What key skills did Lila acquire during her data science education?

The correct answer emphasizes the specific skills that are foundational in the field of data science. Statistics, machine learning, and data analysis are all critical components necessary for any data scientist.

Statistics provides the tools for understanding data distributions, making inferences, and validating hypotheses. Machine learning equips individuals with the ability to build predictive models and algorithms that can learn from data. Data analysis involves the processes of cleaning, transforming, and modeling data to discover useful information and support decision-making.

These skills combine to form a robust framework for understanding complex data sets and extracting meaningful insights, which is essential in the analytics and data science domains. By acquiring these skills, Lila has the qualifications needed to pursue a career in data science, where data-driven decision-making is paramount.

The other choices reflect knowledge and skills that are not directly relevant to the core competencies required for data science, as they focus on arts, humanities, or culinary skills rather than the quantitative and analytical skills necessary in the tech-driven analytics landscape.