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Thinking Like a Storyteller

Mold the data into the stories for expressing result in better way using various visualization techniques.


2 Days


Intermediate Level

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As per your team needs

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Data that sits in Spark or Hadoop or even a spreadsheet is not as powerful as data that is interpreted, packaged and communicated to those who need to hear what it has to say. Anyone who works with data needs to know how to effectively present that analysis to the people who will be making decisions based on the data. And it’s not as simple as throwing charts at people. You need to create narratives and visuals that really connect with audiences. 

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.

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This program is designed for Data Engineers, Data Storytellers and Data Scientists

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  • 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
  • Why is Storytelling important?
  • How to share stories?
  • Key types of plots
  • Which one to choose when?
  • Introduction to Statistics
  • Importance of Statistics
  • Descriptive Statistic
  • Inferential Statistics
  • Data Validation
  • Data Deduplication
  • Handling Missing Data
  • Data Normalization
  • Data Filtering
  • Outlier Detection
  • Data Encoding
  • Data Enrichment
  • Hands-on
  • Exploratory Analysis using MatPlotLib
  • Hands-on Exercises
  • Introduction to Seaborn
  • Demo: Working with Seaborn
  • Hands-on Exercise(s)
  • Visual Perceptions for better Visualization
  • Visual Design and Application of Data Graph
  • Dissecting model Visuals 
  • Principles for Chart Designs
  • Turning your graph into Stories
  • Hands-on Exercise(s)
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Attendees should have basic knowledge of Python.


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