This course will introduce the learner to the basics of Data Science. Data Science refers to the techniques used to analyze data to enhance productivity and business gain. This course is a practical introduction to the tools that will be used in the Post-Baccalaureate diploma in Applied Data Science. In this course students will apply the main tools used in Applied Data Science including: the R programming language, Matplotlib for data visualizations, dplyr for data manipulation, tidyr for reshaping data, ggplot2 for visualization of data, and interactive visualization in R. Additional tools will include version control, markdown, git, GitHub, and RStudio. By the end of this course, students will be able to apply the knowledge from term one of the Post-Baccalaureate in Applied Data Science to tabulate data, clean it, manipulate it, and run basic inferential statistical analyses on it to draw meaningful information from data.
This course will introduce the learner to the basics of Data Science. Data Science refers to the techniques used to analyze data to enhance productivity and business gain. This course is a practical introduction to the tools that will be used in the Post-Baccalaureate diploma in Applied Data Science. In this course students will apply the main tools used in Applied Data Science including: the R programming language, Matplotlib for data visualizations, dplyr for data manipulation, tidyr for reshaping data, ggplot2 for visualization of data, and interactive visualization in R. Additional tools will include version control, markdown, git, GitHub, and RStudio. By the end of this course, students will be able to apply the knowledge from term one of the Post-Baccalaureate in Applied Data Science to tabulate data, clean it, manipulate it, and run basic inferential statistical analyses on it to draw meaningful information from data.
