Advanced Storytelling with Data Visualizations
In this 2 day training you will be learning how to build an effective story and how to tell the data story using Data Visualizations to the stakeholders. We will be using different Business Intelligence Tools based on the customer’s requirement to do the hands on practices (Microsoft Power BI or Oracle Analytics Desktop).
Enterprise organizations need well designed Business Intelligence reports and analyses in order to make effective decisions for different kinds of reasons - like being profitable, mitigating the risks, improving the sales/service productivity etc. Presenting the data in an effective way using Data Visualizations is very crucial to our businesses.
Data Visualization is the graphic representation of the data. It involves usage of images that communicate relationships between data and viewers. To communicate information efficiently, data visualization uses dots, lines, graphics and other tools as well. However, we should show the meaning of the data very easily and deliver an objective message to the stakeholders. Eventually, telling the story using data visualizations plays a very important role at this phase.
About the instructor Cuneyt Yilmaz
Cuneyt is working as senior instructor and consultant with specialty in Analytics, Business Intelligence, Data Management and Performance Tuning. He is a certified trainer for Microsoft and Oracle, and he also delivers training on MongoDB and PostgreSQL.
Cuneyt is based in Istanbul but has been working with customers in 35 countries across the Nordics and EMEA over the last 15 years. He combines top technology skills with his kindness and strong pedagogical skills. We dare to say, nobody leaves Cuneyt`s classroom disappointed!
Private event training
We arrange this training for your company or departement upon request. Please contact us for dates and prices: itpro@glasspaper.no
Course content
Module 1: Traditional Business Intelligence Systems in Organizations and the Existing Challenges
Module 2: New Business Requirements and Solutions for Decision Makers
a. Exploration
b. Analysis
c. Presentation
Module 3: Reports, Attributes and Measure Types
a. Definitions
b. Measurement Scales
c. Semantic Operations in Reports
Module 4: Data Visualizations Overview
a. Purpose of Visualization
b. Visualization Types
c. Visualization Implementation Techniques
d. Narrative Structures in Data Visualization
- Author-Driven Visualizations
- Reader-Driven Visualizations
- Martini Glass
Module 5: Interactivity in Visualizations
a. Tool Tips
b. Filters
c. Drill Down
d. Swap
e. Navigate
f. Highlight
g. Others
Module 6: Choosing the Right Visualizations Types for Different Scenarios
a. Comparisons
b. Relationships
c. Cause and Effect Relationships
d. Proportions
e. Predictions
f. Patterns
g. Others
Module 7: What is Next?
a. Understanding the Data
b. Richness of the Data Sets
c. Analytics
d. Visualizations
e. Infopgraphics
f. Data Storytelling
g. Extracting Information
h. Transforming Information into Knowledge
Module 8: Data Storytelling for extracting knowledge
a. Definitions
b. Requirements
c. Start Point
d. Sketch
e. Direction of the Data Story
f. Story Components
g. Defining the Context
h. Initial Analysis
i. Blocks for Presentation
Module 9: How to Tell the Story?
a. Story Structure
b. Events of a Story
c. Characteristics of the Teller
d. Personas
e. Linearity
f. Simplicity vs Complexity
g. Multiple Perspectives
h. Progressive Depth
i. Redundancy
j. Relatibility
k. Lifecycle of the Story
Module 10: Tips in the Visual Story
a. Cognitive Overload
b. Visual Perception
c. Objective Stories
d. Mapping the Visualizations to Story
e. Visual Order
f. Test the Story
Module 11: Perspective of Business Users
a. Look and Feel (Design)
b. Use of Contrasts
c. Choosing the Right Colors
d. Placing the Visualizations
e. Improving Imaginations
f. Effective Decisions
Module 12: Sample Data Storytelling Project
a. Define the Requirements
b. Build the Analyses
c. Selecting the Visualizations
d. Presentation Phase
Module 13: Best Practices vs Potential Design Mistakes
