Ch. 1 - Language of data

Welcome to the course!

Loading data into R

Types of variables

Identify variable types

Categorical data in R: factors

Filtering based on a factor

Complete filtering based on a factor

Discretize a variable

Discretize a different variable

Combining levels of a different factor

Visualizing numerical data

Visualizing numerical and categorical data

Ch. 2 - Study types and cautionary tales

Observational studies and experiments

Identify type of study: Reading speed and font

Identify type of study: Countries

Random sampling and random assignment

Random sampling or random assignment?

Identify the scope of inference of study

Simpson’s paradox

Number of males and females admitted

Proportion of males admitted overall

Proportion of males admitted for each department

Admission rates for males across departments

Recap: Simpson’s paradox

Identify type of study: Countries [new]

Ch. 3 - Sampling strategies and experimental design

Sampling strategies

Sampling strategies, determine which

Sampling strategies, choose worst

Sampling in R

Simple random sample in R

Stratified sample in R

Compare SRS vs. stratified sample

Principles of experimental design

Identifying components of a study

Experimental design terminology

Connect blocking and stratifying

Ch. 4 - Case study

Beauty in the classroom

Inspect the data

Identify type of study

Sampling / experimental attributes

Variables in the data

Identify variable types

Recode a variable

Create a scatterplot

Create a scatterplot, with an added layer

Congratulations!

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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