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Inicio 4 June 2026 03:27

Fin 4 June 2026

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Dartmouth College

Foundations of Data Science

Explora el fascinante mundo de la Ciencia de Datos con el curso integral "Fundamentos de la Ciencia de Datos", ofrecido por Dartmouth College en edX. Este curso profundiza en la compleja relación entre la Ciencia de Datos, la inteligencia artificial (IA) y sus componentes cruciales, incluyendo el aprendizaje estadístico (AE), el aprendizaje automát.
Dartmouth College via edX

Dartmouth College

15 Cursos


Fundada en 1769, Dartmouth es una universidad de la Ivy League que ofrece un ambiente de pequeña ciudad en Hanover, NH, con académicos de alta calificación y una gran cantidad de actividades estudiantiles.

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Resumen

Explore the fascinating world of Data Science with the comprehensive course "Foundations of Data Science," offered by Dartmouth College on edX. This course delves into the intricate relationship between Data Science, artificial intelligence (AI), and its crucial components, including statistical learning (SL), machine learning (ML), and deep learning algorithms (DL), which are pivotal in driving organizational innovation and value creation.

Echoing Dr. Jim Gray's perspective, this curriculum underscores Data Science as the fourth paradigm, essential for formulating inventive solutions to organizational challenges.

Starting with the fundamentals, the course introduces basic concepts in probability, including joint and conditional probabilities, setting the stage for their application in ML algorithms for Market Basket Analysis and Recommender Systems.

The journey continues through random variables, discrete and continuous probability distributions, sampling, estimation, and the principle of the central limit theorem, laying a solid foundation in statistical analysis.

The curriculum emphasizes the critical process of feature selection in ML model building to circumvent overfitting and underfitting, explaining the use of hypothesis testing in models like regression and logistic regression for this purpose. It further explores various hypothesis tests and their application in feature selection.

Additionally, the course covers the quintessential step of optimization in ML model development, highlighting pivotal techniques and algorithms such as Gradient Descent crucial for enhancing AI and ML models.

A deep dive into the essentials of linear algebra is provided, discussing matrix representation and operations, including matrix inverse and multiplication, as they form the backbone of AI and ML model development.

Designed for both students and practitioners keen on enlarging their understanding of Data Science's fundamental concepts, this course paves the way for a thriving career in Data Analytics. It navigates through the diverse categories of Machine Learning, Deep Learning, Data Science, and Recommender Systems Courses, ensuring a well-rounded educational experience.


Impartido por

Dinesh Kumar


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