Data science services
Generate business insights by gathering and analyzing quality corporate data
Discover unique patterns and relationships within different types of data
Enhance your regular workflow and speed up business processes with advanced AI and ML solutions
Enable stable business progress and continuity with predictive modeling
Optimize operational costs and efficiently allocate business resources with insightful data analytics reports
Tap into advanced data analytics to build feasible digital transformation roadmaps that foster business growth
Data science services Yalantis provides
Data science consulting
Development of AI and ML implementation roadmap
Evaluation of data management processes
Selection of suitable data sources for launching an efficient data science project
Integral support of data science solutions and systems
Make your corporate data work to your advantage
Integrate diverse data sources to collect relevant data and generate business insights that foster effective decision-making.
Industry-specific use cases Yalantis caters to
Predictive maintenance of IoT devices and manufacturing equipment
Data-driven energy management
Cost-efficient vehicle fleet management
Smart home monitoring
Decision support for smart city projects
Patient data analysis for personalized treatment
Clinical research support
Disease prediction and diagnostics
Analysis and prediction of health trends
Patient feedback analysis to improve healthcare service delivery
Timely fraud detection
Behavior analysis and forecasting
AI chatbot for customer support and loyalty
AML Ops automation
Investment risk assessment
Algorithmic trading strategies
Personalized financial plans
Benefits of partnering with our data science services company
Solutions up to industry standards
Develop data science solutions that meet industry needs and help you stay competitive. Improve data management to achieve goals with higher efficiency.
Research and development team (R&D)
Validate any ideas and hypotheses with the support of an experienced data science R&D team. Prepare to present ideas to your internal and external stakeholders to encourage investment.
Top-notch data science team
Bring innovation and the latest trends to your project with experienced data science specialists. Take your data analytics practices to the next level by applying modern techniques and tools.
Build unique custom data science solutions that solve real-world business issues. Select tools and data science techniques that work for your company and ensure ease of use for your employees.
Ensure the complete project lifecycle, from requirements elicitation and model development to solution maintenance and support in the post-release stage. Focus on casting a big business vision while entrusting your technical implementation to a professional team.
Employee training and onboarding
Facilitate the adoption and effective use of data science solutions with a custom training and onboarding program for your employees. Get timely support when functionality issues occur and ensure an uninterrupted workflow.
Yalantis: A company with a proven portfolio
Data lake for a manufacturing company
Learn how Yalantis helped a manufacturing company set up a data lake to increase supply chain visibility and optimize use of manufacturing data.
Platform for real-time election data processing
Find out how we developed a
system for effective election data management to speed up voting results calculations.
Ensure your data management process meets your current needs
Audit your data management practices to check that they don’t hinder your business evolution.
Video reviews of our clients
What triggered us was their remote collaboration practices as well as their experience in the IoT industry. Their strong technical experience helped us scale our platform and deliver great performance to our customers.
Yalantis has been a great fit for us because of their experience, responsiveness, value, and time to market. From the very start, they’ve been able to staff an effective development team in no time and perform as expected.
Working with Yalantis, you get their breadth of experience building hundreds of projects. Their expertise and knowledge were second to none. And that makes the difference between a good product and a great product.
Insights into our data science services
Analytics for IoT: IIoT data management and analytics for large manufacturers
Large manufacturers are gradually jumping to a data-driven operational strategy and utilizing IIoT devices. See how the industry wins by adopting industrial IoT data management.
BI and advanced analytics solutions for supply chain data analysis
Read a guide on BI and advanced analytics solutions for the supply chain. Learn how to implement each one and read about common tools and techniques for both.
Predictive maintenance IoT: how to ensure it for large manufacturers
Learn about the benefits of Internet of Things predictive maintenance for large manufacturers and how a web application can increase its effectiveness.
Create a data-driven business environment
Launch a successful data science project with a skilled expert team that has a deep understanding of your industry needs.
How does your data science agency prepare business data for analysis?
Our data science company follows a regular flow in preparing data for analysis that is typical for most data science projects. The first step is gathering data from sources that are relevant for the analysis and contain the relevant datasets. Our solution architects cooperate with data scientists to set up an uninterrupted data flow and ensure that all data sources provide the necessary data elements in a timely manner.
After gathering data, it’s essential to clean and filter it to validate its quality and integrity and define whether it’s suitable for further manipulations and insightful analysis. For instance, data cleaning can involve removing data duplicates or checking whether critical values are missing. As the next step, data undergoes preprocessing to be fully ready for analysis. With clean and prepared data, our data science service providers proceed to the actual data analysis and build data models and algorithms to generate insights for your business and assist in efficient decision-making.
