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Data Visualization using MatPlotLib & Seaborn
Rating: 3.0 out of 5(1 rating)
6 students

Data Visualization using MatPlotLib & Seaborn

Learn why, what, how about data visualization in simple and easy way
Created byDATAhill Solutions Srinivas Reddy
Last updated 1/2020
English

What you'll learn

  • Matplotlib Introduction
  • PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot
  • Multiple Plots in a Graph
  • Seaborn Plotting Functions
  • Plotting with Categorical Data
  • Multi-Plot Grids
  • Plot Aesthetics
  • What is Data Science
  • What is Machine Learning
  • Data Visualization using Pandas
  • Data Analytics

Course content

1 section • 20 lectures • 4h 23m total length

  • Introduction1:22
  • 1. Matplotlib Introduction1:16

    Learn Matplotlib basics for data visualization and Seaborn context, covering graph functions, data inputs, 3d visuals, and limitations.

  • 1. Matplotlib Introduction31:23
  • 2. PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot1:02

    Harness pyplot to create bar, pie, histogram, scatter, and 3d plots that turn data into clear graphical representations for analysis and reporting.

  • 2. PyPlot, Bar, Pie, Histogram, Scatter & 3D Plot32:58
  • 3. Multiple Plots in a Graph0:55
  • 3. Multiple Plots in a Graph14:38
  • 4. Seaborn Plotting Functions1:43

    Explore Seaborn plotting functions and how to generate graphs from data using the main function.

  • 4. Seaborn Plotting Functions30:49
  • 5. Plotting with Categorical Data0:28
  • 5. Plotting with Categorical Data15:40
  • 6. Multi-Plot Grids0:56
  • 6. Multi-Plot Grids33:55
  • 7. Plot Aesthetics0:53
  • 7. Plot Aesthetics17:38
  • 8. What is Data Science30:42

    Explore what data science is, from statistics and machine learning to data analysis and visualization, and learn the data lifecycle from collection to reporting.

  • 9. What is Machine Learning25:06
  • 10. Data Visualization using Pandas0:49
  • 10. Data Visualization using Pandas19:33

    Explore data visualization using a used cars dataset to build and interpret box plots, scatter plots, and distribution insights, including mean and median comparisons and left-right skew.

  • Summary1:22

Requirements

  • Just some high school mathematics level.
  • No Programming skills needed

Description

Data is the currency of today, and leveraging it correctly at the right time and for the right reasons opens up possibilities beyond imagination.

Data visualization is a vast field with many sub-parts, each a subject in itself. Our course aims to provide a clear picture of what you need to know and what employers will expect from you in a visualization project.

User Experience (UX) in data visualization is crucial in modern times to meet user expectations. This course will highlight the benefits of good UX and teach you how to implement it effectively.

This course is structured to cover all key aspects of data visualization in the simplest and clearest fashion, enabling you to embark on your journey into the world of data visualization.

Hands-On Projects and Case Studies: This course includes hands-on projects and real-world case studies to provide practical experience. By working on these projects, you'll gain the skills needed to tackle complex data visualization challenges and impress potential employers.

Advanced Tools and Technologies: Learn to use advanced data visualization tools and technologies such as Tableau, Power BI, and D3.js. This course covers a wide range of software, ensuring you're well-versed in the tools that are in high demand in the industry.

Storytelling with Data: Discover the art of storytelling with data. This course emphasizes the importance of creating compelling narratives that make your data insights more accessible and impactful to your audience.

Interactive Dashboards and Visualizations: Master the creation of interactive dashboards and visualizations that allow users to explore data dynamically. You'll learn techniques to build responsive and user-friendly visualizations that provide deeper insights and improve decision-making.

Who this course is for:

  • Beginner Python developers curious about Data Science
  • Beginner to advanced level

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