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via Udemy |
Go to Course: https://www.udemy.com/course/estadistica-descriptiva/
Certainly! Here is a comprehensive review and recommendation for the Coursera course based on the details provided: --- **Course Review and Recommendation:** ### Overview: This Coursera course, led by expert instructors Juan Gabriel Gomila and María Santos, is an excellent foundational program designed to introduce students to descriptive statistics and essential data science skills. It is particularly suited for beginners or those looking to solidify their knowledge before advancing to more complex topics such as machine learning or artificial intelligence. ### What You Will Learn: - **Software Installation & Setup:** The course begins with practical guidance on installing R, RStudio, and Anaconda Navigator for Python, ensuring students are equipped with the essential tools for data analysis. - **Programming Skills:** You'll learn to use R and Python as scientific calculators, reviewing functions, trigonometry, and combinatorics. The course also introduces functional programming in R, laying a solid groundwork for future data analysis projects. - **Data Visualization:** Through the creation of various types of graphs—including scatter plots, histograms, pie charts, and box plots—students will learn how to effectively represent and communicate statistical information. Both R (ggplot2) and Python (matplotlib) are employed, providing versatility. - **Basic Machine Learning:** An introduction to machine learning techniques, such as linear regression, helps bridge the gap between descriptive statistics and predictive modeling. - **Data Types and Probability:** The course delves into qualitative, quantitative, and ordinal data analysis, along with a beginner-friendly introduction to probability, random variables, and probability distributions. - **Statistical Metrics:** Key statistics like mean, variance, skewness, and kurtosis are covered, with hands-on calculations in both R and Python. - **Additional Resources:** A GitHub repository with all course scripts allows learners to follow along and practice independently. ### Strengths: - The course is comprehensive yet accessible, making it ideal for students starting their data science journey. - It caters to both R and Python users, broadening applicability. - The focus on foundational skills ensures participants are well-prepared for more advanced courses like Machine Learning, AI, or Data Science with Tidyverse. - The inclusion of a GitHub repository adds significant value for practice and reference. ### Who Should Enroll: - Beginners in data analysis, statistics, or programming. - Students preparing for advanced courses in machine learning, AI, or data science. - Anyone interested in understanding the basics of statistical analysis and data visualization. ### Final Verdict: This course is highly recommended for those seeking a solid foundation in descriptive statistics and data analysis tools. Its clear structure, practical approach, and supplementary resources make it an excellent starting point in the data science field. Completing this course will not only enhance your statistical understanding but also prepare you for more complex topics and projects. **If you're looking to build a strong base in data analysis and programming, this course is an outstanding choice to kickstart your data science journey!** --- Would you like a personalized recommendation based on your current skill level or specific interests?
Conoce toda la estadística descriptiva de la mano de Juan Gabriel Gomila y María Santos. Asienta las bases para convertirte en el Data Scientist del futuro con todo el contenido del curso. En particular verás los mismos contenidos que explicamos en primero de carrera a matemáticos, ingenieros o informáticos como por ejemplo:Logística e instalación de R y RStudio y de Anaconda Navigator para PythonCómo usar R y Python como si fuese una calculadora científica (incluyendo un repaso de funciones, trigonometría y combinatoria)Introducción a la programación funcional con R desde cero (ideal para seguir tomando a posteriori cursos de análisis de datos).Uso de gráficos para representar datos estadísticos incluyendo plots de nubes de puntos, histogramas, diagramas circulares o diagramas de caja y bigotes entre otros. Además tendrás ejemplos tanto en R como con matplotlib de Python.Introducción a las técnicas de machine learning como por ejemplo la regresión lineal.Profundización en tipos de datos cualitativos, cuantitativos y ordinales y el correcto análisis de cada uno de ellos.Introducción a la probabilidad, empezando desde lo más básico, pasando por variables aleatorias hasta llegar a tratar las distribuciones de probabilidad más conocidas (tanto discretas como continuas)Comprende los estadísticos más relevantes de una distribución, como por ejemplo la media, varianza así como sesgo y curtosis. Y aprende a calcularlos tanto con R como con Python.Repositorio Github con todo el material del curso para disponer de los mismos scripts que usamos en clase desde el minuto inicial.Una vez termines el curso podrás seguir con los mejores cursos de análisis de datos publicados por Juan Gabriel Gomila como los cursos de Machine Learning o Inteligencia Artificial con Python o RStudio o el Curso de Data Science con Tidyverse y RStudio. Todo el material del curso está enfocado en resolver los problemas de falta de base que presentan los estudiantes de esos cursos avanzados y poderlo hacer en un curso a parte te permitirá nivelar tus conocimientos y tomar los otros cursos con garantías de éxito.