Data Science
Data science services assist businesses in conducting tests on their data in quest of commercial insights. To suit our clients’ most specific analytics needs, Primoris Systems offers data science consulting services utilising machine learning, artificial intelligence, and deep learning technologies.
Use Cases Primoris Systems Offers Data Science Services
Operational Intelligence
Enhancing process performance via the identification of deviations and undesired trends, the investigation of their underlying causes, and the forecasting and prediction of performance.
Supply-chain Management
Supply chain management is improved by accurate demand forecasts, suggestions for inventory optimisation, and supplier- and risk assessments.
Product Quality
Proactive detection of manufacturing process variances that impact product quality and interruptions.
Predictive Maintenance
Monitoring equipment, spotting and documenting trends that indicate pre-failure and failure stages.
Dynamic Route Planning
On the examination of vehicle maintenance data, real-time GPS data, route traffic data, road maintenance data, weather data, etc., machine learning algorithms are used to select the best delivery route.
Personalising the Customer Experience
Identifying trends in client behaviour and segmenting the customer base to create recommendation engines, provide individualised offerings, etc.
Customer Attrition
Identifying potential clients who could leave by creating predictions based on their behaviour.
Sales Process Improvement
Advanced lead and opportunity scoring, suggestions for the next stage in the sales process, alerts on unfavourable customer feedback, etc.
Financial Risk Management
Estimating project earnings, identifying financial risks, and determining the creditworthiness of potential clients.
Optimisation of Patient Care
Identifying at-risk individuals, enabling individualised medical care, foreseeing potential symptom emergence, etc.
Image Evaluation
Reducing human mistake through automated grading, counting, and face or emotional identification.
What We Offer in Data Science Services
1. Business needs evaluation
- Defining the business goals that data science will help to achieve
- Identifying drawbacks of the current data science solution
- Deciding on the deliverables for data science
2. Data preparation
- Data science source determination
- Gathering, transforming, and cleaning data
3. ML model evaluation and tuning
4. Design and development of machine learning (ML) models
- Data science insights in the form of reports and dashboards are ready for commercial usage.
- Self-service app powered by custom ML (optional).
- Integration of ML models into other applications is optional.
5. Data science support consultations, user and administrator training
Models of Collaboration We Provide
Use of data science solutions
- Simple access to the knowledge or tools needed
- Constructing a data science solution that meets your specific company objectives and runs successfully
Consultancy for data science improvement
- Tactical and strategic advice.
- Overcoming issues in a data science project (noisy or filthy data, erroneous projections, etc.)
Ongoing Advice and Assistance in Data Science
- Support and development of your data science effort throughout time to improve the calibre of insights.
- Adapting the ml models to the environment’s changing needs
Data science as a service (DSaaS)
- There is no investment in internal data science capabilities.
- Obtaining sophisticated data analytics insights produced by data science technology or improving the data science projects already in place.
Technologies and Methods, We Employ
We use sophisticated machine learning algorithms, such as deep neural networks with 10+ hidden layers, as well as tried-and-true statistical methodologies to uncover the useful insights that your data conceals. Methods we use are as:
Statistical Techniques
- Statistically descriptive
- ARIMA ARMA
- Bayesian analysis, etc.
Machine learning techniques Non-NN
- Algorithms for supervised learning, such as support vector machines, decision trees, and linear and logistic regression
- Algorithms for unsupervised learning, such hierarchical and K-means clustering
- Techniques for reinforcement learning, including Q-learning, SARSA, and the temporal differences approach
Deep Learning and Neural Networks
- Recurrent and convolutional neural networks, including LSTM and GRU Autoencoders
- GANs, or generative adversarial networks
- DQN, or Deep Q Network
- Deep Bayesian learning
We Provide Related Data Science Services
ML-powered solutions are advised and developed to assist businesses in locating hidden patterns in vast amounts of data to allow accurate forecasting, root-cause analysis, automated visual inspection, etc
To assist businesses with storing and processing big data in real-time and extracting advanced analytics insights from sizable datasets, big data consultancy, implementation, support, and big data as a service are available
The creation of specialised image analysis software
Obtaining insightful data from vast, diverse, and dynamic data sets without hiring in-house data mining specialists