tvyal is data
As shown in our animated logo we simplify the complexity of any data-blobs to digestible pieces and forms the T-wings that can help your business fly more effectively. The logo is crated using a family of mathematical function inside a unit sphere, and not by graphics editor or design programs.

We build systems that can learn from data and we find relationships between variables to predict outcomes for your business.

We provide custom tailored models that suites on your data needs, including any type of regression or classification models.

We analyze real time data and provide real time summaries. We can deal with practically any problem no matter the size of the data.

We can showcase the beauty of data with visualizations. They are the best tool to understand data, to discover the trend and the unknown.

Dashboarding solutions as a web applications are intended for the end users to understand and play with the data.

We provide analytical, statistical reports for your organizational needs.

Understand your costumers better with statistical surveys. We can help you to gather your data for the future needs.

Learn about the latest analytical methods, technologies, and applications that can make a direct impact on your business. 


Our goal is to extract valuable insights from your data to help you understand what's happening, operate your business more efficiently, and make better data-driven decisions.

Our Clients

IMEX Group

Z&A Stores

Ministry of Territorial Administration and Infrastructure (Armenia)

Free books we recommend

This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data.

This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding experience. This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would.

My favorite aspect of ModernDive, if I must pick a favorite, is that students gain experience with the whole data analysis pipeline (see Figure 0.2). In particular, ModernDive is one of the few intro stats textbooks that teaches students how to wrangle data. And, while data cleaning may not be as groovy as model building, it’s often a prerequisite step! The world is full of messy data and ModernDive equips students to transform their data via the dplyr package.

Speaking of dplyr, students of ModernDive are exposed to the tidyverse suite of R packages. Designed with a common structure, tidyverse functions are written to be easy to learn and use. And, since most intro stats students are programming newbies, ModernDive carefully walks the students through each new function it presents and provides frequent reinforcement through the many Learning checks dispersed throughout the chapters.

Until recently, nearly every computer program that we interact with daily was coded by software developers from first principles. Say that we wanted to write an application to manage an e-commerce platform. After huddling around a whiteboard for a few hours to ponder the problem, we would come up with the broad strokes of a working solution that might probably look something like this: users interact with the application through an interface running in a web browser or mobile application; our application interacts with a commercial-grade database engine to keep track of each user’s state and maintain records of historical transactions; and at the heart of our application, the business logic (you might say, the brains) of our application spells out in methodical detail the appropriate action that our program should take in every conceivable circumstance.

To build the brains of our application, we would have to step through every possible corner case that we anticipate encountering, devising appropriate rules. Each time a customer clicks to add an item to their shopping cart, we add an entry to the shopping cart database table, associating that user’s ID with the requested product’s ID. While few developers ever get it completely right the first time (it might take some test runs to work out the kinks), for the most part, we could write such a program from first principles and confidently launch it before ever seeing a real customer. Our ability to design automated systems from first principles that drive functioning products and systems, often in novel situations, is a remarkable cognitive feat. And when you are able to devise solutions that work 100% of the time, you should not be using machine learning.