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 Praktische Übungen — nothing to install
- 9 Interaktive Lektionen Und 39 topics mapped to the official exam objectives
- 90 Testfragen zur Praxis
Intermediate Selbstgesteuert · 1 Jahr Zugang
54 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
9Interaktive Lektionen
39Topics
54LiveLab
90Testfragen zur Praxis
78Videos
36Karteikarten
36Glossar der Begriffe
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
Try Free →
Keine Kreditkarte benötigt
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 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
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54 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
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90 Übungsfragen Bewertungstests mit ausführlichen Antwortbegründungen
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1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
9 Interaktive Lektionen · 39 topics01 Introduction 2 topics +
- About
- About the Course
02 Introduction to Visualization with Python – Basic and Customized Plotting 5 topics · 12 LiveLab +
- Introduction
- Handling Data with pandas DataFrame
- Plotting with pandas and seaborn
- Tweaking Plot Parameters
- Summary
12 LiveLab in this lesson — see the labs panel →
03 Static Visualization – Global Patterns and Summary Statistics 4 topics · 9 LiveLab +
- Introduction
- Creating Plots that Present Global Patterns in Data
- Creating Plots That Present Summary Statistics of Your Data
- Summary
9 LiveLab in this lesson — see the labs panel →
04 From Static to Interactive Visualization 5 topics · 5 LiveLab +
- Introduction
- Static versus Interactive Visualization
- Applications of Interactive Data Visualizations
- Getting Started with Interactive Data Visualizations
- Summary
5 LiveLab in this lesson — see the labs panel →
05 Interactive Visualization of Data across Strata 4 topics · 10 LiveLab +
- Introduction
- Interactive Scatter Plots
- Other Interactive Plots in altair
- Summary
10 LiveLab in this lesson — see the labs panel →
06 Interactive Visualization of Data across Time 10 topics · 7 LiveLab +
- 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 LiveLab in this lesson — see the labs panel →
07 Interactive Visualization of Geographical Data 4 topics · 7 LiveLab +
- Introduction
- Choropleth Maps
- Plots on Geographical Maps
- Summary
7 LiveLab in this lesson — see the labs panel →
08 Avoiding Common Pitfalls to Create Interactive Visualizations 5 topics · 4 LiveLab +
- Introduction
- Data Formatting and Interpretation
- Data Visualization
- Cheat Sheet for the Visualization Process
- Summary
4 LiveLab in this lesson — see the labs panel →
09 Appendix A: Data Structures, Strings, and Numpy +
Praktische Übungen Our edge
54 LiveLabs- Creating a User-defined Function
- Anwenden der Funktion ceil() auf eine DataFrame-Spalte
- Adding a Column to a DataFrame
- Anwenden der Funktion „beschreiben()“.
- 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
- Erstellen eines nach einem bestimmten Merkmal gruppierten Balkendiagramms
- Zeichnen eines Histogramms
- 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
- Erstellen eines statischen Konturdiagramms
- 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
Labore laufen direkt im Browser — nichts zu installieren.
03 / FAQS
Fragen, bevor Sie beginnen
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 Jahr voller Zugang
- 54 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt