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2024 Python Data Analysis & Visualization Masterclass Pandas, Matplotlib, Seaborn, & More! Analyze Dozens of Datasets & Create Stunning Visualizations Highest rated 4.7 (2,224 ratings) 18,964 students Created by Colt Steele Last updated 4/2023 English English [Auto], Simplified Chinese [Auto] 2024 Python Data Analysis & Visualization Masterclass
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Applied Data Visualization. In this final section, apply your data visualization skills in Python on real world tasks. Learn to build interactive web applications with Dash, and interactive web maps using Folium. Then, explore the creative side of data visualization by drawing the Mandelbrot set, a famous fractal, using Matplotlib and Pillow.
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One of the most powerful programming languages that are used for Data science is Python, which is an easy, simple and very powerful language with many libraries and packages that facilitate working on complex and different types of data. This course will cover: Python tools for Data Analysis
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In this comprehensive course, we'll teach you everything you need to know about data analysis and data science using Python. You'll learn how to use Python libraries like Numpy and Pandas to manipulate and process data, and how to visualize your findings for maximum impact.
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About Outcomes Modules Recommendations Testimonials Reviews What you'll learn Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble
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By the end of this course you will know how to: Use Anaconda, the worlds leading data science platform, to setup Python and manage libraries. Install and setup the free to use Atom Text Editor. Create Virtual Environments. Clone a GitHub Repository directly into Atom. Create new code branches in GitHub and Atom. Install the Pandas library.
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In this article, we'll embark on a journey to master data visualization in Python, exploring key libraries and techniques to transform raw data into insightful visuals. To enhance our learning, we'll utilize a basic example dataset, providing hands-on code examples using Matplotlib, Seaborn, and Plotly.