Modernizing the Curricula of Statistics Courses through Statistical Learning

• Published in: Promoting Statistical Practice and Collaboration in Developing Countries pp. 351 - 361
• Main Author: Nunes, Marcus Alexandre
• Format: Book Chapter
• Language: English
• Published: CRC Press 2022
• Edition: 1
• Subjects: UCI Machine Learn Repository; Pennsylvania State University; Instituto Brasileiro De Geografia; Undergraduate Statistics Programs; Statistical Consulting Centers; Exponential Ratio Type Estimator; Free Statistical Software; ASA Guideline; Support Vector Machine; Vice Versa; HDI; Random Forest; Undergraduate Statistics Students; HPD Interval; IBGE; Data Science Practice; UFO Sighting; Left Join; Random Forest Model; Big Data Modeling; Autoregressive Distributed Lag Model; Good Data Visualization; HPD; Basic Statistical Inference
• ISBN: 103219555X; 9781032195551; 1032198508; 9781032198507
• DOI: 10.1201/9781003261148-30

With recent advances in computer technology, it has become easier to fit complex models to data. Moreover, free computing cloud alternatives can help those without a powerful computer in this fashion. Nonetheless, many data repositories from governments and companies can be obtained for free, helping those who have research questions to answer. These two changes are shaping statistical learning in developing countries, making the available tools for developed and developing countries less unequal. In this chapter, we will show how statistical educators can take advantage of some of these resources, making their students more prepared for the changes that are already happening in the statistical world in the 21st century