Data Visualization with Python

TRAINING COURSE

Details Audience and Pre-Requisites Course Outline / Curriculum Schedule Dates and Booking Enquiry

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

Delivery Method Duration
Classroom
Instructor-led classroom-based training. Classes are scheduled at various conference centres in the Sandton area or your premises. Stationary and printed manuals or online resources are included. Refreshments, including 2 tea breaks and a cooked meal for lunch are provided. Training is between 9 am to 4 pm.
Classroom
1 Days Get a Quote
Live Virtual Training
This course is delivered live via Microsoft Teams. You will be able to see and hear the instructor, view their whiteboard, and ask questions or communicate in the chat. Attend from the comfort of your own home or private office.
Live Virtual Training
1 Days Get a Quote

Discounts Available

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

Brochure:

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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

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