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Thinking like a Storyteller

Course Summary

The Thinking like a Storyteller training course is designed to demonstrate how to create data narratives and visuals that connect with audiences.

The course begins with an illustration of the realm of data and why data is important. It then examines the art of storytelling as well as good and bad visualizations. The course concludes with an advanced analysis of storytelling and effective visualization.

Prerequisites: A basic knowledge of Python.

Purpose
 This course will cover data storytelling, data visualization and communications best practices - all with an eye to turning a raw set of data and converting it into a compelling narrative presentation that will resonate with your audience.
Audience
 Python developers looking to increase their data analysis and visualization skills.
Role
Business Analyst - Data Engineer - Data Scientist - DevOps Engineer - Project Manager - Q/A - Software Developer - System Administrator - Technical Manager - Web Developer
Skill Level
Intermediate
Style
Workshops
Duration
2 Days
Related Technologies
Big Data Training | Data Visualization | Python

 

Productivity Objectives
  • Explain why data is important
  • Analyze the different types of data
  • Comprehend the art of telling stories
  • Differentiate between good and bad visualizations
  • Illustrate your data with statistics
  • List the ways data is pre-processed and why it is pre-processed
  • Perform exploratory analysis using MatPlotLib
  • Design effective visualizations

What You'll Learn:

In the Thinking like a Storyteller training course, you'll learn:
  • Realm of Data
    • Why Data is important
    • Different types of Data
    • Context is the King
    • Metadata - Data about Data
    • Know your Data Sources
    • Data Governance
    • Reference & Master Data
    • Various Data Quality Dimensions
  • Art of Telling Stories - Introduction
    • Importance of Storytelling
    • Share stories
    • Key types of plots
    • How to make decisions
  • Good and Bad Visualization
    • Effective Data communication
    • Effective communication examples
    • Deadly Sins of Graph Design
  • Understanding your Data
    • Introduction to Statistics
    • Importance of Statistics
    • Descriptive Statistics
    • Inferential Statistics
  • Data Pre-Processing
    • Data Validation
    • Data Deduplication
    • Handle Missing Data
    • Data Normalization
    • Data Filters
    • Outlier Detection
    • Data Encode
    • Data Enrichment
  • Storytelling with Data
    • Exploratory Analysis using MatPlotLib
    • Introduction to Seaborn
    • Demo: Working with Seaborn
  • Designing Effective Visualization
    • Visual perceptions for better visualization
    • Visual design and application of data graph
    • Dissect model visuals
    • Principles for Chart Designs
    • Turn graphs into Stories
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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