DIVE INTO THE NEW AGE OF ACCELERATED ANALYTICS

OUR SERVICES

DATA PROCESSING, ANALYSIS, AND VISUALIZATION

In today's digital age, data has become an invaluable resource for organizations and individuals. But how to use this huge amount of data and turn it into useful information? The answer lies in data processing, analysis and visualization.

Data processing

The first step in obtaining valuable insights from data is collection and processing. Data processing refers to cleaning, transforming and organizing data to make it usable for further analysis. This includes removing duplicates, filling in missing values, and formatting. Data processing ensures that the data is reliable and usable.

Data analysis

After processing, we come to the core - data analysis. Here, various research techniques and tools are used to identify patterns, trends and key insights. Data analysis can include statistical methods, machine learning, deep analysis and many other techniques. The goal is to understand what the data is saying and how this can be applied to make better decisions.

We provide following types of data analysis:

Descriptive analysis: understanding basic patterns in data and a detailed overview of key statistical indicators.
Inferential analysis: making inferences and drawing conclusions on sample data.
Machine learning: models for data prediction, classification and segmentation.
Time Series Analysis: understanding and predicting time patterns and trends in data.
Data visualization: visual representations of data for easier results understanding and communication.

We process, analyze and visualize data in R, a programming language for computing and graphics, and we use the RStudio integrated development environment.

For generating reports in html, pdf, ms word, dashboard, we use R Markdown.

MACHINE LEARNING

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that focuses on the development of algorithms and models that enable computers to learn and improve their performance without being explicitly programmed. The core idea behind machine learning is to allow systems to automatically learn and adapt from experience or data, identify patterns, and make decisions or predictions. ML is used to find patterns and information that are difficult or impossible to observe using traditional statistical or analytical methods. It enables experts to better understand data, make informed decisions and extract valuable insights from data.

In data analysis, ML is used for the following purposes:

Classification: ML algorithms can categorize data into different classes or groups. Classification of e-mails (spam or non-spam), classification of clients into different segments based on their characteristics, detection/prediction of customer churn.
Regression: Prediction of numerical values based on historical data. This is useful in scenarios such as predicting sales figures, stock prices, tree growth, damage from natural disasters such as storms, fires or floods in forest areas or other continuous variables.
Clustering: Grouping similar data together to identify natural patterns in data (identifying groups of similar users based on their behavior).
Time Series Analysis: Time series forecasting, such as stock market prices, consumer habits over time, meteorological data.
Anomaly detection: ML models can identify anomalies or outliers in datasets, which may indicate errors, fraud, or unusual patterns. This is crucial in maintaining data quality and security.
Natural Language Processing (NLP): A subset of machine learning for analyzing and understanding human language. It is used in sentiment analysis, text summarization, and language translation contributing to text-based data analysis.
Computer vision: It enables machines to analyze and make decisions based on visual data, similar to how humans perceive and interpret images and videos (objects, faces or movements in images and videos).

Our machine learning service offering includes classification, regression, clustering and time series analysis.

For building ML models we use h2o.ai (automated ML), an advanced open access machine learning tool available in the R programming language, and for the interpretation we use LIME (Local Interpretable Model-agnostic Explanations).

SHINY WEB APPLICATIONS

Shiny web applications are interactive applications based on R programming language and the Shiny framework. They provide analysis, visualization, data sharing, and analytical results via a web browser.

A few key features of Shiny web applications:

Interactivity: Users can interact with the application through various input elements, such as buttons, drop-down menus, text input fields, and graphs that respond to actions.
Data Analysis: Shiny applications are often used for data analysis. Users can upload their data, perform statistical analysis and generate graphs or tables to better understand their data.
Data visualization: Shiny allows you to create different types of graphs and visualize data. This is extremely useful for displaying data in a way that makes it easier to understand and make decisions.
Adaptability: The Shiny framework allows adjusting the appearance and functionality of the application upon the user needs and wishes. Colors, styles, layout and more can be customized.
Responsiveness: Shiny apps are "reactive" meaning they automatically update to changes in user input. This enables fast and dynamic data analysis.
Sharing results: Shiny applications allow easy sharing of analysis results with others through a web browser which is especially useful for team collaboration and communication.
Development and deployment: Shiny applications can be developed locally and deployed on a web server or in the cloud, depending on the needs and requirements.

We offer the service of implementing Shiny applications by placing them on a cloud server so that they are available on the web. In doing so, we use Amazon Web Services (AWS), Git and Docker technologies.