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Data Visualization in Python (Mplib, Seaborn, Plotly, Dash)

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Data Visualization in Python (Mplib, Seaborn, Plotly, Dash), available at $74.99, has an average rating of 4.7, with 110 lectures, based on 58 reviews, and has 591 subscribers.

You will learn about Explore data sets visually in Python. Create web interfaces to visually present results. Master the most important Python data visualization libraries (matplotlib, seaborn, plotly and dash). Synthesize data sets for presentation to non-technical audiences. This course is ideal for individuals who are Aspiring data scientists who want to master this fundamental skill. or Data analysts who want to learn how to discover relevant information from data. or Professionals who need to communicate complex data visually to third parties. or Students in the areas of programming, science or business who want to present complex information visually. It is particularly useful for Aspiring data scientists who want to master this fundamental skill. or Data analysts who want to learn how to discover relevant information from data. or Professionals who need to communicate complex data visually to third parties. or Students in the areas of programming, science or business who want to present complex information visually.

Enroll now: Data Visualization in Python (Mplib, Seaborn, Plotly, Dash)

Summary

Title: Data Visualization in Python (Mplib, Seaborn, Plotly, Dash)

Price: $74.99

Average Rating: 4.7

Number of Lectures: 110

Number of Published Lectures: 110

Number of Curriculum Items: 110

Number of Published Curriculum Objects: 110

Original Price: $64.99

Quality Status: approved

Status: Live

What You Will Learn

  • Explore data sets visually in Python.
  • Create web interfaces to visually present results.
  • Master the most important Python data visualization libraries (matplotlib, seaborn, plotly and dash).
  • Synthesize data sets for presentation to non-technical audiences.

Who Should Attend

  • Aspiring data scientists who want to master this fundamental skill.
  • Data analysts who want to learn how to discover relevant information from data.
  • Professionals who need to communicate complex data visually to third parties.
  • Students in the areas of programming, science or business who want to present complex information visually.

Target Audiences

  • Aspiring data scientists who want to master this fundamental skill.
  • Data analysts who want to learn how to discover relevant information from data.
  • Professionals who need to communicate complex data visually to third parties.
  • Students in the areas of programming, science or business who want to present complex information visually.

Learn how to synthesize complex data sets easily in a visual way. In this course, you will develop this basic data science skill (data visualization) by exploring real data sets with the most popular Python tools (matplotlib, seaborn, plotly, and dash). You will learn how to extract the most relevant information from data and present it with a variety of graphs and charts to non-technical people.

Learn how to extract visual knowledge from complex data for decision-making with Python.

– Master the main visualization libraries in Python for Data Science.

– Discover and extract the most important knowledge from complex data.

– Learn to build web interfaces with charts to present important results to a wider audience.

Master a basic data science skill.

In the course, you will explore 8 different datasets. You will learn to understand their content and answer questions by building a variety of graphs, basic and advanced. This is a basic data science skill as data science professionals analyze and model data to assist decision-making and solve complex problems. Data visualization is a fundamental part of this process, guiding the data scientist’s analysis and presenting the results in a way that people with diverse profiles can understand.

For the presentation of results, we will create a web interface with the plotly library that will show in real-time the most relevant information of a web page: visits, user types, session duration, purchases, etc.

At the end of the course, you will master all these tools fluently and will be able to visually analyze your own datasets and extract the most relevant information from them.

Course Curriculum

Chapter 1: Welcome

Lecture 1: Welcome & Introduction

Lecture 2: Setting up the environment

Lecture 3: Data Science and Machine Learning series

Lecture 4: Our complete course catalog

Lecture 5: Follow us on social media

Chapter 2: Matplotlib

Lecture 1: A brief introduction to matplotlib

Lecture 2: Line Plot

Lecture 3: Our first graph

Lecture 4: Anatomy of a figure in matplotlib

Lecture 5: The Figure and Axes classes

Lecture 6: Pyplot

Lecture 7: Object-oriented interface

Lecture 8: Add annotations to the graph

Lecture 9: Draw shapes on the graph

Lecture 10: Draw lines on the graph

Lecture 11: Manipulate the axes of the graph

Chapter 3: Data exploration with matplotlib: Iris dataset

Lecture 1: Dataset presentation

Lecture 2: Loading the dataset

Lecture 3: Pie chart

Lecture 4: How many records of each class do we have?

Lecture 5: Modifying the style of the chart

Lecture 6: Scatter plot

Lecture 7: Can we differentiate the species by their petals?

Lecture 8: 3D Plots

Lecture 9: Does it help to know the length of the sepal?

Lecture 10: Box Plot

Lecture 11: What is the range of values for each feature?

Lecture 12: Violin Plot

Lecture 13: Feature value distribution

Lecture 14: Bar chart

Lecture 15: Multiple graphs

Lecture 16: Do different species have different features?

Lecture 17: Global styles

Chapter 4: Image manipulation with matplotlib

Lecture 1: Using images in matplotlib

Lecture 2: Grayscale vs RGB

Lecture 3: Color maps

Lecture 4: Creating complex graph structures

Lecture 5: Creating color histograms for an image

Lecture 6: Increasing image resolution

Lecture 7: Saving images or graphs to a local file

Chapter 5: Seaborn

Lecture 1: Introduction to Seaborn

Lecture 2: Figure level and axes level graphs

Lecture 3: Pandas DataFrames

Lecture 4: Modifying the style of the graphs

Chapter 6: Data exploration with Seaborn: Titanic dataset

Lecture 1: Data presentation

Lecture 2: Loading the dataset

Lecture 3: Density plots

Lecture 4: Who was on the Titanic? – Part 1

Lecture 5: Who was on the Titanic? – Part 2

Lecture 6: How much did the guests pay?

Lecture 7: Does paying more improve the odds of surviving?

Lecture 8: Where did the guests stay?

Lecture 9: What are the odds of surviving?

Chapter 7: Data exploration with Seaborn: Penguin species

Lecture 1: Presentation and loading of the dataset

Lecture 2: The PairGrid class

Lecture 3: How can we differentiate between penguin species?

Lecture 4: The JointPlot class and marginal plots

Lecture 5: Identifying penguins with joint plots

Chapter 8: Data exploration with Seaborn: monthly number of flights

Lecture 1: Presentation and loading of the dataset

Lecture 2: Long vs wide data format

Lecture 3: Evolution of the number of flights

Chapter 9: Plotly

Lecture 1: Introduction to Plotly

Lecture 2: Plotly express

Chapter 10: Data exploration with Plotly: Wind dataset

Lecture 1: Polar charts

Lecture 2: Presentation and loading of the dataset

Lecture 3: Which way is the wind blowing? With what intensity?

Chapter 11: Data exploration with Plotly: FMRI

Lecture 1: Presentation and loading of the dataset

Lecture 2: How does the brain react to certain events?

Chapter 12: Data exploration with Plotly: stocks

Lecture 1: Presentation and loading of the dataset

Lecture 2: How have the stock prices evolved?

Chapter 13: Data exploration with Plotly: price of diamonds

Lecture 1: Dataset loading and presentation

Lecture 2: How many diamonds do we have (by color, cut and clarity)?

Lecture 3: What are the most common features of diamonds?

Lecture 4: How does cut, color and clarity affect the price?

Lecture 5: Which continuous variables affect the price?

Lecture 6: Explore the relationship between carats, cut, clarity and price

Chapter 14: Data exploration with Plotly: carshare activity in Montreal

Lecture 1: Working with maps in Plotly

Lecture 2: Carshare activity in Montreal

Chapter 15: Data exploration with Plotly: car accidents in the US

Lecture 1: Choropleths

Lecture 2: Car accidents in the US by state

Chapter 16: Dash

Lecture 1: Introduction to Dash

Lecture 2: Elements of a Dash application

Chapter 17: Results presentation: World Bank data

Lecture 1: Creating the dash application

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 1 votes
  • 3 stars: 4 votes
  • 4 stars: 11 votes
  • 5 stars: 41 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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