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LinkedIn Learning

Data Fluency: Exploring and Describing Data (2019)

via LinkedIn Learning

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Overview

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Learn how anyone, in any industry, can speak the language of data analysis. Find out how to prepare data, explore it visually, and describe it using statistical methods.

Syllabus

Introduction
  • Gather greater insight and make better decisions with your data
1. Think with Data
  • The meaning of data fluency
  • Data fluency is for everyone
  • Data fluency in practice
  • Make intuitive thinking explicit
  • Think about causes
  • How to develop data fluency
  • Data-driven decision-making
  • ROI and the 80/20 rule for data fluency
  • Put data in context
2. Prepare Data
  • Data ethics
  • Use in-house data
  • Use open data
  • Gather new data
  • Use third-party data
  • Assess the quality of data
  • Assess the generalizability of data
  • Assess the meaning of data
  • Assess the ambiguities in data
  • Adapt data: Coding text
  • Adapt data: Sums and means
  • Adapt data: Rates
  • Adapt data: Ratios
  • Adjust ratios in practice
3. Explore Data
  • Visual primacy: The importance of starting with pictures
  • Bar charts
  • Grouped bar charts
  • Pie charts
  • Dot plots
  • Box plots
  • Histograms
  • Line charts
  • Sparklines
  • Scatterplots
4. Describe Data
  • Numerical descriptions
  • Describe measures of center
  • Describe variability with the range and interquartile range (IQR)
  • Describe variability with the variance and standard deviation
  • Rescale data with z-scores
  • Interpret z-scores
  • Describe group differences with effect sizes
  • Interpret effect sizes
  • Predict scores with regression
  • Describe associations with correlations
  • Effect size for correlation and regression
Conclusion
  • Next steps

Taught by

Barton Poulson

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4.6 rating at LinkedIn Learning based on 2437 ratings

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