Alex Aklson
PhD., Data Scientist
COURSE
Visualizing Data with Python
Visualizing Data with Python Highlights
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Starts on 15 June 2021
Enrollment closes on |
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Fee US$99 - US$199 |
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Starts on 15 June 2021
Enrollment closes on |
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Fee US$99 - US$199 |
About Visualizing Data with Python Course
"A picture is worth a thousand words." We are all familiar with this expression. It especially applies when trying to explain the insights obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way.
In this course, you will learn how to leverage a software tool to visualize data that will also enable you to extract information, better understand the data, and make more effective decisions.
When you sign up for this course, you get free access to IBM Watson Studio. In Watson Studio, you’ll be able to start creating your own data science projects and collaborating with other data scientists. Start now and take advantage of everything this platform has to offer!
What You Will Learn
- How to present data using some of the data visualization libraries in Python, including Matplotlib, Seaborn, Folium, Plotly and Dash
- How to use basic visualization tools, including area plots, histograms, and bar charts
- How to use specialized visualization tools, including pie charts, box plots, scatter plots, and bubble plots
- How to use advanced visualization tools, including waffle charts, word clouds, and Seaborn and regression plots
- How to create maps and visualize geospatial data
- How t0 Create Dashboards with Plotly and Dash
Course Syllabus
Module 1 -Introduction to Visualization Tools
- Introduction to Data Visualization
- Introduction to Matplotlib
- Basic Plotting with Matplotlib
- Dataset on Immigration to Canada
- Line Plots
Module 2 -Basic Visualization Tools
- Area Plots
- Histograms
- Bar Charts
Module 3 -Specialized Visualization Tools
- Pie Charts
- Box Plots
- Scatter Plots
- Bubble Plots
Module 4 -Advanced Visualization Tools
- Waffle Charts
- Word Clouds
- Seaborn and Regression Plots
Module 5 -Creating Maps and Visualizing Geospatial Data
- Introduction to Folium
- Maps with Markers
- Choropleth Maps
Module 6 -Creating Dashboards with Plotly and Dash
- Dashboarding Overview
- Introduction to Plotly
- Introduction to Dash
- Make Dashboards Interactive
Prerequisites
Meet your instructors
Course Outline
Welcome! (3:01)
General Information
Learning Objectives
Syllabus
Grading Scheme
Change Log
Copyrights and Trademarks
Learning Objectives
Introduction to Data Visualization (4:36)
Introduction to Matplotlib (6:26)
Basic Plotting with Matplotlib (4:39)
Dataset on Immigration to Canada (2:43)
Line Plots (3:41)
Introduction to Matplotlib and Line Plots
Review Questions
Learning Objectives
Area Plots (4:45)
Histograms (4:58)
Bar Charts (3:30)
Area Plots, Histograms, and Bar Plots
Review Questions
Learning Objectives
Pie Charts (4:14)
Box Plots (4:11)
Scatter Plots (4:17)
Pie Charts, Box Plots, Scatter Plots, and Bubble Plots
Review Questions
Learning Objectives
Waffle Charts (1:28)
Word Clouds (1:28)
Seaborn and Regression Plots (2:26)
Waffle Charts, Word Clouds, and Regression Plots
Review Questions
Learning Objectives
Introduction to Folium (2:35)
Maps with Markers (2:21)
Choropleth Maps (4:21)
Generating Maps in Python
Review Questions
Learning Objectives
Dashboarding Overview (4:33)
Additional Resources for Dashboards
Introduction to Plotly (3:46)
Additional Resources for Plotly
Plotly basics: scatter, line, bar, bubble, histogram, pie, sunburst
Introduction to Dash (4:30)
Additional Resources for Dash
Dash basics: HTML and core components
Dash basics using Jupyterlab
Make Dashboards Interactive (4:39)
Additional Resources for Interactive Dashboards
Add interactivity: user input and callbacks
Flight Delay Time Statistics Dashboard
Review Questions
Introduction
Guidelines for Submission
Final Assignment - Peer Review Questions
Instructions
Final Exam
Download your Certificate
Earn your certificate
Once you have completed this course, you will earn your certificate.
Course details
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Get a Free ConsultationFAQs
Python is a widely used programming language for data visualization. It has a useful set of libraries for creating informative and helpful bar charts, scatterplots, line charts, and geographical maps, among other things. These packages make it simple to generate visually appealing displays of data analysis results. Matplotlib, Plotly, Seaborn, GGplot, and Geoplotlib are just a few examples of these libraries, although there are many more.
No, is the short answer. Though knowing Python is essential for pursuing a career in AI or data science, you will also require additional talents.
Data Visualization with Python is a self-paced online course. As a result, you will require internet connectivity in order to use the course materials. When you register for this course, you will immediately have access to the course materials through the course link on your dashboard.
There is no defined schedule for live sessions or webinars in self-paced courses. You can work as swiftly or as slowly as you wish as long as you complete the modules and the course before the deadline.
In the realm of data science, this is a contentious topic. Both languages are valuable for data science, according to IBM, and each has its own benefits and disadvantages. Both languages are widely used in data science because they are suitable for a wide range of activities. These can include everything from data manipulation to big data analysis. Their disparities can thus be best understood by looking at how each one came to be. Python is a general-purpose computer language that was first developed in 1989. R, on the other hand, grew out of statistical analysis and is hence incredibly strong but more difficult to use.
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