DATA-VIS-PYTHON.AJ2
Data Visualization with Python
Learn Python programming for data analysis and visualization. Transform raw data into beautiful visuals conveying meaningful insights.
- Practice in 54 Laboratorios prácticos — nothing to install
- 9 Lecciones interactivas y 39 topics mapped to the official exam objectives
- 90 Preguntas del examen de práctica
Intermediate A tu propio ritmo · 1 año de acceso
54 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
9Lecciones interactivas
39Topics
54Laboratorio en vivo
90Preguntas del examen de práctica
78Vídeos
36Tarjetas didácticas
36Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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No se requiere tarjeta de crédito
Data Visualization with Python is a training course that equips you with the skills to transform raw data into compelling, interactive visualizations and insights. The comprehensive course comes with interactive learning features like flashcards, quizzes, video tutorials, and hands-on Labs to make learning a fun experience for you. You’ll learn the fundamentals of data visualization with Python, including plotting with pandas and seaborn. By the end course, you’ll be able to communicate insights effectively and make data-driven decisions.
- Expertise in data manipulation using pandas for data cleaning, filtering, and transformation
- Visualization with the use of libraries like matplotlib and seaborn for creating various static plot types
- Building interactive plots that allow dynamic exploration with Altair library
- Ability to communicate insights and trends effectively through visualizations
- Knowledge of global insights and summary statistics to represent overall trends and key metrics
- Geographical data visualization with Choropleth maps and other techniques
- Expertise in handling temporal data visualizing time-series
- Awareness of common pitfalls and guidelines for creating effective visualizations
Course Highlights
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9 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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54 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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90 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
9 Lecciones interactivas · 39 topics01 Introduction 2 topics +
- About
- About the Course
02 Introduction to Visualization with Python – Basic and Customized Plotting 5 topics · 12 Laboratorio en vivo +
- Introduction
- Handling Data with pandas DataFrame
- Plotting with pandas and seaborn
- Tweaking Plot Parameters
- Summary
12 Laboratorio en vivo in this lesson — see the labs panel →
03 Static Visualization – Global Patterns and Summary Statistics 4 topics · 9 Laboratorio en vivo +
- Introduction
- Creating Plots that Present Global Patterns in Data
- Creating Plots That Present Summary Statistics of Your Data
- Summary
9 Laboratorio en vivo in this lesson — see the labs panel →
04 From Static to Interactive Visualization 5 topics · 5 Laboratorio en vivo +
- Introduction
- Static versus Interactive Visualization
- Applications of Interactive Data Visualizations
- Getting Started with Interactive Data Visualizations
- Summary
5 Laboratorio en vivo in this lesson — see the labs panel →
05 Interactive Visualization of Data across Strata 4 topics · 10 Laboratorio en vivo +
- Introduction
- Interactive Scatter Plots
- Other Interactive Plots in altair
- Summary
10 Laboratorio en vivo in this lesson — see the labs panel →
06 Interactive Visualization of Data across Time 10 topics · 7 Laboratorio en vivo +
- Introduction
- Temporal Data
- Types of Temporal Data
- Understanding the Relation between Temporal Data and Time-Series Data
- Examples of Domains That Use Temporal Data
- Visualization of Temporal Data
- Choosing the Right Aggregation Level for Temporal Data
- Resampling in Temporal Data
- Interactive Temporal Visualization
- Summary
7 Laboratorio en vivo in this lesson — see the labs panel →
07 Interactive Visualization of Geographical Data 4 topics · 7 Laboratorio en vivo +
- Introduction
- Choropleth Maps
- Plots on Geographical Maps
- Summary
7 Laboratorio en vivo in this lesson — see the labs panel →
08 Avoiding Common Pitfalls to Create Interactive Visualizations 5 topics · 4 Laboratorio en vivo +
- Introduction
- Data Formatting and Interpretation
- Data Visualization
- Cheat Sheet for the Visualization Process
- Summary
4 Laboratorio en vivo in this lesson — see the labs panel →
09 Appendix A: Data Structures, Strings, and Numpy +
Laboratorios prácticos Our edge
54 Laboratorio en vivos- Creating a User-defined Function
- Aplicar la función ceil() en una columna DataFrame
- Adding a Column to a DataFrame
- Aplicando la función describe()
