Become a professional Data Scientist with R and learn Machine Learning, Data Analysis + Visualization, Web Apps + more!
InstructorJuan E. GalvanEditorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This course outline slide details the machine learning curriculum, listing key technical topics such as Support Vector Machines, K-Means Clustering, and Neural Nets.
This instructional slide outlines the curriculum for data types and structures in R, covering essential concepts like vectors, matrices, lists, and data frames.
This course module introduces data manipulation in R, covering essential skills like Tidy Verse, string handling, web scraping, and specific dplyr functions.
This course module introduces career preparation for data scientists, covering essential topics like resume building, personal branding, freelancing strategies, and professional networking.
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The curriculum provides structured coverage of data types like vectors, matrices, and lists, alongside practical Tidyverse manipulation using dplyr verbs.
One sampled review suggests that some learners have found value in the exercises provided to reinforce new concepts.
Instructional content focuses on linear and logistic regression, leaving several promised machine learning topics like SVMs and NLP unaddressed.
One signal indicates that video lengths can be excessive, and a specific web scraping URL was reported as non-functional.
Certain advanced modules, such as R Shiny and data preprocessing pipelines, are described by some as brief or surface-level.
The curriculum aligns with the goal of learning R basics, but does not meet the expectations of those seeking a full data science career path.