Overview
Unlock the potential of Python for creating impactful data visualizations with “Python for Data Visualization: The Complete Masterclass.” This course guides you from the basics of data visualization to mastering advanced techniques, using Python’s most powerful libraries, including Matplotlib, Seaborn, Plotly, and more. You’ll learn to transform raw data into compelling visual stories, enhancing both your analytical and storytelling skills. Through hands-on projects, you’ll explore various visualization types like line charts, bar charts, scatter plots, heatmaps, and interactive dashboards. Not only will you grasp the technical elements of Python-based visualizations, but you’ll also develop an eye for design principles, color theory, and best practices to make your visuals both insightful and aesthetically pleasing. By the end of this course, you’ll be equipped with the knowledge to confidently create visuals that make data-driven insights accessible and engaging, whether for presentations, reports, or web applications.
Learning Outcomes
By the end of this course, you will be able to:
- Understand the fundamentals of Python’s visualization libraries.
- Create static and interactive visualizations using Matplotlib, Seaborn, and Plotly.
- Apply data visualization techniques to real-world datasets.
- Master visual aesthetics like color, labeling, and layouts.
- Develop interactive dashboards for enhanced data exploration.
- Use Python scripts to automate and customize visual outputs.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
This course is designed for aspiring data scientists, data analysts, business intelligence professionals, and anyone interested in making data insights visually compelling. It’s ideal for individuals with some Python knowledge who wish to add data visualization skills to their toolkit.
Requirements
Basic knowledge of Python programming is recommended but not mandatory, as introductory Python resources are provided. A computer with an internet connection and Python installed is required. Familiarity with basic data analysis concepts is helpful but not essential for this course.
Career Path
- Data Analyst
- Data Scientist
- Business Intelligence Developer
- Python Developer specializing in Data Visualization
- Analytics Consultant
Course Curriculum
- 9 sections
- 49 lectures
- 3 hours, 44 minutes total length
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Installing the Anaconda Navigator 00:07:00 -
Installing Matplotlib, seaborn & cufflinks 00:03:00
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Reading data from a csv file with pandas 00:03:00
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Explaining Matplotlib libraries apart 00:07:00
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Changing the axis scales 00:06:00
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Label Styling 00:04:00
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Adding a legend 00:04:00
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Changing colors, linestyles, linewidth and markers 00:09:00
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Adding a grid to the chart 00:04:00
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Filling only a specific area 00:07:00
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Filling area on line plots and filling only specific area 00:04:00
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Changing fill color of different areas (negative vs positive for example) 00:03:00
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Changing edge color and adding shadow on the edge 00:04:00
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Adding legends, titles, location and rotating pie chart 00:06:00
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Histograms vs Bar charts (Part 1) 00:03:00
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Histograms vs Bar charts (Part 2) 00:02:00
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Changing edge colour of the histogram 00:03:00
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Changing the axis scale to log scale 00:07:00
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Adding median to histogram 00:04:00
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Advanced Histograms and Patches (Part 1) 00:04:00
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Advanced Histograms and Patches (Part 2) 00:05:00
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Overlaying bar plots on top of each other (Part 1) 00:04:00
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Overlaying bar plots on top of each other (Part 2) 00:01:00
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Creating Box and Whisker Plots 00:11:00
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Plotting a basic stack plot 00:13:00
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Plotting a stem plot 00:05:00
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Plotting a stack plot od data with constant total 00:04:00
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Plotting a basic scatter plot 00:06:00
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Changing the size of the dots 00:06:00
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Changing colors of markers 00:05:00
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Adding edges to dots 00:04:00
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Using the Python datetime module 00:03:00
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Connecting data points by line 00:04:00
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Converting string dates using the .to_datetime() pandas method 00:05:00
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Plotting live data using FuncAnimation in matplotlib 00:04:00
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Setting up the number of rows and columns 00:04:00
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Plotting multiple plots in one figure 00:02:00
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Getting separate figures 00:03:00
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Saving figures to your computer 00:03:00
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Introduction to seaborn 00:02:00
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Working on hue, style and size in seaborn 00:05:00
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Subplots using seaborn 00:05:00
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Line plots 00:02:00
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Cat plots 00:03:00
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Jointplot, pair plot and regression plot 00:02:00
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Controlling Plotted Figure Aesthetics 00:03:00
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Installation and Setup 00:02:00
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Line, Scatter, Bar, box and area plot 00:07:00
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3D plots, spread plot and hist plot, bubble plot, and heatmap0 00:07:00
Frequently Bought Together
Big Data Analytics with PySpark Tableau Desktop and MongoDB
4.7
(1 student)
4.7 course rating - 18 reviews
£12.99
£419
This course includes:
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Duration:3 hours, 44 minutes
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Access:1 year access
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Units:49
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Level:Intermediate
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CPD Points:10
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Certificate:Yes