- Viewing Data from Dataset
- Deleting Columns from a DataFrame
- Reading Data from a File
- Creating a Bar Plot and Calculating the Mean Growth Rate Distribution
- Creación de gráficos de barras agrupados por una característica específica
- Trazar un histograma
- Tweaking the Plot Parameters of a Grouped Bar Plot
- Annotating a Bar Chart
- Presenting Data across Time with Multiple Line Plots
- Creating a Static Line Plot
- Creating a Static Hexagonal Binning Plot
- Creating a Static Scatter Chart
- Creación de un gráfico de contorno estático
- Creating a Static Heatmap
- Creating a Linkage in a Static Heatmap
- Creating a Static Box Plot
- Creating a Static Violin Plot
- Creating the Base Static Plot for Interactive Data Visualization
- Adding a Slider to the Static Plot
- Adding a Hover Tool to a Scatter Plot Using bokeh
- Creating an Interactive Scatter Plot
- Using the merge() function
- Adding Zoom-In and Zoom-Out to a Static Scatter Plot Using altair
- Adding Hover and Tooltip Functionality to a Scatter Plot Using altair
- Exploring Select and Highlight Functionality on a Scatter Plot Using altair
- Performing Selection across Multiple Plots
- Performing a Selection Based on the Values of a Feature
- Adding the Zoom Feature and Calculating the Mean on a Static Bar Plot
- Representing the Mean on a Bar Plot using a Shortcut
- Linking a Bar Plot and a Heatmap Dynamically
- Adding a Zoom Feature on a Static Heatmap
- Creating a Bar Plot and a Heatmap Next to Each Other
- Calculating zscore to Find Outliers in Temporal Data
- Performing Upsampling and Downsampling in Temporal Data
- Using shift and tshift to Shift Time in Data
- Adding Zoom-in and Zoom-out Functionality on a Line Plot Using Bokeh
- Adding Interactivity to Static Line Plots using Bokeh
- Changing the Line Color and Width on a Line Plot
- Adding Box Annotations to Find Anomalies in a Dataset
- Creating a Worldwide Choropleth Map
- Tweaking a Worldwide Choropleth Map
- Adding Animation to a Choropleth Map
- Creating a Choropleth Map for the US Population across States
- Creating a Scatter Plot on a Geographical Map
- Creating a Bubble Plot on a Geographical Map
- Creating Line Plots on a Geographical Map
- Visualizing Outliers in a Dataset with a Box Plot
- Dealing with Outliers
- Dealing with Missing Values
- Creating a Confusing Visualization
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
Why should I learn data visualization with Python? +
There are many reasons to explain why you should learn data visualization, we have listed a few here:
- It is a powerful tool for transforming raw data into meaningful insights
- It will enhance your decision-making capabilities
- You’ll be able to communicate insights effectively
- You’ll gain a new perspective on problem-solving
- Data visualization is a highly sought-after skill. Learning it will increase your job opportunities with higher compensation
Who should do this Python Data Visualization course? +
This course is ideal for all those who intend to work with data, for instance data analysts, data scientists, business analysts, marketing professionals, researchers, and students. Anyone who wants to utilize data visualization to get better at decisions-making, communicating, and enhancing their career should consider taking this course.
Do I need prior programming experience for this Data course? +
No, you don’t need any programming experience to take this course.
Will I get a certificate at the end of the course? +
Yes, you’ll be awarded a certificate of completion after scoring 90% or more in the final assessment.
What can I do next after finishing this course? +
After finishing this course, you can:
- Continue practicing your skills and experiment with new tools and techniques to get better at data visualization
- Explore new Python libraries, such as Plotly and Bokeh
- Apply your skills to build personal or professional projects
- Seek certifications to become a Certified Data Analyst (CDA) or Certified Data Scientist (CDS) and also to validate your skills
- Pursue advanced topics to deepen your knowledge
Does this course cover any advanced topics?+
The course content focuses on providing a strong foundation in data visualization with Python. However, it also covers some advanced topics but doesn’t go deep into them. These topics include interactive visualizations, Geospatial data visualization, 3D visualization, and specialized visualization techniques like treemaps, Sankey diagrams, and parallel coordinates. You can study these subjects further to deepen your understanding of advanced topics.
Data Visualization: The Art of Storytelling
Learn how to transform raw data into effective & interactive stories.
- 1 año de acceso completo
- 54 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito