Educational Background: A basic understanding of biology, chemistry, and computational techniques is recommended.
Prerequisites: Familiarity with molecular biology and basic computer skills.
Course Access: Available online 24/7, accessible from anywhere with an internet connection.
Device Compatibility: Compatible with desktop, laptop, tablet, and mobile devices.
Technical: Reliable internet connection and up-to-date browser/software.
Course Description
Fundamental Concepts of R Programming: Explore data types, variables, and how R handles different data types, setting the foundation for effective data manipulation and analysis.
Data Structures in R: Learn about vectors, matrices, and data frames, essential for organizing and storing data efficiently for statistical computing tasks.
Control Structures and Automation: Master loops and conditional statements to automate tasks and make data-driven decisions based on specific conditions within R programming.
Functions in R: Understand the concept of functions as reusable blocks of code, enabling you to create, call, and utilize them effectively for task-specific operations.
Data Handling and Visualization: Gain skills in importing, exporting, and manipulating data from external sources, coupled with data visualization techniques such as pie charts for effective data exploration and communication.
Advanced Data Manipulation with dplyr: Utilize the dplyr package to filter, transform, summarize, and visualize data, extracting meaningful insights crucial for informed decision-making in data analysis.
Course Outcomes
Master fundamental R programming concepts for data manipulation and analysis.
Gain proficiency in using R's data structures (vectors, matrices, data frames) for efficient data organization.
Develop skills in automating tasks and making data-driven decisions using loops and conditional statements in R.
Create and apply functions in R to enhance code modularity and efficiency in data analysis.
Learn data handling techniques and data visualization methods (like pie charts) to explore and present data effectively.
Utilize dplyr for advanced data manipulation tasks, enabling insights extraction from complex datasets in R.
Rules & Regulations
Chapter Sequence: All chapters must be attended in the specified sequence to ensure a structured learning path.
Mandatory Completion: Completion of all chapters is required to qualify for automatic certification.
Access and Progress: Learners can access the course content anytime and progress at their own pace, with no strict deadlines for each chapter.
Assessments: Each chapter may include quizzes, assignments, or assessments that must be completed to progress to the next chapter.
Certification:Upon successful completion of all chapters and assessments, a certificate of completion will be automatically generated and made available in the learner's LMS profile
Support and Assistance: Learners have access to technical support and course assistance through email or discussion forums.
