What you'll learn:
- Basics of R programming and its syntax.
- Working with variables, data types, and operators in R.
- Understanding control structures like loops and conditional statements.
- Handling data structures such as vectors, matrices, lists, and data frames.
- Importing, exporting, and manipulating data in R.
- Exploratory data analysis and data visualization using R.
Course offers:
- We offer one-on-one or group tutoring sessions in a variety of subjects.
- Experienced tutors who provide personalized instruction and guidance.
- Interactive virtual classrooms with video and audio communication.
- Access to numerous educational resources and study materials.
- Online chat or messaging for communication outside of tutoring sessions.
- Integration with other online learning platforms or tools.
Requirements:
- Reliable internet connection to ensure smooth video streaming and communication.
- You will require an audio- and video-capable computer, laptop, or mobile device.
- Some examples of updated web browsers include Google Chrome, Mozilla Firefox, and Safari.
- Necessary software or applications, such as video conferencing tools or learning management systems.
- A headset or headphones with a microphone for clear audio communication.
Course content:
Duration : 130 hours
- Class #1:Introduction to the C programming language.
- Class #2:Basic syntax and data structures in R (vectors, matrices, data frames).
- Class #3:Working with data manipulation packages in R (dplyr, tidyr).
- Class #4:Importing and exporting data in various formats (CSV, Excel, SQL databases).
- Class #5:Exploratory data analysis (EDA) techniques using R.
- Class #6:Data visualization with popular R packages (ggplot2, plotly).
- Class #7:Statistical analysis and hypothesis testing in R.
- Class #8:Building and evaluating predictive models with R (regression, classification).
- Class #9:Working with time series data and forecasting using R.
- Class #10:Creating interactive dashboards and reports with R Shiny.
- Class #11:Web scraping and text mining techniques in R.
- Class #12:Introduction to machine learning in R (decision trees, random forests, SVM).
- Class #13:Optimizing R code for efficiency and performance.
- Class #14:Collaborative coding and version control with RStudio and Git.
- Class #15:Working with big data in R using distributed computing frameworks (Spark, Hadoop).
- Class #16:Integrating R with other programming languages and tools (Python, SQL).
- Class #17:Best practices for writing clean and maintainable R code.
- Class #18:Debugging and troubleshooting common errors in R.
- Class #19:Package development and publishing on CRAN (Comprehensive R Archive Network).
- Class #20:Practical projects and real-world case studies to apply R programming skills.
Skills you will acquire:
- Data Analysis
- Debugging
- Rstudio
- R Programming
Book Demo Class
Description:
R is a programming language and environment designed specifically for statistical computing and data analysis. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and was first released in 1995. R has gained widespread popularity in the data science and statistics communities due to its extensive libraries, interactive nature, and robust statistical capabilities.
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Book Demo Class
