Data Visualisation using Stata | Graphs You Should Know

Data Visualisation using Stata | Graphs You Should Know

From Univariate to Multivariate Data and much more.

Learn to create clear, impactful graphs in Stata in this two-day workshop. From univariate plots to advanced animations, gain practical skills to visualise and present your data effectively using Stata’s built-in and advanced graphing tools.

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£300.00
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2 Day
University of Westminster, London

Data visualisation is a critical step in the research workflow, yet it is often underutilised or misapplied. Based on the newly released Graphs Everyone Should Know and How to Create Them in Stata (Stata Press), this two-day course looks closer at the art of visualising data. By focusing on practical examples, participants will learn best practices for creating clear, insightful graphs, ranging from simple univariate distributions to advanced animated graphs and model-based visualisations.

 

By the end of the course, participants will understand how to:

  • Identify the most appropriate graph type for different data structures (univariate, bivariate, or multivariate).
  • Create a wide variety of plots (including histograms, box plots, scatterplots and heat plots) in Stata using both built-in and community-contributed commands.
  • Tailor essential graph options (labels, scales, overlays) without getting overwhelmed by every possible customisation.
  • Incorporate more advanced or novel graph types, such as animations, maps, coefficient plots, and 3D surface plots, into their workflow.
Course Structure

Day 1: Univariate & Bivariate

Topics Include:

Morning session (10am to 12.30pm)

  • Introduction to graphs in Stata
  • Graphs for Univariate
  • Hands on Exercise (Generating Univariate Plots in Stata)

 

Afternoon session (1.30pm to 4.30pm)

  • Bivariate Data Visualisation 
  • Lines of Fit, Nonparametric Smoothers, and Bivariate Charts.
  • Hands on Exercise (Generating Bivariate Plots in Stata)

 

Wrapping Up for the Day (1.30pm to 4.30pm)

Day 2: Multivariate & Specialised Visualisation

Topics Include:

Morning session (10am to 12.30pm)

  • Multivariate Data Visualisation
  • Contour and Trivariate Heat Plots
  • Bubbles Plots
  • 3D Scatterplots
  • Hands on Exercise (Generating correlation matrices, bubble plots & 3D visuals)

 

Afternoon session (1.30pm to 4.30pm)

  • Specialised Advanced Graphs 
  • Hands on Exercise (Advanced automated plotting)

 

Final Q&A with Open Discussion (4.30pm to 5.00pm)

Learning Outcomes

By the end of this two-day course, participants will be able to:

  • Identify the most effective graphical method to visualise univariate, bivariate, and multivariate data in Stata.
  • Confidently use Stata’s built-in and community-contributed graph commands, customising essential elements like labelling, colouring, and overlays.
  • Interpret and present complex results with clarity.
  • Automate repetitive tasks using do-files for reproducible research.

 

Who Should Attend
  • Researchers and graduate students looking to polish the graphical presentation of their data.
  • Analysts in government, finance, healthcare, or any field that requires high-quality visualisation.
  • Anyone seeking to expand beyond basic bar charts and scatterplots to gain deeper insights from their data.
Prerequisites
  • Basic knowledge of data analysis
  • Basic knowledge of Stata is an advantage but not essential.
  • No previous experience with specialised visualisation techniques is required.
  • But basic familiarity with is conducive with an enjoyable learning experience.

Day 1

Morning Session (10:00 – 12:30)

Introduction to Graphs in Stata

  • Overview of Stata’s graph capabilities.
  • Navigating essential Stata commands and syntax for plotting, including the command graph.
  • Understanding the Stata Graph Editor.

 

Graphs for Univariate Data

  • Histograms and kernel density plots: understanding distributions.
  • Box plots and violin plots: spotting outliers and distribution shape.
  • Dot plots and stem-and-leaf plots: alternative ways to depict univariate distributions.
  • Βar charts, pie charts, and radar charts: visualising categories and proportions.

 

Hands-On Exercise

  • Generate key univariate plots in Stata using training datasets.
  • Exploring custom bin widths, multiple overlays, and labelling best practices.
Afternoon Session (13:30 – 16:00)

 

Bivariate Data Visualization 

  • Scatterplots: analyse variables jointly, layer data points and group by categories.
  • Heat plots, hex-plots, and binned scatter plots: reveal dense clusters of data.
  • Line plots and area plots: visualise trends and ranges.
  • Lines of best fit, polynomial fits, and nonparametric smoothers: understand bivariate relationships and fits
  • Bivariate bar charts, stacked bar charts and table plots: graphs for analysing frequencies, percentages and categories.

 

Hands-On Exercise

  • Practice creating bivariate plots with various layering and labelling.
  • Experiment with overlays of fitted lines and confidence intervals.
Wrapping Up the First Day (16:00 - 16:30)
  • Key takeaways for univariate and bivariate visualisation.
  • Q&A session and practical troubleshooting.

