Data Analytica
People, Infrastructure, & Algorithms

What gets analyzed and mined gets managed
The objective is to convert data into facts and facts into insights and assets. We at Data Analytica focus on innovative and comprehensive Big Data, data analytics, data mining, robotics, and operations research related projects. We fuse business and technology and focus on comprehensive data solutions that are based on sound business, science, and engineering principles. 

We specialize in customizing all your business data needs into a comprehensive, transparent, and cost effective solution. We do this by applying business and technology strategies applicable to a particular customer solution to provide our clients with a wide spectrum of vertically and horizontally aligned business data solutions.
  1. Analytics & Data Mining
    The objective of a data mining project is to extract knowledge from a data set. In this context, knowledge refers to interesting patterns that are valid, unknown, novel, useful, and can be understood by humans.
  2. Machine Learning
    Machine Learning
    ML is basically an AI technology that provides systems with the ability to learn without being explicitly programmed. ML focuses on the development of applications that can adjust when exposed to new data.
  3. Research
    We develop new & advanced ML algorithms for predictive analytics or workload schedulers. We work on humanoid robots and autonomous vehicles (land, water, air) focusing on advancing SLAM algorithms.
  4. Training
    We provide customized AI and ML training, focusing or real-world Big Data analytics and data mining projects. We further teach basically the entire Apache Big Data stack to our clients.
Analyzed Data Equals Understanding

Data reflects the new world-wide business currency

The to-be processed and analyzed data volume encountered by almost any company is skyrocketing. Some companies are struggling with integrating all the data into an analysis framework while other companies do not trust their own data sets. Big Data basically refers to the fact that the current tool-sets cannot be utilized anymore to conduct an analysis and extract value. In other words, the current techniques that are used to work with the data sets are running out of steam. The Data Analytica philosophy is that just because a company can store all the raw data does not imply that a company should do it. Hence, we aid in filtering the data starting at the source and adding value via machine learning at every step along the way. 

Descriptive Analytics

Predictive Analytics

Data Analytics & Mining

Descriptive analytics basically describes the simplest form of analysis. The focus is on condensing the large datasets into smaller constructs that contain actual value (summarize the contents).  A lot of social analytics studies utilize some form of descriptive analytics.
Predictive analytics is used to predict (in a probabilistic manner) what may happen down the road. Techniques such as statistics, data mining, or machine learning are used to develop the prediction models. Most sentiment analysis studies are based on predictive analytics, techniques to study recent and historical data, thereby allowing analysts to make predictions about the future.
While both, Data Analytics and Data Mining aim at information that is actionable, Data Analytics and Data Mining are rather different. Data Analytics projects usually encompass hypotheses testing. The analyst has something in mind and is focused on answering a question, but he/she has a hypotheses about the outcome. Data Mining on the other hand represents the act of discovery that in most cases lacks a hypotheses. Data Mining seeks for patterns (often by processing vast amounts of data), but these patterns and relationships were not previously known or anticipated.