Module 1: Foundations of Data Visualization and Design Principles

• The history of data visualization and the work of Tufte, Cleveland, and Few
• Understanding the Grammar of Graphics (e.g., layers, aesthetics, geoms, and facets)
• Cognitive load and the principle of minimizing data-ink ratio
• Choosing effective visual encodings (position, length, angle, color)
• Practical session: Setting up the Visualization Environment (R/Python) and creating core charts using the Grammar of Graphics

Module 2: Visualizing Univariate and Comparative Data

• Detailed creation and refinement of Histograms and Density Plots for distribution analysis
• Box plots, violin plots, and ridgeline plots for comparing distributions across groups
• Best practices for Bar Charts, ensuring zero baseline and proper sorting
• Dealing with overplotting in scatter plots using alpha, jitter, and binning
• Practical session: Comparing experimental group distributions using advanced box and violin plots with jittered data points

Module 3: Effective Visualization of Relationships and Distributions

• Using scatter plots and marginal plots to visualize bivariate correlation
• Creating correlation heatmaps and matrices for multivariate relationship summary
• Designing parallel coordinates and radial plots for high-dimensional feature comparison
• Utilizing contour plots and hexagonal binning to display density in 2D space
• Practical session: Generating a multivariate correlation matrix heatmap with hierarchical clustering for research variables

Module 4: Advanced Time-Series and Flow Visualization

• Best practices for line charts: aspect ratio, aggregation, and handling multiple series
• Stacked area charts vs. small multiples (facets) for time-series comparisons
• Flow diagrams and Sankey charts for visualizing movement and changes in state
• Waterfall charts for showing cumulative effects over a period
• Practical session: Creating small multiples of line charts to track key metrics over time for different study cohorts

Module 5: Geospatial Data and Static Mapping

• Introduction to coordinate systems and geospatial data formats in R/Python
• Creating Choropleth Maps to visualize data aggregated by administrative boundaries
• Using point maps, bubbles, and heatmaps for specific location-based data
• Proper use of map projections and scale indicators in research figures
• Practical session: Generating a Choropleth map to visualize research data (e.g., disease prevalence or survey results) across defined regions

Module 6: Interactive Visualization and Dashboard Creation

• Introduction to interactive visualization libraries (e.g., Plotly, Leaflet, Shiny/Dash)
• Designing user interactions: zooming, filtering, tooltips, and highlighting
• Structuring a research dashboard layout for optimal information consumption
• Best practices for mobile responsiveness and performance of interactive plots
• Practical session: Building a simple interactive dashboard to allow users to filter and explore raw data subsets

Module 7: Visualization for Statistical Modeling and Machine Learning

• Creating diagnostic plots for linear and generalized linear models (e.g., residual plots)
• Visualizing model uncertainty and prediction intervals for forecasts
• Interpreting and plotting the results of cluster analysis (dendrograms, scatter plots)
• Techniques for visualizing feature importance and model interpretability (e.g., Partial Dependence Plots)
• Practical session: Visualizing the residuals and model fit statistics for a simple linear regression analysis

Module 8: Principles of Visual Perception and Cognitive Load

• Understanding the Gestalt principles and how humans group visual elements
• The appropriate use of color: sequential, diverging, and categorical palettes
• Handling accessibility: color blindness considerations and legible typography
• Minimizing chart clutter: removing non-data ink and maximizing clarity
• Practical session: Redesigning a "bad chart" based on Tufte's principles to reduce cognitive load and improve clarity

Module 9: Communicating Uncertainty and Statistical Inference

• Correct use of confidence intervals (CIs) and standard error bars
• Techniques for visualizing P-values and statistical significance directly on plots
• Designing comparison visualizations: using difference plots over paired bar plots
• Visualizing the result of Bayesian inference (e.g., posterior distributions)
• Practical session: Creating a plot that correctly displays both effect size and the 95% confidence interval for a comparative study

Module 10: Publication-Ready Graphics and Ethical Reporting

• Exporting vector graphics (SVG, EPS) vs. raster graphics (PNG, TIFF) for journals
• Customizing plot themes, fonts, and labels to meet specific journal guidelines
• Ensuring data integrity: avoiding truncation, misrepresentation, or cherry-picking data points
• Establishing a reproducible graphics workflow using version control and scripts
• Practical session: Refining a visualization from a previous module into a high-resolution, vector-based figure suitable for academic submission

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588