Data Science & Advanced Analytics

We help companies of all sizes to drive value from Machine Learning. Our services offering is designed to accommodate different levels of Machine Learning adoption in the company.Our standard engagement types in the Data Science domain include:

ML Roadmap Design

We assist you on the road to embrace ML and AI in your business processes. We work together with your team to identify key improvement areas that can benefit from the use of advanced analytics within your company. We define the infrastructure and competencies needed to achieve the goal and create a game plan. The created roadmap incorporates industry best practices and inputs from your business. In our approach we are always tool-agnostic and first try to maximize the usage of existing IT infrastructure and analytical platforms. If your existing approach to analytics no longer gives competitive advantage we often suggest exploring one of the available ML-as-a-service cloud solutions.

ML Audit and Consulting

Our broad industry experience in delivering analytics to Fortune 500 companies gives us confidence to be able to evaluate and assess your current analytical ecosystem and processes. If your company is struggling with adoption of ML or business benefits from AI are not to par, we are here to help. By evaluating every step of ML lifecycle in your organization we will be able to pinpoint the challenges and prepare a resolution plan. We summarize our findings in a formal report where we also outline recommendations for the organization and outline next steps to be taken. Our findings often include cost cutting guidelines, strategies for improving time to market team. We also define processes limiting amount of rework needed in the ongoing support of Machine Learning products.

Design and Implementation of AI Solutions

Core of our engineering services is delivering end-to-end AI tools that solve business critical problems. We can help your company gain competitive advantage in the following areas:

Team Augmentation

If your goal is to develop Machine Learning competences in-house, we can introduce our Data Science consultants to your team. DS Stream Data Scientist will provide you with the capacity to get the analytical work done faster while training others in the process. Us working in proximity with your in-house staff will enable easy knowledge transfer and present a lot of learning opportunities.

With our extensive practical experience, we can provide comprehensive services for Apache Airflow








Airflow implementation


Testing and debugging


Benefits of Data Science & Advanced Analytics services

Become a data-driven organization with the meaningful insights required to make business decisions immediately.

How can your organization benefit from Data Engineering services?

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Our Clients

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Our Technology Tool Stack

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Data Science & Advanced Analytics FAQ

Data Science is a multidisciplinary field that combines domain expertise, programming skills, and knowledge of mathematics, statistics, and machine learning algorithms to extract insights and make predictions from large and complex data sets.

Big Data refers to the volume, velocity, and variety of data that organizations collect and process. Data Science, on the other hand, is the process of analyzing and extracting insights from Big Data using various techniques and tools.

A Data Scientist is responsible for identifying business problems, framing them as analytical questions, and applying the right analytical techniques to derive insights and recommendations. They work closely with stakeholders to communicate findings and collaborate with developers to implement solutions.

The future of Data Science is bright, as organizations continue to generate more and more data, and the demand for insights and predictions increases. Some emerging trends in Data Science include the use of AI and machine learning algorithms for automation and decision-making, the integration of Data Science with other disciplines like Business Intelligence and Operations Research, and the adoption of cloud-native and serverless architectures for scalability and cost-effectiveness.