Data science services assist companies in conducting experiments on their data in order to discover business insights. PRAGMA INTERNATIONAL utilizes Machine Learning, Artificial Intelligence, and Deep Learning to assist with the most deliberate analytics needs of our clients.
PRAGMA INTERNATIONAL’s data science services are utilized in the creation of cases.
Operational intelligence

Improving the efficiency of a process by identifying irregularities and undesirable patterns and their causes, predicting and forecasting the performance of the process.

Supply chain management

Developing a supply chain that is efficient, cost-effective, and has a predictable demand. Additionally, recommendations for inventory optimization, supplier risk assessment, and supplier selection are made.

Quality

Proactively identifying the causes of deviations in quality and disruptions in the production process.

Predictive Maintenance

Monitoring equipment that identifies and reports on patterns that lead to failure before it occurs.

Dynamic route optimization

A system that utilizes ML to generate a recommendation for the most efficient route based on the analysis of vehicle maintenance data, real-time GPS data, traffic data, road data, weather data, etc.

Customer experience customization

Understanding customer behavior and creating customer segments to build recommendation engines, design personalized services, etc.

Customer dissatisfaction

Identifying potential customers who will leave by using historical data to predict their behavior.

Sales process improvement

A variety of lead scoring methods, next-step recommendations, notifications regarding negative customer sentiments, and other methods of lead nurturing.

Financial risk management

Project forecasting, evaluating financial risks, assessing a potential investor’s creditworthiness.

Patient-centered care

Identifying patients who are at risk, providing personalized medical care, predicting the potential development of symptoms, etc.

Image analysis

Implementing automated visual inspection, facial or emotional recognition, grading, and counting.

Our data science services include:

Business Planning
  • Describing the goals of a business in order to interact with data science.
  • Describing the problems associated with the existing data science solution.
  • Deciding on the deliverables of data science.
Data Collection
  • Identifying the source of data for data science.
  • Data collection, transformation and cleansing.
The Application of ML to Model Design and Development
  • The choice of the most effective data science techniques and methods.
  • Discussing the criteria for the future evaluation of the ML models.
  • I have experience with the development, testing, deployment and maintenance of the MQL model.
ML Model Evaluation and Tuning

Providing a standardized output format for data science.

  • Data science knowledge that is useful for business purposes in the form of reports and charts.
  • Create an app that is driven by ML that allows customers to self-serve (this is optional).
  • The ability to integrate the model into other applications is optional.
User & Admin Training “Data Science Support Consultations”

We have several models of cooperation that we can offer.

Data Science Implementation

  • Easy access to the necessary experience or resources.
  • Developing a data-driven solution that is specifically tailored to your business’s needs.
Data Science Improvement Consulting
  • The ability to provide strategic and tactical advice.
  • Resolving issues (such as noisy or inaccurate data, inaccurate predictions, etc.) in a data science endeavor.
Ongoing Data Science Assistance & Consulting
  • Continuous assistance and enhancement of your data science initiative in order to produce more accurate insights.
  • Adjusting the models to account for the changing environment.
Data Science as a Subscription (DSaaS)
  • No dedicated funding for in-house data science skills.
  • Advanced analytics that are derived from data science or enhanced the existing data science initiatives.

We utilize a variety of methods and technologies to accomplish our goals. To uncover the valuable information in your data, we employ both proven statistical methods and complex machine learning algorithms, including such intricate techniques as deep neural networks with 10+ hidden layers.

Statistical Methods
  • Descriptive statistics
  • ARMA
  • ARIMA

These methods are also used to determine the probability of an event occurring, the likelihood of an event occurring, and the like.

Non-NN Methods of Machine Learning
  • Supervised learning algorithms, such as decision trees, linear regression, logistic regression, support vector machines.
  • Unsupervised learning algorithms, for example, K-means clustering and hierarchical clustering.
  • Reinforcement learning methods, such as Q-learning, SARSA, and temporal differences, are employed.
Neural Networks, Including Deep Learning
  • Convolutional and recurrent neural networks (including LSTM and GRU).
  • Autoencoders.
  • Generative adversarial networks (GANs).
  • Deep Q-network.
  • Bayesian deep learning