Are you interested in learning machine learning and data science but struggle with messy, real-world biological data? Do you want to learn how to pre-process biological data for your machine learning projects easily and effectively?
If your answer is yes, then we have an exciting opportunity for you. BioCode is offering a comprehensive course on Manipulation of Biological Datasets in R using Dplyr and TidyR for Machine Learning and Data Science.
In this course, you will learn how to transform raw biological data into a format that is more easily and effectively processed for data mining, machine learning, and other data science-related tasks. You will gain an in-depth understanding of data pre-processing and manipulation in R using the Dplyr and TidyR packages.
The course is designed for absolute beginners in bioinformatics scripting, and you don’t need any prior knowledge of scripting or bioinformatics to get started.
The course consists of three sections:
Section 1: Introduction to Machine Learning and Data Science – In this section, you will gain an understanding of the concepts related to machine learning and data science. You will learn about R language, data science, machine learning, and data pre-processing.
Section 2: Cancer & Biological Data Pre-processing for Machine Learning Using Dplyr – In this section, you will learn how to pre-process biological data for machine learning using the Dplyr package in R language. You will learn about various functions provided by Dplyr, such as filter(), select(), mutate(), group_by(), and summarize().
Section 3: Cancer & Biological Data Pre-processing for Machine Learning Using Tidyr – In this section, you will learn how to pre-process biological data for machine learning using the TidyR package in R language. You will learn about various functions provided by TidyR, such as drop_na(), fill(), replace_na(), spread(), gather(), separate(), and pull().
By the end of this course, you will be able to perform data manipulation on biological data using various functions provided by Dplyr and TidyR package in R. You will also gain hands-on experience in solving a case study using TidyR.