Data Science / Analytics

Engenius offers a comprehensive range of services for data science projects.  Our typical data science project involves conducting background research, developing grant applications, collecting, cleaning, exploring, and mining data, developing machine learning models, validating and testing these models, deploying them, and filing patent applications.  Engenius provides clients with the flexibility to choose between commercial software and open-source tools, as well as data warehousing systems that are tailored to meet their specific project needs and budget.  With our expertise in data science and innovative solutions, Engenius is committed to delivering outstanding results and providing clients with a competitive edge in their respective industries.


Data Intelligence

Nowadays, data is in abundance.  When data is put into proper use, we can make more informed decisions, gain insights and generate predictions or forecasts.  Using data mining techniques, statistics, and visualization tools, we turn data into important insights.  Data science/analytics can be used in many different applications:

Technical Innovation and a Modern Thought Concept

Business Intelligence

Artificial Intelligence & Machine Learning

Healthcare Analytics

ML in Oil & Gas

Machine learning (ML) is becoming increasingly popular in the oil and gas industry, as it offers a range of valuable applications that can help companies optimize their operations and reduce costs.  Today, a common application of ML is reservoir characterization.  By analyzing well logs, seismic data, and other geoscientific data using ML algorithms, companies can build more accurate models of subsurface reservoirs.  This can help them identify new oil and gas reserves, optimize drilling operations, and reduce exploration costs.  ML can also be applied to production operations by optimizing production rates based on field conditions and market demands.  Additionally, ML can be used to improve safety, by analyzing data from safety incidents and near-misses to identify trends and develop preventative measures.

Data Mining

Machine Learning Model Development Process

Data Acquisition

collect data from different sources including databases, historical records, sensors, or the Internet

Data Exploration

including data wrangling, clean, pre-processing, dimension reduction and feature selection

Visualization & Descriptive Analytics

perform statistical analysis & data visualization to gain insights from the data

Machine Learning Modelling

train, validate, test and improve model accuracy

Predictive Analytics

deploy the ML model to make predictions on future outcomes