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Python for Data Visualization: The Complete Masterclass teaches you everything on the topic thoroughly from scratch so you can achieve a professional certificate for free to showcase your achievement in professional life. This Python for Data Visualization: The Complete Masterclass is a comprehensive, instructor-guided course, designed to provide a detailed understanding of the nature of the related sector and your key roles within it.
To become successful in your profession, you must have a specific set of skills to succeed in today’s competitive world. In this in-depth training course, you will develop the most in-demand skills to kickstart your career, as well as upgrade your existing knowledge & skills.
The training materials of this course are available online for you to learn at your own pace and fast-track your career with ease.
- Accredited by CPD
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with audio voiceover
- Self-paced learning and laptop, tablet, smartphone-friendly
- 24/7 Learning Assistance
- Discounts on bulk purchases
Sneak Peek
Who should take the course
Anyone with a knack for learning new skills can take this Python for Data Visualization: The Complete Masterclass. While this comprehensive training is popular for preparing people for job opportunities in the relevant fields, it also helps to advance your career for promotions.
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £5.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.
Accreditation
All of our courses, including this Python for Data Visualization: The Complete Masterclass, are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.
Course Curriculum
The detailed curriculum outline of our Python for Data Visualization: The Complete Masterclass is as follows:
- Installing the Anaconda Navigator
- Installing Matplotlib, seaborn & cufflinks
- Reading data from a csv file with pandas
- Explaining Matplotlib libraries apart
- Course Materials
- Changing the axis scales
- Label Styling
- Adding a legend
- Changing colors, linestyles, linewidth and markers
- Adding a grid to the chart
- Filling only a specific area
- Filling area on line plots and filling only specific area
- Changing fill color of different areas (negative vs positive for example)
- Changing edge color and adding shadow on the edge
- Adding legends, titles, location and rotating pie chart
- Histograms vs Bar charts (Part 1)
- Histograms vs Bar charts (Part 2)
- Changing edge colour of the histogram
- Changing the axis scale to log scale
- Adding median to histogram
- Advanced Histograms and Patches (Part 1)
- Advanced Histograms and Patches (Part 2)
- Overlaying bar plots on top of each other (Part 1)
- Overlaying bar plots on top of each other (Part 2)
- Creating Box and Whisker Plots
- Plotting a basic stack plot
- Plotting a stem plot
- Plotting a stack plot od data with constant total
- Plotting a basic scatter plot
- Changing the size of the dots
- Changing colors of markers
- Adding edges to dots
- Using the Python datetime module
- Connecting data points by line
- Converting string dates using the .to_datetime() pandas method
- Plotting live data using FuncAnimation in matplotlib
- Setting up the number of rows and columns
- Plotting multiple plots in one figure
- Getting separate figures
- Saving figures to your computer
- Introduction to seaborn
- Working on hue, style and size in seaborn
- Subplots using seaborn
- Line plots
- Cat plots
- Jointplot, pair plot and regression plot
- Controlling Plotted Figure Aesthetics
- Installation and Setup
- Line, Scatter, Bar, box and area plot
- 3D plots, spread plot and hist plot, bubble plot, and heatmap
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 | 00:06:00 | ||
| 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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- 1 year
- Intermediate
- Course Certificate
- 3 hours, 41 minutes Gift this course
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