Free online courseData Science

Course Description

The course "Data Science with R programming" offers a comprehensive introduction to mastering data science using the R programming language. With a total duration of 5 hours and 58 minutes, this extensive course delves into numerous facets of R programming, making it an excellent resource for aspiring data scientists and professionals in the field of Information Technology, specifically within the subcategory of Artificial Intelligence.

Starting from the basics, the course introduces participants to RStudio, illustrating its significance and encouraging them to download and set up the environment. Participants will then be guided through basic arithmetic operations and coding in R, building a solid foundation for more complex topics.

One of the critical aspects emphasized in the course is working with data structures in R. From creating and manipulating vectors and matrices to importing data from various formats such as Excel, CSV, and text files, the course ensures that participants gain hands-on experience with practical data handling techniques. Participants will also learn to export data from R, ensuring they can seamlessly transition between different software environments.

The course places a strong emphasis on data manipulation and transformation, illustrating how to work with variables, subset data, and utilize logic statements. R's powerful functions, such as cbind and rbind for combining data, are also covered in detail. Additionally, participants will learn to set up their working directory in R and develop their coding practices by writing scripts and installing packages.

Visualization is another essential component of data science, and this course does not disappoint. From bar charts and pie charts to box plots, histograms, and scatterplots, participants will gain the skills to create a wide range of visual data representations. The course also covers how to customize plots, allowing participants to modify and enhance the appearance of their visualizations with text and legends.

Understanding data distributions is pivotal in data science, and the course delves into various distributions such as binomial, Poisson, and normal distributions, along with concepts like Z scores and t scores. This foundational knowledge prepares participants for more advanced statistical tests and methods.

Participants will explore a variety of hypothesis testing techniques, including one-sample and two-sample t-tests, Mann-Whitney U tests, bootstrapping, permutation tests, and more. These topics are essential for making data-driven decisions and validating findings in real-world scenarios.

Advanced topics, such as ANOVA, chi-square tests, Fisher's exact tests, and regression analysis, are also thoroughly covered. The course guides participants through simple linear regression, multiple linear regression, and polynomial regression. It also explains how to check regression assumptions, handle dummy variables, and interpret interactions in linear regression models.

Overall, "Data Science with R programming" provides a thorough and practical guide to data science using R, enabling participants to confidently tackle data analysis and visualization tasks in their professional endeavors. Whether you are a novice or looking to enhance your existing skills, this course serves as a valuable resource in the realm of data science and artificial intelligence.