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Python for Data Visualization: The Complete Masterclass

Overview Python for Data Visualization: The Complete Masterclass provides a comprehensive guide to mastering data visualisation with Python. The course …

Python for Data Visualization: The Complete Masterclass

Python for Data Visualization: The Complete Masterclass

Original price was: $417.25.Current price is: $35.30.

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Python for Data Visualization: The Complete Masterclass Overview

Python for Data Visualization: The Complete Masterclass provides a comprehensive guide to mastering data visualisation with Python. The course covers essential libraries, including Matplotlib, Seaborn, Plotly, and Cufflinks. You will learn to create various visualisations, from simple line plots to complex time series charts. Through hands-on projects, you’ll develop the skills needed to effectively present data and make informed decisions based on visual insights.

Learning Outcomes

  • Set up and install Python libraries for data visualisation.
  • Create line plots using Matplotlib.
  • Plot histograms and bar charts with Matplotlib.
  • Generate stack plots and stem plots.
  • Develop scatter plots with Matplotlib.
  • Visualise time series data with Matplotlib.
  • Create multiple subplots for detailed data analysis.
  • Use Seaborn for advanced data visualisation.
  • Implement interactive plots with Plotly and Cufflinks.
  • Apply data visualisation techniques to real-world datasets.

Who Is This Course For

This course is ideal for anyone interested in data visualisation using Python. It suits data analysts, data scientists, and professionals in related fields who want to enhance their ability to present data visually. Beginners with basic Python knowledge will also find this course valuable for developing their data visualisation skills.

Eligibility Requirements

To enrol in Python for Data Visualization: The Complete Masterclass, participants should have a solid grasp of relevant subjects or disciplines and a strong interest in the field. This course is designed for recent graduates, professionals looking to enhance their skills, and individuals aiming to transition into the industry.

Entry Requirements

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path

Completing this course opens various career opportunities in data analysis, data science, and business intelligence. With expertise in Python data visualisation, you can pursue roles in industries such as finance, healthcare, marketing, and technology. The skills acquired in this course are essential for data-driven decision-making and can significantly enhance your career prospects.

 

Course Curriculum

Setup & Installation
Installing the Anaconda Navigator 00:07:00
Installing Matplotlib, seaborn & cufflinks 00:03:00
Reading data from a csv file with pandas 00:03:00
Explaining Matplotlib libraries apart 00:07:00
Plotting Line Plots with matplotlib
Changing the axis scales 00:06:00
Label Styling 00:04:00
Adding a legend 00:04:00
Changing colors, linestyles, linewidth and markers 00:09:00
Adding a grid to the chart 00:04:00
Filling only a specific area 00:07:00
Filling area on line plots and filling only specific area 00:04:00
Changing fill color of different areas (negative vs positive for example) 00:03:00
Plotting Histograms & Bar Charts with matplotlib
Changing edge color and adding shadow on the edge 00:04:00
Adding legends, titles, location and rotating pie chart 00:06:00
Histograms vs Bar charts (Part 1) 00:03:00
Histograms vs Bar charts (Part 2) 00:02:00
Changing edge colour of the histogram 00:03:00
Changing the axis scale to log scale 00:07:00
Adding median to histogram 00:04:00
Advanced Histograms and Patches (Part 1) 00:04:00
Advanced Histograms and Patches (Part 2) 00:05:00
Overlaying bar plots on top of each other (Part 1) 00:04:00
Overlaying bar plots on top of each other (Part 2) 00:01:00
Creating Box and Whisker Plots 00:11:00
Plotting Stack Plots & Stem Plots
Plotting a basic stack plot 00:13:00
Plotting a stem plot 00:05:00
Plotting a stack plot od data with constant total 00:04:00
Plotting Scatter Plots with matplotlib
Plotting a basic scatter plot 00:06:00
Changing the size of the dots 00:06:00
Changing colors of markers 00:05:00
Adding edges to dots 00:04:00
Time Series Data Visualization with matplotlib
Using the Python datetime module 00:03:00
Connecting data points by line 00:04:00
Converting string dates using the .to_datetime() pandas method 00:05:00
Plotting live data using FuncAnimation in matplotlib 00:04:00
Creating multiple subplots
Setting up the number of rows and columns 00:04:00
Plotting multiple plots in one figure 00:02:00
Getting separate figures 00:03:00
Saving figures to your computer 00:03:00
Plotting charts using seaborn
Introduction to seaborn 00:02:00
Working on hue, style and size in seaborn 00:05:00
Subplots using seaborn 00:05:00
Line plots 00:02:00
Cat plots 00:03:00
Jointplot, pair plot and regression plot 00:02:00
Controlling Plotted Figure Aesthetics 00:03:00
Plotly and Cufflinks
Installation and Setup 00:02:00
Line, Scatter, Bar, box and area plot 00:07:00
3D plots, spread plot and hist plot, bubble plot, and heatmap 00:07:00

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