The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues. The course covers terminology and concepts, tools and methods, use cases and success stories of data science applications. The course explains what is Data Science and why it is so hyped. It discusses the value that Data Science can create, the main classes of problems that Data Science can solve, the difference is between descriptive, predictive and prescriptive analytics, and the roles of machine learning and artificial intelligence. From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms. All topics are covered with example-based lectures, discussing use cases, success stories and realistic examples.
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Qué aprenderás
What is data science
How data science, machine learning, and data-driven innovation can benefit business outcomes
Foundational concepts and intuitions about machine learning techniques
Habilidades que obtendrás
Programa - Qué aprenderás en este curso
Introduction to Data-driven Business
This module introduces the course and offers some basic overview of the topics. It presents the crucial concepts related to data science and big data and provides an outlook on how to use them in real world settings for increasing business value.
Terminology and Foundational Concepts
In this module, you will learn the foundational concepts of machine learning and data science. You will understand how these techniques can be useful in terms of increased business value for organizations, thanks to the discussion of a very well known success story, namely Netflix, which can be deemed as a completely data-driven business. You will also understand how machine learning is different from programming.
Data Science Methods for Business
In this module, you will learn the concepts and intuitions about the basic approaches for data analysis, including linear regression, naive Bayes, decision trees, clustering, and logistic regression. All the methods are presented starting from typical business uses and are covered in an intuitive way through a guided explanation of how the approach works on simple examples.
Challenges and Conclusions
This module summarizes the concepts learned so far and introduces a set of challenges and risks that data-savvy managers must take into account when deciding for a data-driven strategy.
Instructores
Marco Brambilla
Associate Professor
Emanuele Della Valle
prof.Acerca de EIT Digital
Acerca de Politecnico di Milano
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