 

Day 2

Morning Session (10:00 – 12:30)

Multivariate Data Visualization 

  • Matrix plots and trellis plots: exploring multiple variables simultaneously.
  • Contour plots and trivariate heat plots: using colour to visualise a third dimension
  • Bubble plots, Chernoff faces, and triplots: creative ways to show multiple dimensions.
  • 3D scatterplots and 3D surface plots: how to use basic 3D graphs in Stata.

 

Hands-On Exercises

  • Working with sample datasets to create correlation matrices, bubble plots, and 3D visuals.
  • Group discussion on identifying the best graph to convey research findings.

 

Afternoon Session (13:30 – 16:30)

Specialised and Advanced Graphs 

  • Animated graphs: creating gifs or videos for time-lapse or progression data.
  • Rainbow plots: colour-coded series to highlight patterns over time.
  • Plotting regression results: visualising model coefficients, interaction effects, and predictions.
  • Plotting equations: quick ways to visualise functional relationships.

 

Hands-On Exercises

  • Building a short do-file to automate advanced plots.

 

Final Q&A & Discussion (16:30 - 17:00)
  • Tips for exporting and embedding graphics in reports or presentations.
  • Course review and further resources.
  • Additional questions and advanced troubleshooting.

 

Course Timetable

Subject to minor changes
Day Morning Session Afternoon Session (Including Q&A)
Day One 10am-12:30pm (London time) 1:30pm-4:30pm (London time)
Day Two 10am-12:30pm (London time) 1:30pm-4:30pm (London time)

Terms

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Delegates are provided with temporary licences for the principal software package(s) used in the delivery of the course. It is essential that these temporary training licenses are installed on your computers prior to the start of the course.
  • Payment of course fees required prior to the course start date.

 

Cancellations

  • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
  • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
  • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of attendees is restricted. Please register early to guarantee your place.

Delivered By

Student Testimonials

Giovanni's delivery is fantastic; makes great connections between new and prior knowledge and focuses on the key strengths and limitations of the discussed methods. Excellent course design that builds on the Introductory Machine Learning course and knowledge acquired in the PhD Econometrics sequences of courses. This is all nicely supplemented by detailed Stata code with explanations and sample datasets. 

Georgi Boichev

A student on our

Excellent course and great explanations on ML techniques and applications from Giovanni ! I leanred so much including the coding and applications plus the fundamentals of ML.

Marco Delprado

A student on our

The 'Advanced Machine Learning (AML)' experience was excellent for trying to gain more experience in Statistics using links Python and STATA.  

I'm not a Statistician! However, Giovanni managed to link the 'Fundamentals of Machine Learning (FML) ' to 'Advanced Machine Learning' in his usual excellent way. When starting the AML, for me I am pleased that the FML was a tremendous help and allowed me to use my mathematical knowledge for Physics and Science. I'm looking forward to Giovanni's next course (using large datasets) and his book.

Linking my knowledge of mathematics (from Science and Engineering) to Statistics. I do hope it is leading towards becoming better at 'Medical Statistics' that require very large datasets...and a big thank you to Giovanni!

William Ware

A student on our

Very well organized, very useful and relevant content, looking forward to joining future events!

Hebatallah Nashaat

A student on our

As always great service and real good courses. In addition, thanks to Professor Cerulli for making himself understood in the best way.

David Pineda

A student on our

The delivery of this course was exceptionally well done. It really helped me to appreciate the concepts as well as the practical applications in Stata. If you are new to this topic, this will provide a good introduction to complex issues.

Very easy to communicate, all emails contained all the information necessary. I think that the course was very well structured and organized. The tutor provided a number of codes that were extremely helpful for understanding. Overall, very useful and easy to follow!

I highly appreciated Professor Giovannu Cerulli course. The classes notes are very clear   and well prepared with an extensive coverage of the course subjects. And they are simultanesouly quite objective by focusing on the most important contents. Professor Giovannu Cerulli lectures are very didatic which greately helps the easily assimilation of the   corespondent knowledge. Furthermore, the course materials are quite   comprehensive and they englobe not only the classes notes, but also the referenced papers as well as data and Stata programs to estimate the models in this software. All in all, I greatly recommend this   course, as it really amazingly speeds up the acquaintance of the underlying theory and appied aplication in a very short period of time.

I found the Stata Summer School 2021 very useful and interesting. The course was perfectly structured and organised, with a good progression during the week. The instructors presented the topics covered in an easy and understandable way. There were room for questions and answers when needed. Materials shared for the course were tidy and informative, and I am sure I will use them frequently. This course was arranged online, which in my opinion worked very well. I believe the course delivered as promised and according to information found online when I signed up for the course. Easy to purchase/sign up for the course. User friendly. Quick and timely response.

Very efficient in terms of communication and delivery. Provides a very comprehesnive applied knowledge of stata. I would definitely recommend others to buy from them.

I went UK University of Cambridge for a summer school with Timberlake, it was excellent.

It was a great course and I thoroughly enjoyed it. Many of my fellow participants were eager to share their ideas. I thought the course could help further many people in a similar stage to my career!