We solve problems related to data science to improve understanding of business operations and enable sustainable improvement in business performance.
Whether analyizing well-structured historical data or tackling an ambiguous goal, such as improving business performance through the use of reliable, available data, capSpire data science experts can help you better understand your data and use these insights to your advantage. We follow a methodical process of data exploration and assessment, model selection and development, and, ultimately, optimization of performance.
Optimization takes your business to a new level. It helps you operate smarter and more efficiently, lets you take better advantage of market opportunities, and enables you to pivot faster than your competitors when the playing field changes.
- We use state-of-the-art solvers including Gurobi, CPLEX, and open-source solutions.
- Our business background gives us a unique means of translating contracts into systems of equations.
- We integrate live data into existing tools.
- Our best-of-breed visualization technology helps you gain insights from data quicker.
- On-premise solutions or cloud-based systems allow you to scale and respond to business needs faster.
Open Data Science (Kataiku, R, Python)
Open-source data science tools have proven very capable of developing related workloads. Where the biggest challenge arises is in taking an idea from the proof-of-concept stage to production deployment. That’s where capSpire can help.
- Our data science and engineering teams take your idea and design a solution that is capable of running 24/7/365 in production environments.
- We take R and Python code from a desktop and migrate it to a server-side environment to provide more robust controls and governance over the models.
- We take existing data science workloads written in legacy applications or programming languages, or even proprietary ones, and re-engineer them to work in open-source technologies.
Our approach to machine learning ensures full transparency of the models and algorithms used so that we can more easily understand and harness the power of machine learning methods to improve business performance.
- From applying clustering algorithms to data with many features to consturcting marketing segments based on features identified as similar among customers, we can help you target your data-processing efforts and budgets towards solutions that deliver the greatest impact and value to businesses.
- We apply many predictive modeling techniques to help you find predictors for outcomes with unknown causes, from spam detection to new customers.
- We assess the ROI for machine-learning projects. This includes not only quantifying costs and benefits, but determining where the most profitable improvements for the future can be found in improved data, expanded data science teams, or selection of projects.
- When the gains of a machine-learning project have been validated and can be carried forward, we help you implement models into your existing processes through training and code.
Artifical Intelligence (AI)
We provide support in exploring pilot projects and incorporating AI components into your projects.
- We provide detailed research and documentation for a range of platform choices for your AI initiatives, including platform design tradeoffs between ease of use and transparency, performance for training (backpropagation and parameter estimation) and support through capSpire or other vendors.
- We provide data preparation, including feature engineering, normalization, and dimension reduction, for both AI and traditional machine learning.
- Assessing the outcome of AI projects can be challenging give the black-box nature of tools. We assess underlying assumptions, reproducibility, the stability of results, and likelihood for future success to help you choose the next steps beyond your pilot projects.
Time-series analytics is of particular importance to energy companies. Quite often, these companies deal with data that contains a high degree of uncertainty. To forecast, it is necessary to use the following time-series methods:
- We implement solutions that enable the creation of forecasts. For example: ‘What will wind generation output be in the next hour based on past trends?’
- We correlate external factors with time-series data, which helps to increase the accuracy of forecasts.
- We combine AI/machine learning ensemble modeling methods in our framework, which enables us to rapidly prototype and fine-tune multiple models at the same time.
Using traditional statistical analysis, capSpire can help you find relationships and understand diverse data sets from outside sources, processes, or customers.
- We employ descriptive statistics and hypothesis testing because they are powerful techniques for determining if relationships exist in your data and where to pursue process improvements.
- We first define a conceptual model of the problem. Then we deploy approaches such as linear regression models that can specify the exact relationships and tradeoffs with statistical confidence.
- When our conceptual model is less certain, logistic regression can help identify events that may be casual.