Technologies

Data preparation and quality control in R

Practical training on data preparation and quality control in R using the tidyverse ecosystem. Participants will learn the data processing pipeline (dplyr, tidyr, stringr, lubridate), validation tools (pointblank, validate), missing data handling methods (mice, naniar), outlier detection, data profiling, and building reproducible ETL workflows.

Why choose this training?

Data quality is the foundation of every reliable analysis — and in practice, analysts spend up to 80% of their time cleaning and preparing data. Without a systematic approach to validation, hidden errors propagate through the entire analytical pipeline. R with the tidyverse ecosystem offers some of the best tools for structural data processing and quality control.

During the training, participants will learn the complete pipeline — from loading raw data, through transformation with tidyverse, validation with pointblank and validate, missing data handling with mice and naniar, to building reproducible ETL workflows with the targets package.

After completing the training, participants will be able to: build data processing pipelines using tidyverse, define and automate validation rules with pointblank and validate, analyze missing data patterns and apply appropriate imputation methods, detect outliers using statistical methods and visualize anomalies. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: data analysts responsible for data quality in their organization, data scientists working with unclean datasets, data engineers building ETL pipelines in R.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 2 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Build data processing pipelines using tidyverse (dplyr, tidyr, stringr, lubridate)
Define and automate data validation rules with pointblank and validate
Analyze missing data patterns and apply appropriate imputation methods (mice)
Detect outliers using statistical methods and visualize anomalies
Create automated data profiling and quality reports
Design reproducible ETL workflows with the targets package
Integrate data quality control into existing analytical pipelines

Who is this training for?

Data analysts responsible for data quality in their organization

Data Scientists working with unclean datasets

Data Engineers building ETL pipelines in R

Statisticians and researchers ensuring analysis reproducibility

Business Intelligence specialists

R programmers looking to standardize data cleaning processes

Data managers and data governance professionals

Prerequisites

Basic knowledge of R (variables, functions, data structures)
Experience working with data frames (data.frame / tibble)
Basic familiarity with tidyverse (dplyr, ggplot2) is recommended
Own laptop with R and RStudio installed

Training program

Tidyverse architecture and tidy data philosophy
dplyr — filtering, transformation and data aggregation
tidyr — pivoting, separating and combining columns
stringr — text data processing and regex
lubridate — date and time zone handling

pointblank — declarative validation with HTML reports
validate — validation rules and quality monitoring
Defining business rules and acceptance thresholds
Automated data quality reports
Integrating validation into processing pipelines

naniar — visualization and analysis of missing data patterns
Missing data mechanisms (MCAR, MAR, MNAR)
mice — multiple imputation by chained equations
Comparing imputation methods and evaluating their quality
Strategies for handling missing values in different data types

Statistical methods for outlier detection (IQR, Z-score, Mahalanobis)
Data profiling packages (skimr, DataExplorer)
Automated dataset profiling reports
Distribution and anomaly visualization
Strategies for handling outliers

Designing ETL pipelines with the targets package
Data cleaning as a reproducible workflow
Logging and auditing data transformations
Pipeline testing with testthat
Best practices for organizing data quality projects

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What prerequisites do I need to meet before the training?

Basic knowledge of R (variables, functions, data structures) and experience working with data frames are required. Familiarity with tidyverse is recommended but not mandatory — key functions will be reviewed at the beginning of the training.

What format is the training delivered in and how long does it last?

The training lasts 2 days and is available in online (live) and on-site formats. The program is based on hands-on exercises — participants work with real, messy datasets. Each participant receives training materials and a completion certificate.

Who is this training designed for?

The training is designed for data analysts responsible for data quality, data scientists working with unclean datasets, data engineers building ETL pipelines in R, and statisticians ensuring analysis reproducibility.