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via Udemy |
Go to Course: https://www.udemy.com/course/advanced-r/
Certainly! Here's a detailed review and recommendation of the Coursera course based on the provided description: --- **Course Review and Recommendation** If you are an intermediate or advanced R and data science professional seeking to elevate your skills beyond basic analytics, this comprehensive Coursera course is an excellent choice. Designed to push the boundaries of your current R knowledge, it offers in-depth coverage of advanced topics that will significantly enhance your data manipulation, programming, and integration capabilities. **What You Will Learn:** - **Advanced R Programming:** The course begins with sophisticated topics such as closures, environments, dates, and regular expressions. These are fundamental for writing clean, efficient, and effective R code. - **Web Data Parsing:** You'll gain skills in parsing HTML data, enabling you to scrape and analyze web-based information seamlessly. - **R Package Development:** The course guides you through the process of writing and documenting R packages according to CRAN standards, a valuable skill for sharing and deploying your code professionally. - **Code Profiling and Optimization:** Learn how to profile your R code to identify bottlenecks and improve performance. - **High-Performance Computing:** A standout feature is the integration of C++ with R via Rcpp, along with parallel programming using OpenMP. Mastering these will allow you to develop R functions that run 4-8 times faster, giving you a significant edge in computational efficiency. - **Language Interoperability:** The course also explores calling Python and Java from R and vice versa, empowering you to build versatile, multi-language data workflows and leverage the strengths of each language. - **SQL Integration:** Using the sqldf package, you can perform complex SQL queries within R, streamlining data processing workflows for production-grade tasks, despite some noted limitations. ** Pros:** - Extensive coverage of advanced topics suitable for experienced users. - Hands-on examples with downloadable code files and lecture materials. - Focus on performance optimization and language interoperability. - Practical insights into package development and data scraping. - Access to professional-level tools such as Rcpp and OpenMP. **Cons:** - Not suitable for beginners; prior familiarity with R is recommended. - The depth of content may be challenging for those new to advanced programming topics. **Final Recommendation:** This course is highly recommended for R practitioners who already have a solid foundation and wish to deepen their expertise. Whether you're aiming to accelerate your code, develop sophisticated R packages, or integrate R with Python, Java, or C++, this course provides the necessary skills and practical examples to transform your data science workflows. The comprehensive nature and focus on performance and interoperability make it a valuable investment for serious data professionals looking to stand out in a competitive field. --- If you enjoy learning through examples and want to expand your capabilities in high-performance computing and multi-language integration, this course is an ideal addition to your professional development portfolio.
This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries. After that we introduce the necessary skills for profiling your R code. We then move into C++ and Rcpp, and we show how to write super fast C++ parallel code that uses OpenMP. Understanding and mastering Rcpp will allow you to push your R skills to another dimension. When your colleagues are writing R functions, you will be able to get Rcpp+OpenMP equivalent code running 4-8X times faster. We then move into Python and Java, and show how these can be called from R and vice-versa. This will be really helpful for writing code that leverages the excellent object oriented features from this pair of languages. You will be able to build your own classes in Java or Python that store the data that you get from R. Since the Python community is growing so fast, and producing so wonderful packages, it's great to know that you will be able to call any function from any Python package directly from R. We finally explain how to use sqldf, which is a wonderful package for doing serious, production grade data processing in R. Even though it has its limitations, we will be able to write SQL queries directly in R. We will certainly show how to bypass those limitations, such as its inability to write full joins using specific tricks. All the code (R,JAVA,C++,.csv) used in this course is available for download, and all the lectures can be downloaded as well. Our teaching strategy is to present you with examples carrying the minimal complexity, so we hope you can easily follow each lecture. In case you have doubts or comments, feel free to send us a message