Analytics and Consumer Science

Despite the growing availability and analysis of large data sets, there are many obstacles blocking the path to truly unlocking big data’s potential. According to a study by the McKinsey Global Institute, the United States will face a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.

Analyze Corporation offers the necessary data science skills that businesses are lacking. We provide novel solutions designed for each businesses specific needs. We offer solutions for data collection, data organization, advanced analysis, data visualization, and scalability.

Data Science as a Service™ is Analyze Corporation’s unique approach to providing businesses and government agencies with the most advanced and efficient big data and data science analytics. Our solutions can keep you ahead of the competition, increase your revenue, and improve your operation efficiency.

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Advanced Analytics Solutions

Meeting your business needs by using the best open source tools and techniques. Some examples include:

  • Social Media/Social Network Analysis
  • Behavior Analysis Motion and Geospatial Analytics
  • Natural Language Processing

Big Data Ecosystem Support

Leveraging a variety of open source big data and analytics ecosystems. Some examples include:

  • Apache Hadoop
  • NoSQL/Hbase/Mongo/Hive
  • Machine Learning/Weka/Mahout
  • Custom Analytics in Python and R

Predict the location and behavior of your data, and draw meaningful conclusions from their motion. We understand which techniques are best applied to a particular problem.

Analytics and Consumer Science

Graph Theoretical and Network Techniques

Graph theory is a rich mathematical discipline with established solutions to many common problems such as connectedness, pathfinding, distances, and probabilistic prediction. Interconnectedness in human relationships can also be explored effectively through graph theoretical techniques.

Geometric and Pattern Matching Techniques

Sometimes motion is expressed in regular, predictable, or distinctive mathematical or geometric patterns. Shape and object recognition techniques, series pattern detection and geometric algorithms help us understand, organize, and classify motion.

Machine Learning Techniques

Machine learning techniques help make sense of complex, unorganized, and random data sets. With the combination of clustering, correlation, and dimensionality you can predict what an object in motion should do based on past behavior. It can also detect irregular behavior from an object.

Geospatial Analysis and Illegal Fishing

Illegal fishing results in approximately $20 billion in economic losses annually and causes significant environmental challenges for countries around the world. To solve this problem, Analyze created a complex algorithm with geospatial data to identify illegal fishing behaviors.

To solve this problem, Analyze had to address multiple questions while working with an abundance of geospatial data. For example, why does one vessel take a seemingly inefficient route between two places in perfectly good weather? Or, why do two fishing vessels from different countries rendezvous on the open ocean?

This approach promises to be a factor in suppressing illegal fishing in the world, gaining economic value and reducing environmental challenges.