Embark on a transformative journey into the world of applied data science with Universidad de los Andes' "Introducción a la ciencia de datos aplicada" course. This comprehensive program is designed to equip learners with the essential knowledge and skills required to thrive in the dynamic field of data science. Through a series of four immersive modules, participants will gain a deep understanding of the core principles of data science, the methodologies employed in data projects, and the utilization of statistical tools for constructing and interpreting analytical models.
Aspiring data scientists and professionals from diverse backgrounds will explore topics ranging from the fundamentals of data science to the characteristics of data scientists, the tools they use, and the methodologies essential for project implementation. Participants will delve into statistical exploratory and univariate analysis, bivariate statistical models, and the validation of statistical models. The course emphasizes the practical application of concepts through the use of Jupyter Notebook and Python programming language, ensuring a hands-on learning experience.
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This course comprises four modules, each providing a comprehensive exploration of fundamental and advanced concepts in data science, with a strong emphasis on practical application and hands-on learning.
This module provides an in-depth introduction to the fundamentals of data science and the methodologies employed in data projects. Participants will gain insights into the ASUM-DM methodology and its practical application, along with the utilization of design thinking for identifying business problems and opportunities.
Explore the realm of exploratory data analysis, statistical visualization, and data preparation using Jupyter Notebook. Gain a comprehensive understanding of univariate analysis and its application in validating business hypotheses, with a focus on practical application in real-world scenarios.
Delve into the realm of bivariate statistical analysis, including correlation tests and contingency table analysis. Participants will gain practical experience in applying statistical techniques to validate hypotheses involving multiple variables, with a focus on real-world applications and hands-on learning using Jupyter Notebook.
This module focuses on comparisons between groups and the validation of statistical models. Participants will explore the concept of p-tests, t-tests, and ANOVA, gaining practical experience in applying these techniques to validate hypotheses and analyze different samples in real-world scenarios.
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