Python For Data Visualization: The Complete Masterclass

Transforming Data into Insights: A Comprehensive Guide to Python-based Data Visualization

What you'll learn

Understanding the importance of data visualization, its role in data analysis, and the principles of effective visualization design.

Exploring popular Python libraries such as Matplotlib, and Seaborn, and learning how to leverage their functionalities to create a variety of visualizations.

Understanding how to customize and enhance visualizations by adjusting colors, labels, titles, legends, and other visual elements.

Understanding the principles of effective data storytelling and best practices for designing clear, impactful, and informative data visualizations.

Requirements

The ability to do simple math

No programming experience needed

No prior data science knowledge required

Readiness, flexibility, and passion for learning

Description

Use Python to build spectacular data visualisations and fascinate your audience. Join our transformative masterclass to master Python for data visualisation.Visual storytelling is crucial in a data-driven environment. This comprehensive Python course will teach you how to turn raw data into stunning visualisations.You'll learn how to maximise Matplotlib, Seaborn, and Plotly via immersive hands-on activities and real-world examples. Python opens us a universe of data visualisation possibilities, from simple charts to heatmaps, time series visualisation, and geospatial mapping.As you master every component of your visualisations, you may customise them to create stunning masterpieces that fascinate and engage your audience. Interactive dashboards will let people explore data and discover hidden insights.This masterclass will teach data analysts, corporate leaders, researchers, and aspiring data enthusiasts how to use the most popular data visualisation programming language to have a lasting effect. Practical projects, real-world case studies, and industry experts will give you the confidence and skills to tackle any Python data visualisation challenge.Avoid boring presentations that don't tell your data's story. Join us to use Python to visualise difficult data in beautiful, persuasive ways. Become a Python data visualisation expert and boost your career. Enrol today and unleash your creativity with Python.

Overview

Section 1: Setup & Installation

Lecture 1 Installing the Anaconda Navigator

Lecture 2 Installing Matplotlib, seaborn & cufflinks

Lecture 3 Reading data from a csv file with pandas

Lecture 4 Explaining Matplotlib libraries

Section 2: Plotting Line Plots with matplotlib

Lecture 5 Changing the axis scales

Lecture 6 Label Styling

Lecture 7 Adding a legend

Lecture 8 Adding a grid to the chart

Lecture 9 Filling only a specific area

Lecture 10 Filling area on line plots and filling only specific area

Lecture 11 Changing fill color of different areas (negative vs positive for example)

Section 3: Plotting Histograms & Bar Charts with matplotlib

Lecture 12 Changing edge color and adding shadow on the edge

Lecture 13 Adding legends, titles, location and rotating pie chart

Lecture 14 Histograms vs Bar charts (Part 1)

Lecture 15 Histograms vs Bar charts (Part 2)

Lecture 16 Changing edge color of the histogram

Lecture 17 Changing the axis scale to log scale

Lecture 18 Adding median to histogram

Lecture 19 Advanced Histograms and Patches (Part 1)

Lecture 20 Advanced Histograms and Patches (Part 2)

Lecture 21 Overlaying bar plots on top of each other (Part 1)

Lecture 22 Overlaying bar plots on top of each other (Part 2)

Lecture 23 Creating Box and Whisker Plots

Section 4: Plotting Stack Plots & Stem Plots

Lecture 24 Plotting a basic stack plot

Lecture 25 Plotting a stem plot

Lecture 26 Plotting a stack plot od data with constant total

Section 5: Plotting Scatter Plots with matplotlib

Lecture 27 Plotting a basic scatter plot

Lecture 28 Changing the size of the dots

Lecture 29 Changing colors of markers

Lecture 30 Adding edges to dots

Section 6: Time Series Data Visualization with matplotlib

Lecture 31 Using the Python datetime module

Lecture 32 Connecting data points by line

Lecture 33 Converting string dates using the .to_datetime() pandas method

Lecture 34 Plotting live data using FuncAnimation in matplotlib

Section 7: Creating multiple subplots

Lecture 35 Setting up the number of rows and columns

Lecture 36 Plotting multiple plots in one figure

Lecture 37 Getting separate figures

Lecture 38 Saving figures to your computer

Section 8: Plotting charts using seaborn

Lecture 39 Introduction to seaborn

Lecture 40 Working on hue, style and size in seaborn

Lecture 41 Subplots using seaborn

Lecture 42 Line plots

Lecture 43 Cat plots

Lecture 44 Jointplot, pair plot and regression plot

Lecture 45 Controlling Plotted Figure Aesthetics

Section 9: Plotly and Cufflinks

Lecture 46 Installation and Setup

Lecture 47 Line, Scatter, Bar, box and area plot

Lecture 48 3D plots, spread plot and hist plot, bubble plot, and heatmap

Individuals who are new to data visualization and have little or no prior experience with Python or programming.,Professionals working with data who want to enhance their data visualization skills to effectively communicate insights and findings.,Individuals with programming experience who wish to expand their knowledge and incorporate data visualization into their skill set.,Business Professionals: Managers, executives, and professionals from non-technical backgrounds who want to gain a practical understanding of data visualization to make informed decisions and effectively communicate data-driven insights.,Students and Researchers: Those studying data science, computer science, statistics, or related fields who want to strengthen their proficiency in visualizing and presenting data using Python.,Anyone Interested in Data Visualization: Enthusiasts, hobbyists, or curious learners who have an interest in data visualization and want to explore the capabilities of Python for creating impactful visualizations.