What technologies and tools do your data science services include?
Yalantis’ data science service providers carefully select tools to use in each particular data science project. The data science technologies and toolset depend on the project specifications, requirements, types of data, and data sources in use. For instance, our data engineers are proficient in the Python and R programming languages for effective data manipulation and analysis. Diverse machine learning libraries such as TensorFlow and Scikit-Learn assist us in creating machine learning models.
Depending on the goals you set for your data science project, such as data classification, exploration, or segmentation, our data scientists conduct a comprehensive study of existing solutions that can be used for your custom project.
How do you maintain data security as a data science development company?
We emphasize the importance of your corporate data security and privacy when delivering our data science consulting services. Primarily, we won’t use any of your data without your consent and without signing an NDA. We are transparent about how your data will be used and request access only to datasets and data sources that are relevant to the project’s execution. To protect your data from unauthorized access, Yalantis data scientists implement role-based access control to ensure each team member can use your corporate data only within their set of responsibilities.
How does your data science services company ensure the accuracy and reliability of your data models?
To achieve high accuracy and reliability for the data models we build, our data science services team applies optimal data science techniques and follows best practices:
- Collecting and processing only high-quality data adhering to the principle of garbage in, garbage out (GIGO), which presumes that if you feed the data model with low-quality data, your results will be low-quality as well
- Transforming data into a suitable format for training data models by applying norms of feature engineering
- Using cross-validation to split data into separate training, validation, and testing sets. Training data is the data the model learns from, and testing data allows for checking how well the model performs. We often use the Scikit-Learn library for data splitting, as it has a convenient function for that.
- Applying model evaluation metrics depending on the problem the data model solves
- Regularly monitoring the data model’s performance to troubleshoot issues in a timely manner
Choosing between internal data science projects and data science as a service: Balancing utility and monetization with our data science service providers
When embarking on a data science journey, businesses may stumble upon a critical decision: whether to develop data science projects for internal corporate use only or offer them as services to external clients for monetization. Yalantis data science service providers can help you evaluate each approach to define the option that will work best for your organization.
Internal data science projects: Enhancing organizational insights
Internal data science projects are focused on addressing an organization’s specific challenges and optimizing its internal processes. The primary goal is to harness data science services to improve decision-making, operational efficiency, and the customer experience. Such projects can lead to a deeper understanding of business trends, customer preferences, and market dynamics.
Advantages of internal data science projects include:
Tailored solutions: Internal projects can be developed to cater to the organization’s unique requirements, ensuring that solutions directly address critical pain points.
Strategic insights: By optimizing internal processes and identifying trends with the help of custom data science services, companies gain a competitive edge, bolster their strategies, and potentially cut costs.
Data security and privacy: Since data remains within the organization’s confines, concerns about data security, privacy, and compliance are well manageable.
Organizational learning: Building an in-house data science team or partnering with a data science agency and executing internal projects can enhance the organization’s data literacy and analytics capabilities.
Data Science as a Service (DSaaS): Monetizing expertise
DSaaS involves employing data science service expertise to provide solutions to external clients on a subscription or project basis. This approach allows organizations to monetize their data science capabilities and expand their revenue streams. The key factor in opting for DSaaS is the presence of in-depth data science expertise and skills at your company, as if you don’t have them, you won’t be able to fully support the system.
Advantages of DSaaS projects include:
Diverse revenue streams. DSaaS allows companies to capitalize on their data science service expertise by offering valuable insights and solutions to other businesses, creating additional revenue sources.
Economies of scale. Serving multiple clients with similar needs can lead to economies of scale, making it more cost-effective to deliver data science solutions.
Showcasing innovation. By providing data-driven solutions to external clients, a data science services company can showcase their innovation and establish a strong brand presence.
Full skill implementation. Organizations can fully use their data scientists’ talent and technology stack, maximizing their returns on investment.
Striking the balance
The choice between internal data science projects and DSaaS isn’t always binary. Organizations can strategically balance both approaches of data science services delivery to reap the data science benefits of each. A few key considerations include:
Resource allocation. Assess the availability of resources, both in terms of data science expertise and infrastructure, to determine if it’s feasible to cater to both internal and external demands.
Market demand. Evaluate the market potential for DSaaS offerings. If there’s demand for data-driven solutions in your industry, DSaaS can be a lucrative option.
Data sensitivity. Define how sensitive your corporate data is. While internal projects offer greater control over data security, DSaaS entails sharing insights with external entities.
Long-term goals. Align your decision to offer data science services with your organization’s long-term goals. If your focus is on innovation and revenue diversification, DSaaS might be more attractive.