Data Science Consulting Services
100+
Satisfied and Happy Clients we have Served all over the World.
What We Do
Evolve Your Business While Evolving Your Data Landscape as you Hire Data Science Consultants
The increasing volume of data & its complexity can be simplified with Tableau – a BI and visual data analytics suite, to help Businesses & people make informed decisions. Data Science with us for data management, insightful analysis, & effective visual presentation that are fast, convenient, & result-oriented to establish a Data Culture & spur change. With our Tableau developers, turn your data insights into value-based actions.
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Data Science Services We Offer
Data science consulting
Whether you need an ML model to solve a specific business task or plan to implement a complex data science solution, our industry analysts and data scientists are ready to provide you with an exhaustive consultation. With a detailed project roadmap and an optimal tech stack from us, you will get actionable steps to turn data into a value driver.
Data science solution implementation
Our experts build industry-centric data science solutions that foster informed decision-making, streamline operations, eliminate human work, increase safety, enhance customer experience, and ensure other data-driven benefits. For complex projects, we are ready to start with a PoC
Data science evolution
Our experts will provide you with strategic and tactical guidance if your data science solution needs to meet new challenging goals. We will design and implement new ML models, software features and modules to reinforce your solution with extra ML-powered capabilities.
Data science solution support
We regularly check your ML models for accuracy and adjust them to ensure high-quality insights and predictions. With proactive monitoring and efficient issue resolution by a trusted IT partner, you can be sure your solution functions seamlessly.
Complementary Data Science Services We Offer
Machine learning consulting
Advising on and developing ML-powered solutions to help companies find hidden patterns in massive amount of data to enable accurate predictions and forecasting, root-cause analysis, automated visual inspection, etc.
Big data services
Big data consulting, implementation, support, and big data as a service to help companies store and process big data in real-time as well as retrieve advance analytics insights out of huge datasets.
Image analysis services
Designing and developing custom image analysis software.
Data mining services
Retrieving valuable insights out of large, heterogeneous and constantly changing data sets without investing in in-house data mining talents.
Business intelligence
Helping companies achieve informed decision-making and optimize processes through data-driven insights.
Data warehousing
Consolidating disparate data into a single point of truth as the background for enterprise-wide analytics and automated reporting.
How Data Science Process Unfolds with ScienceSoft
Business needs analysis.
- Outlining business objectives to meet with data science.
- Defining issues with the existing data science solution (if any).
- Deciding on data science deliverables.
Data preparation.
- Determining data source for data science.
- Data collection, transformation and cleansing.
Machine learning (ML) model design and development.
- Choice of the optimal data science techniques and methods.
- Defining the criteria for the future ML model(s) evaluation.
- ML model development, training, testing and deployment.
Delivering data science output in an agreed format
- Data science insights ready for business use in the form of reports and dashboards.
- Custom ML-driven app for self-service use (optional).
- ML model integration into other applications (optional).
Use Cases ScienceSoft Covers with Data Science Services
Operational intelligence
Optimizing process performance due to detecting deviations and undesirable patterns and their root-cause analysis, performance prediction and forecasting.
Supply chain management
Optimizing supply chain management with reliable demand predictions, inventory optimization recommendations, supplier- and risk assessment.
Product quality
Proactively identifying the production process deviations affecting product quality and production process disruptions.
Predictive maintenance
Monitoring machinery, identifying and reporting on patterns leading to pre-failure and failure states.
Dynamic route optimization
ML-based recommendation of the optimal delivery route based on the analysis of vehicle maintenance data, real-time GPS data, route traffic data, road maintenance data, weather data, etc.
Customer experience personalization
Identifying customer behavior patterns and performing customer segmentation to build recommendation engines, design personalized services, etc.
Customer churn
Identifying potential churners by building predictions based on customers’ behavior.
Sales process optimization
Advanced lead and opportunity scoring, next-step sales recommendations, alerting on negative customer sentiments, etc.
Methods and Technologies We Use
To get to the valuable insights that your data hides, we apply both proven statistical methods and elaborate machine learning algorithms, including such intricate techniques as deep neural networks with 10+ hidden layers.
Methods
Statistics methods
- Descriptive statistics, e.g., to summarize customer data, identify outliers in stock prices, visualize equipment performance data.
- ARMA and ARIMA, e.g., to forecast sales, prices, demand, etc.
- Bayesian inference, e.g., to predict possible outcomes like equipment failure or disease likelihood and model spatial patterns.
Non-NN machine learning methods
- Supervised learning algorithms are good for classification and regression tasks, e.g., diagnosing based on image analysis or stock price prediction.
Unsupervised learning algorithms are good for clustering tasks, e.g., segmenting customers based on their purchase history or detecting fraudulent financial transactions.
Reinforcement learning methods are good for decision-making influenced by interaction with the environment, e.g., personalization engines responding to user behavior.
Neural networks, including deep learning
- Convolutional and recurrent neural networks (including LSTM and GRU), e.g., for NLP tasks.
- Autoencoders, e.g., to analyze medical images.
- Generative adversarial networks (GANs), e.g., to generate images that will be used for training ML algorithms.
- Deep Q-network (DQN), e.g., to optimize energy consumption, to recommend the best settings for manufacturing equipment.
- Bayesian deep learning, e.g., to improve speech recognition and translation accuracy.
