of Computational Sciences
Analytics and Data Science + Data Engineering + Data Visualization
Computational Sciences are about the business of extracting valuable information from data. They require analysis, interpretation and programming skills, tools and methods from statistics, mathematics and machine learning, and the ability to identify cause and likely outcomes.
Data Science. Data science combines the use of theoretical, mathematical, computational and other practical methods to study and evaluate data. The key objective is to extract required or valuable information that can be used for decision making that is descriptive, predictive, or prescriptive in nature. Computational science concepts and processes are derived from data engineering, statistics, econometrics, epidemiology, programming, social engineering, machine learning and natural language processing, among others.
Data Engineering. Data is a strategic asset. Knowesis has supported Federal clients through their journeys from traditional on-premises data warehousing (extract, transform, load, or “ETL” approach) to cloud-based data lake (extract, load, transform, or “ELT”) approach). Data lake provides a 360-degree analytics platform for both structured and unstructured data for machine learning and big data analytics in mind, where the data can be accessed faster with high throughput speed and can scale exponentially.
Data Visualization and Enterprise Reporting. Our visualization solution uses contextual visual design to create engaging, interactive content that distills large amounts of data as well as enabling them to look at their underlying components. Our technique integrates data from different sources into one highly interactive display so that users can quickly and effectively see the ‘big picture’ hidden in reams of data and volumes of reports. In addition, Knowesis also employs data journalists to develop infographics to visually story-tell complex quantitative data.
How intuition and business acumen blend; how storytelling and visualization drive business outcomes. Our data scientists and engineers realize real-world results drive the investment in data. Our approaches are based on achieving the business result
first and scaling the solution in keeping with the organizations ability to absorb and capitalize upon it.
Computational Science Practices
Artificial Intelligence and Machine Learning
Modeling and Simulation
IMPRINT is our unique implementation process that addresses the most critical aspect of decision science, the business context. IMPRINT uses immersion techniques like those found in intensive language study programs whereby they are embedded in the day-to-day work of the business function. Using these templates, tools and techniques, we are quickly able to ‘speak’ the language of the organization and understand the business challenges at hand. IMPRINT is designed specifically for federal agency operational requirements. Post implementation, the IMPRINT analytic product development cycle, provides a common framework for any analytic endeavor, from augmented analytics, predictive modeling and visualization to program evaluations and social science research.
Descriptive, Predictive and Prescriptive Modeling | Data Visualization | Statistical Support Services
Natural Language Processing | Artificial Intelligence | Computational Programing Skills (R, Python, SQL, Java, Julia)
Data Science Training and Community Leadership | Data Engineering | Enterprise Reporting
Decision Science Computing Platform Architecture (AWS Hadoop, Hive, Spark) | Infographics
Self Service Business Intelligence | Metric Development | Dashboard Development