Ch. 1 - Exploring Categorical Data

Exploring categorical data

Bar chart expectations

Contingency table review

Dropping levels

Side-by-side barcharts

Bar chart interpretation

Counts vs. proportions

Conditional proportions

Counts vs. proportions (2)

Distribution of one variable

Marginal barchart

Conditional barchart

Improve piechart

Ch. 2 - Exploring Numerical Data

Exploring numerical data

Faceted histogram

Boxplots and density plots

Compare distribution via plots

Distribution of one variable

Marginal and conditional histograms

Marginal and conditional histograms interpretation

Three binwidths

Three binwidths interpretation

Box plots

Box plots for outliers

Plot selection

Visualization in higher dimensions

3 variable plot

Interpret 3 var plot

Ch. 3 - Numerical Summaries

Measures of center

Choice of center measure

Calculate center measures

Measures of variability

Choice of spread measure

Calculate spread measures

Choose measures for center and spread

Shape and transformations

Describe the shape

Transformations

Outliers

Identify outliers

Ch. 4 - Case Study

Introducing the data

Spam and num_char

Spam and num_char interpretation

Spam and !!!

Spam and !!! interpretation

Check-in 1

Collapsing levels

Image and spam interpretation

Data Integrity

Answering questions with chains

Check-in 2

What’s in a number?

What’s in a number interpretation

Conclusion

About Michael Mallari

Michael is a hybrid thinker and doer—a byproduct of being a StrengthsFinder “Learner” over time. With nearly 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.

Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.

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