Software Product Engineering Services

We cover all aspects of the software engineering process, starting with your strategy, allowing you to remain focused on your core business and market.

We design, develop and enhance software products.

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Data Science Solutions

The ultimate goal of Data Science is to help organizations make better decisions to gain competitive advantages and maximize their effectiveness, moving from intuitive to data-driven decision-making.

We’re here to help you develop your Data Science strategy and harness the hidden value in structured and unstructured data.


Pick your industry to learn how ELEKS Data Scientists tackle the toughest business problems

ELEKS has helped many organizations maximize their effectiveness by implementing data science to solve common and complex business challenges. Applying predictive analytics, predictive modeling, and Big Data processing practices has resolved many industry-specific challenges including: new product development, process efficiency, financial risk management, customer experience enhancements, improved cross-selling, price optimization, and many more.

Our Approach

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Data Visualization

At ELEKS, we believe visualization is the key to data-driven organizations achieving efficiency and effectiveness in their value creation process -- improving the effectiveness of daily decision making. Analytical dashboards and real-time visualizations answer specific business questions asked at the beginning of data science projects. Meaningful visualization helps decision makers focus on the most important issues in their business, as well as create available time to interpret results from project modeling stages to reaffirm decisions or set a new course of action.

Big Data Processing

When developing mission-critical Big Data solutions, our team of data scientists and engineers thoroughly analyzes business and technical requirements in each step of the data processing pipeline. Considering various technical requirements, we choose the most appropriate data processing approach:

  • Traditional ETL for a large variety of structured sources
  • Distributed systems (Flume or Kafka) for collecting events from multiple sources
  • NoSQL solutions for storing billions of records, petabytes of data
  • Hadoop Map Reduce paradigm processing of massive amounts of data on commodity hardware, reducing the Total Cost of Ownership (TCO) of data storage and processing
  • In-memory alternative Spark framework, Hadoop is not enough, which share the same Map Reduce paradigm

Often Big Data processing alone is not sufficient to solve a business problem. To derive useful insights from large chunks of raw data, our experts in math, statistics, and machine learning apply predictive modeling to support data-driven decision-making. Deep learning algorithms such as deep belief networks, convolutional neural networks, and deep Boltzmann, are supported by our experience in High Performance Computing (HPC). Using GPGPU to train highly dimensional neural networks allows us to quickly train hundreds of models with different hyper parameters, and deploy them for customers’ mission critical applications.

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