How it works
We provide services and tools grounded in robust statistical, artificial intelligence and data science methodologies, designed to autonomously pinpoint the best tailor-made predictive modeling solution.
Frequently asked questions
Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It encompasses a broad range of techniques from statistics, data analysis, machine learning, and computer science to analyze and interpret complex data. Business Intelligence, Predictive Analytics, Machine Learning, and Deep Learning are all tools and methodologies within the broader scope of data science that can be used to solve specific problems, uncover insights, and drive decision-making.
Business Intelligence refers to the technological and procedural infrastructure that collects, stores, and analyzes the data produced by a company's activities. BI encompasses a wide range of tools, applications, and methodologies that enable organizations to collect data from internal and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards, and data visualizations. This process supports decision making in business operations, strategic planning, and performance management.
Predictive Analytics uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to provide a best assessment of what will happen in the future. This can be particularly useful in areas like customer relationship management, risk management, and inventory forecasting.
Machine Learning is a subset of artificial intelligence (AI) that focuses on building systems that learn from data. Instead of being explicitly programmed to perform a task, ML models are trained using large sets of data and algorithms that give them the ability to learn how to perform the task. ML can be used for a wide range of applications, including predictive analytics, natural language processing, and image recognition.
Deep Learning is a subset of machine learning that uses neural networks with many layers (deep neural networks) to model complex patterns in data. It is particularly well-suited for tasks involving large amounts of data and complex patterns, such as image and speech recognition, language translation, and autonomous driving.
Big Data refers to datasets that are so large or complex that traditional data processing software is inadequate to deal with them. Big Data is characterized by the three Vs: Volume (the quantity of data), Velocity (the speed at which data is generated and processed), and Variety (the different types of data). Big Data technologies, such as Hadoop and Spark, are used to store, process, and analyze these datasets.
A Neural Network is a series of algorithms that mimics the operations of a human brain to recognize relationships between vast amounts of data. Modeled loosely on the human brain, it consists of layers of nodes, or "neurons," which process information using dynamic state responses to external inputs. Neural Networks are a foundational technology in deep learning applications.
Artificial Intelligence is a branch of computer science that aims to create systems capable of performing tasks that would typically require human intelligence. Data Science plays a crucial role in AI by providing the methodologies and data necessary for these systems to learn and improve. Essentially, data science can be seen as the foundation upon which AI models and algorithms are built and trained.
Predictive Analytics helps businesses forecast future trends, behaviors, and events with a significant degree of accuracy. It enables proactive decision-making, risk management, and strategic planning, leading to improved operational efficiency, customer satisfaction, and competitive advantage.