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Presenting Visual Data

Course Summary

The Presenting Visual Data training course will engage students to understand visualization best practices and design principles for communicating business results. The course will support students to effectively translate business results using data.

The course begins with a focus on design principles, a comparison of technology stack with relevant visualizations and demonstrations of the tools for developers. Next, the course covers business case analysis and includes hands-on individual and group labs for students to simulate business environments and use cases. The course concludes with a comparison of visualization tools.

The course is designed for performance team members, who have an understanding of software principles and data analysis.

Purpose
 Learn how to evaluate a visuals request, choose the most appropriate visualization tool, and create effective visuals to suit the needs of the request.
Audience
 Performance Team members familiar with performance data that needs to be shared with other stakeholders, such as customers, developers, business stakeholders, etc.
Role
Business Analyst - Project Manager - Software Developer - Technical Manager
Skill Level
Introduction
Style
Workshops
Duration
2 Days
Related Technologies
Python

 

Productivity Objectives
  • Identify Design principles for creating effective visualizations
  • Assess the benefits of market-leading visualization tools
  • Apply business case analysis to determine how visualizations relate to Key Performance Indicators (KPIs) and Objectives and Key Results (OKRs)
  • Use core tools for visualizations both with drag-and-drop and code interfaces

What You'll Learn:

In the Presenting Visual Data training course, you'll learn:
  • Introduction to Presenting Visual Data:
    • Importance of effective visualization in communicating business results.
    • Role of visualization in performance analysis and decision-making.
  • Design Principles for Effective Visualizations:
    • Understanding design principles such as clarity, simplicity, consistency, and relevance.
    • Importance of choosing the right visualization type based on the data and the audience.
    • Best practices for creating visually appealing and informative charts and graphs.
    • Hands-on Lab: Designing Visuals - create sample visualizations applying design principles discussed.
  • Simplifying Data Models:
    • Techniques for simplifying complex data models for better visualization.
    • Strategies for data preprocessing and transformation to enhance visualization effectiveness.
    • Use of aggregation, summarization, and filtering techniques to focus on key insights.
    • Hands-on Lab: Data Preprocessing - preprocess sample datasets to prepare them for visualization.
  • Visualization Tools: Pandas, Plotly, Grafana, Jupyter Notebooks
    • Overview of popular visualization tools and libraries.
    • Introduction to Pandas for data manipulation and visualization in Python.
    • Hands-on Lab: Pandas Basics - practice basic data manipulation and visualization using Pandas.
    • Exploring Plotly for interactive and web-based visualizations.
    • Hands-on Lab: Interactive Visualizations with Plotly - create interactive plots and dashboards using Plotly.
    • Introduction to Grafana for time-series data visualization and monitoring.
    • Hands-on Lab: Time-Series Visualization with Grafana - explore Grafana's features for time-series data visualization.
    • Overview of Jupyter Notebooks for creating interactive and collaborative data notebooks.
    • Hands-on Lab: Jupyter Notebooks Introduction - get familiar with Jupyter Notebooks and its integration with visualization libraries.
  • Comparison of Visualization Tools:
    • Detailed comparison of Pandas, Plotly, Grafana, and Jupyter Notebooks.
    • Evaluation criteria including ease of use, flexibility, interactivity, and scalability.
    • Use cases and scenarios where each tool excels or falls short.
    • Hands-on Lab: Coding comparisons of features in Pandas, Plotly, Grafana, and Jupyter Notebooks
  • Evaluating Requests:
    • Understanding the process of evaluating visualization requests.
    • Techniques for gathering requirements and clarifying objectives.
    • Analysis of sample visualization requests and brainstorming potential solutions.
    • Hands-on Lab: Gather requirements for a visualization project and create prototypes / mockups to validate visualization concepts.
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

VMware

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