Confident start with R and RStudio - You will set up R and RStudio step by step, so you can prepare your own working environment and start analyzing data faster without confusion or common technical roadblocks.
Reliable data importing - You will learn how to load data from CSV files, Excel sheets, and databases, convert it into tibbles, and immediately inspect structure, missing values, and key summaries.
Effective data manipulation - You will use dplyr to filter, select, sort, group, and summarize data with clear code that is easy to extend, reuse, and apply in your everyday analytical work.
Tidying data without manual fixes - You will learn tidyr and turn messy datasets into tidy structures without tedious spreadsheet editing, making your data ready for analysis, reporting, and further processing.
Clear charts with ggplot2 - You will build bar, line, scatter, and box plots, then customize them so your analytical results are easier to explain and more convincing for both business and technical audiences.
Combining multiple data sources - You will learn how to join tables safely with different types of joins, helping you create richer datasets and avoid common mistakes that often appear when merging information.
Hands-on real-world cases - You will practice sales analysis, survey data cleaning, and dashboard creation, so after the course you can transfer the techniques directly into your own analytical projects.
Reporting and presenting results - You will learn to prepare analyses and dashboards in RMarkdown, allowing you to present results in a structured way and reduce the time needed to create reports for your team.
Basic computer skills - You should be comfortable installing software, working with files and folders, and using your operating system so you can set up the R environment and training materials smoothly.
Understanding tabular data - You should understand columns, rows, headers, and data formats, because these ideas will be used throughout the course when importing, cleaning, and analyzing datasets.
Basics of data analysis - You should know core analytical concepts such as filtering, sorting, grouping, and summarizing, so you can follow the logic of the operations performed in tidyverse more easily.
Readiness to work with code - You should be willing to write and run simple text-based commands, even if you have never programmed before, because the training is built around hands-on work in R.
