Course Highlights
In a fast-paced job market, the ability to work with data and present it visually has become an invaluable skill. The Python for Data Visualization course offers an extensive learning journey, introducing you to the concepts and tools that will enable you to turn raw data into powerful insights. The course begins by familiarising you with the core Python libraries, such as Matplotlib and Seaborn, before progressing to interactive visualisations using Plotly. As industries across the globe rely on data to guide decision-making, the demand for professionals proficient in Python and data visualisation continues to rise.
Take the example of a financial analyst at a global bank. By leveraging the skills learned in this course, the analyst can use Python to generate insightful visualisations that not only demonstrate trends but also predict future financial movements. This ability to interpret and communicate data is essential, as it influences key business decisions and strategy. As companies increasingly turn to data-driven approaches, those with expertise in Data Visualisation will find themselves in high demand, with opportunities in diverse sectors such as finance, marketing, and healthcare.
Completing this course empowers you to build a career centred around Python and Data Visualisation, allowing you to stand out in a competitive job market. With the skills to present complex data effectively, you’ll help organisations uncover the stories hidden within their numbers.
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Course Curriculum
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Installing the Anaconda Navigator
00:07:00
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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 heatmap
00:07:00
14-Day Money-Back Guarantee
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Duration:3 hours, 44 minutes
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Access:1 Year
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Units:49


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