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Opiniones y comentarios de aprendices correspondientes a Data Analysis with R por parte de Habilidades en redes de IBM

155 calificaciones

Acerca del Curso

The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM....

Principales reseñas


5 de dic. de 2021

It is excellent course. I recommend for all that do not have a lot of knowledge and experience in data analysis with R Programming. Thank you for this opportunity.


3 de ago. de 2021

I could not use WatsonStudio and used RStudio instead. It might have caused problems to the reviewers of peer assignment. Course content is good.

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