Data Science

Data science services help companies run experiments on their data in search of business insights. ScienceSoft renders data science consulting leveraging Machine Learning, Artificial Intelligence, and Deep Learning technologies to meet our clients’ most deliberate analytics needs.

Use Cases Sukrom Covers with Data Science Services

Optimizing process performance due to detecting deviations and undesirable patterns and their root-cause analysis, performance prediction and forecasting.

Optimizing supply chain management with reliable demand predictions, inventory optimization recommendations, supplier- and risk assessment.

Proactively identifying the production process deviations affecting product quality and production process disruptions.

Identifying customer behavior patterns and performing customer segmentation to build recommendation engines, design personalized services, etc.

Identifying at-risk patients, enabling personalized medical treatment, predicting possible symptom development, etc.

Cooperation Models We Offer

Data science solution implementation
  • Easy access to the required experience or resources.
  • Building a smoothly functioning data science solution tailored to your unique business needs.
Ongoing data science consulting and support
  • Continuous support and evolution of your data science initiative to increase the quality of insights.
  • Adjusting the ML models to the changing environment.
Data science improvement consulting
  • Strategic and tactical guidance.
  • Overcoming problems (noisy or dirty data, inaccurate predictions, etc.) in a data science project.
Data science as a service (DSaaS)
  • No investment in in-house data science competencies.
  • Getting advanced data analytics insights derived with data science technologies or enhancing the existing data science initiatives.

Methods and Technologies We Use

  • Descriptive statistics
  • ARMA
  • Bayesian inference, etc.
  • 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, temporal differences method.
  • Convolutional and recurrent neural networks (including LSTM and GRU)
  • Autoencoders
  • Generative adversarial networks (GANs)
  • Deep Q-network (DQN)
  • Bayesian deep learning
Visit Us A-13, Aalap Avenue,
University Road,
Rajkot - 360005

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