Data Science - Business Analytics - BI - Statistics
Discover patterns and build predictions using data and algorithms with machine learning and artificial intelligence.
Why to work with data?
Why you will choose us?
Data Science is the science that works on the study of data. It is responsible for extracting information from large amounts of data. Data Science combines statistics, mathematics, and computer science to interpret data. The goal is to make decisions.
This data is obtained through different channels. Mobile phones, social networks, e-commerce or surveys are just some of the sources used. Our tastes, routines or movements generate data of great value for companies that want to know their customers in detail. However, the interpretation of unstructured data does not add value to companies. Hence the need for data scientists on their teams. Thanks to Data Science, companies can anticipate when making decisions.
We leverage proven analytics methodologies, best practices and tools to define the right analytics solutions for you, that solve complex business challenges and drive future growth.
We blend Data Science/ Analytics with a business focused strategic outlook to ensure our clients achieve *ROI from their various analytics initiatives.
*Return on Investment (ROI) is a performance measure used to evaluate the efficiency of an investment or compare the efficiency of a number of different investments. … To calculate ROI, the benefit (or return) of an investment is divided by the cost of the investment. The result is expressed as a percentage or a ratio.
We help clients understand their current analytics maturity, and provides a comprehensive data and analytics roadmap to develop a culture of data-driven decision making.
We empower our clients to quickly discover and gain insights from their business data. Our key offerings includes; Ad-hoc and Insightful Reporting, Intelligent Dashboards, Data Visualization, ***OLAP Cube Design and Implementation.
**Ad hoc reporting is a business intelligence process in which dynamic, real-time data reports are created by the user on an as-needed basis. They are designed to answer a specific business question, usually in response to an event
***An OLAP cube is a computer-based technique of analyzing data to look for insights. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than 3.
How it’s works?
There are a number of steps to follow. Below a brief explanation of what they consist of.
Data Mining is as a process used to collect and store useful data. For this, it is necessary to analyze data patterns in large batches using one or more software. Thanks to this process, companies can obtain more information about their customers and develop more effective strategies. This helps them make better information-based decisions. To segment the data and evaluate it, Data Mining uses mathematical algorithms.
The goal of Deep Learning is to solve problems through neural networks that mimic the behavior of the brain. These networks of artificial neurons are structured in layers. The first layer is where the information is captured. This data goes to the next layer, in charge of performing calculations. And finally, the collected information is projected on the last of the layers. Some of the most used applications in Deep Learning are word processing and image, object or speech recognition.
First of all, it is necessary to emphasize that Machine Learning is not synonymous with artificial intelligence. Rather, it is a concept framed within it. Fundamentally, Machine Learning is in charge of educating technology to correct errors on its own. It is based on the prediction and classification of data to obtain useful information applicable to different areas.
ARTIFICIAL INTELLIGENCE (AI).
It is based on algorithms used to create machines that mimic human behavior. Today, emotional intelligence is applied in facial recognition or in the creation of chatbots, among others.