Data Visualization with Python Course

Data Visualization with Python

Details

To interpret your data and draw insights, you need to be able to visualise it. Python, one of the most popular and powerful coding languages around, has impressive libraries and resources for displaying your data. You'll be able to take your data and use it to tell insightful stories with compelling visualisations to convince stakeholders and build trust.

In this course you will learn to:

Create data visualisations
Use various graph types
Apply skills to real world scenarios
Improve data accessibility and understandability
Create robust summaries

Delivery Methods

Leading Training is focusing on providing virtual training courses for the foreseeable future and will only consider in-person and classroom training on request, with a required minimum group size of four delegates. We remain committed to offering training that is fast, focused and effective.

Delivery Method	Duration	Price (excl. VAT)
Live Virtual Training	1 days	GBP 595.00	Get a Quote

Discounts Available

Save up to 10% by booking and paying 10 business days before the course.

Brochure:

Information may change without notice.

Audience

Data scientists
Programmers
Business analysts
Engineers
Scientists

Pre-Requisites

Leading Training's Python Course or equivalent knowledge

Course Outline / Curriculum

Visualising Data

Instantiating defaults
Changing colors and line widths
Setting limits
Setting ticks
Setting tick labels
Moving spines
Adding a legend
Annotate some points
Putting it all together
Figures, Subplots, Axes and Ticks

Graph Types

Scatter Plots
Bar Plots
Contour Plots
Imshow
Pie Charts
Quiver Plots
Multi Plots
Polar Axis
Histogram
3D Plots

Data visualization in practice

Case study: new insights on poverty
Hans Rosling’s quiz
Scatterplots
Faceting
Fixed scales for better comparisons
Time series plots
Labels instead of legends
Data transformations
Log transformation
Which base?
Transform the values or the scale?
Visualizing multimodal distributions
Comparing multiple distributions with boxplots and ridge plots
Boxplots
Ridge plots
Example: 1970 versus 2010 income distributions
Accessing computed variables
Weighted densities
The ecological fallacy and importance of showing the data
Logistic

Data visualization principles

Encoding data using visual cues
Know when to include
Do not distort quantities
Order categories by a meaningful value
Show the data
Ease comparisons
Use common axes
Align plots vertically to see horizontal changes and horizontally to see
vertical changes
Consider transformations
Visual cues to be compared should be adjacent
Use color
Think of the color blind
Plots for two variables
Slope charts
Bland-Altman plot
Encoding a third variable
Avoid pseudo-three-dimensional plots
Avoid too many significant digits
Know your audience
Exercises
Case study: vaccines and infectious diseases
Exercises

Robust summaries

Outliers
Median
The inter quartile range (IQR)
Tukey’s definition of an outlier
Median absolute deviation
Exercises
Case study: self-reported student heights

Schedule Dates and Booking

There are currently no scheduled dates.

Please note that this course needs a minimum of 4 delegates to schedule a course. You can choose to be added to the waiting list by clicking the button below and we will contact you when we have enough delegates interested. Should we not get enough delegates, we will refund or credit your paid booking.

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Should you need this course urgently, the following options are available:

Pay for 4 delegates (whether you have them or not) and we will schedule the course as soon as possible.

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