating Test Data.mp4

19:16

2. Quick Error Fix.mp4

00:30

3. Creating the Visuals using Plotly Part 1.mp4

20:03

4. Creating the Visuals using Plotly Part 2.mp4

14:37

4. Adding the Visuals to the App Layout

1. Adding the Visuals to the App Layout.mp4

14:17

5. Basic Callbacks

1. Create Callback Data.mp4

07:55

2. Adding a Slider.mp4

05:49

3. Multiple Output Callback.mp4

07:58

4. Multiple Input Callback.mp4

03:00

5. Chained Callback.mp4

14:16

6. Advanced Callbacks

1. Catching Error with PreventUpdate.mp4

02:48

2. Displaying Error with dash.no_update.mp4

04:05

3. Determining which Input has fired using dash.callback_context.mp4

09:40

4. Improving Performance using Memoization.mp4

02:18

7. Creating a Multi Page App

1. Create a Multi Page Dashboard.mp4

08:10

8. Deploying the App on Heroku

1. Deploying the Dashboard.mp4

11:05

9. Conclusion

1.1 Linkt to Github Repository containing all the code and instructions for deployment.html

1. Conclusion.mp4

01:10

Description

Learn how to create a visual dashboard using dash and plotly.

What You'll Learn?

Create test data using pandas and numpy
Create comprehensive visuals using plotly
Create a dashboard layout using dash
Add additional functionality to the dashboard using dash callbacks
Deploy the dashboard on Heroku

Who is this for?

This course is targeted towards people who are interested in visualisation and want to create a visually appealing dashboard that showcases data. This course is additionally intended for data analysts that want to expand the visual aspects of their analytics.

More details

Description
In this course you will learn to create various visuals and construct an aesthetic dashboard to showcase the visuals. Towards the end of the course you will learn how to add additional functionality to the dashboard and finally deploy the dashboard for production capabilities.

You will learn to create various types of charts using the library plotly, and how to style and update them. The added functionality will be created using advanced and basic callbacks. For instance if you want to display data for multiple years, this course will show you how to add a slider that will update the data based on the value of the input. You will also be introduced to a brief use of pandas and numpy, which are two libraries used for manipulating and performing calculations on data. All the code and the instructions for deployment will be added a resource at the end of the course. As a final note, this course will use a sales analytics dashboard as an example to walkthrough the various components.

This course is targeted towards anyone who is interested in data analytics, data visualisation, python in general, and for those who are just curious to learn something new. The level of python needed for this course is beginner/intermediate. If you don't have any experience with python you are definitely welcome to follow along, however I recommend that you should take a beginners python course before taking this course.

Who this course is for:
This course is targeted towards people who are interested in visualisation and want to create a visually appealing dashboard that showcases data. This course is additionally intended for data analysts that want to expand the visual aspects of their analytics.

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Python

Data Visualization

Andreas Miller

Instructor's Courses

I have been working with python and data analysis for approximately 3 years. I have worked in the Business Tech and Transformation Team in an Asset Management Firm where I helped digitalise various parts of the business. I am currently working on becoming a freelancer within the field of data analysis. I also have knowledge in website design, both frontend and backend, and I am always eager to learn more.

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