Data science company

Data science services

Generate limitless potential for your business

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What is data science?

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Data science is the key to transforming raw data into meaningful insights that drive decision-making and strategic planning. By applying advanced analytical techniques, data science enables businesses to predict trends, uncover patterns, and make data-driven decisions.

Why choose Devico for
data science services

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High retention rate

96%

We go beyond the 80% industry norm with reliable, expert support.

Wide expert network

3000

Access to over 3000 engineers and AI experts.

Proven track record

500,000

Over 500,000 man-days successfully delivered.

Support

24/7

Highly experienced management team available around the clock.

Your data can do more

43% of data scientists consider automation as the most important skill to develop for the future

How does it work?

01

Start with the right data

Relevant. Real. Captured from every critical source.

Start with the right data

02

Clean without compromise

Errors removed. Duplicates gone. Integrity restored.

Clean without compromise

03

Explore for insight

Hidden patterns revealed. Signals separated from noise.

Explore for insight

04

Transform for clarity

Raw inputs reshaped into powerful, structured features.

Transform for clarity

05

Build models that think

Algorithms designed to learn, adapt, and predict.

Build models that think

06

Evaluate what matters

Performance measured by precision, accuracy, and impact.

Evaluate what matters

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Deploy into reality

Seamless integration into systems, processes, and decisions.

Deploy into reality

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Monitor, improve, repeat

Constant feedback. Continuous learning. Always relevant.

Monitor, improve, repeat

How businesses use
Data Science

Healthcare

Data science enables healthcare providers to improve patient outcomes through predictive analytics and personalized treatment plans. It also aids in drug discovery and the management of healthcare resources.

Use cases:

  • Predictive analytics for patient deterioration.
  • Personalised treatment plans based on genetic data.
  • Optimising resource allocation in hospitals.
  • Accelerating drug discovery processes.
Finance and insurance

Financial institutions leverage data science for fraud detection, risk management, and to enhance customer service through personalised financial products.

Use cases:

  • Real-time fraud detection.
  • Risk assessment for credit and loans.
  • Personalised financial product recommendations.
  • Enhancing customer service through chatbots.
Retail

Retailers use data science to understand customer preferences, manage inventory, and optimise pricing strategies. This technology also helps in creating personalised marketing campaigns.

Use cases:

  • Customer preference analysis for personalised experiences.
  • Inventory management and demand forecasting.
  • Dynamic pricing strategies.
  • Targeted marketing campaigns.
Manufacturing

In manufacturing, data science improves operational efficiency through predictive maintenance, quality control, and supply chain optimization.

Use cases:

  • Predictive maintenance for machinery.
  • Quality control through defect detection.
  • Optimising production processes.
  • Enhancing supply chain management.

Future-proof your data

It is estimated that 97.2 zettabytes (ZB) of data will be generated globally

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    What it means: Data collection involves gathering raw data from various sources such as databases, APIs, sensors, and user interactions.

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    Goal: To acquire relevant and high-quality data that serves as the foundation for subsequent data processing and analysis.

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    Practical application: In healthcare, data collection from electronic health records, wearable devices, and medical tests helps create comprehensive patient profiles, leading to better diagnosis and personalized treatment plans.

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    Data & statistics: By 2025, the global data sphere is projected to grow to 175 zettabytes, highlighting the critical need for effective data collection methods.

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    What it means: Data storage refers to the methods and technologies used to save and manage data securely and efficiently.

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    Goal: To ensure that collected data is stored in a way that is accessible, reliable, and scalable for future use.

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    Practical application: In finance, robust data storage solutions enable the secure retention of transaction records and customer information, ensuring compliance with regulatory requirements and enabling advanced analytics.

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    Data & statistics: According to IDC, global spending on cloud services is expected to reach $1.3 trillion by 2025, underscoring the shift towards scalable data storage solutions.

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    What it means: Data extraction involves retrieving relevant information from various sources for analysis.

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    Goal: To transform raw data into a structured format that can be easily analyzed and utilized.

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    Practical application: In retail, data extraction from customer feedback and sales transactions helps in understanding customer preferences and improving product offerings.

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    Data & statistics: Forbes reports that 95% of businesses consider managing unstructured data a major challenge, emphasizing the importance of effective data extraction.

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    What it means: Data cleaning involves correcting or removing inaccuracies, inconsistencies, and errors from the dataset to improve its quality.

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    Goal: To ensure the integrity and accuracy of the data, making it suitable for analysis.

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    Practical application: In marketing, refining customer data ensures that marketing campaigns are targeted accurately, reducing waste and increasing effectiveness.

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    Data & statistics: Gartner estimates that poor data quality costs organizations an average of $12.9 million annually, highlighting the importance of data cleaning.

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    What it means: Data analysis involves inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.

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    Goal: To extract meaningful insights from data that inform strategic business decisions.

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    Practical application: In manufacturing, analyzing production data can identify inefficiencies in the process and suggest improvements to increase productivity and reduce costs.

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    Data & statistics: According to a report by McKinsey, data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain them, and19 times more likely to be profitable.

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    What it means: Data visualization is the graphical representation of data to help stakeholders understand complex data insights through visual elements like charts, graphs, and maps.

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    Goal: To present data in a visually appealing and easily interpretable format that aids in understanding and decision-making.

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    Practical application: In education, visualizing student performance data helps educators identify areas for improvement and tailor their teaching strategies accordingly.

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    Data & statistics: The global data visualization market is expected to reach $10.2 billion by 2027, reflecting the growing importance of visual tools in data analysis.

Basic techniques of data science

analytics

Probability and statistics

Provides the foundation for analyzing data and making predictions.

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Distribution analysis

Examines how data points are dispersed across a range of values. Helps identify patterns and anomalies.

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Regression analysis

Predicts future outcomes by modeling the relationship between dependent and independent variables.

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Descriptive statistics

Utilizes measures like mean, median, and standard deviation to summarize key data characteristics.

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Inferential statistics

Draws conclusions and makes predictions about a population based on analysis of a sample.

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Non-parametric statistics

Analyzes data without assuming any specific data distribution. Useful for handling data that do not fit normal distribution patterns.

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Hypothesis testing

Evaluates the validity of assumptions or claims about a dataset using sample data analysis.

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Linear regression models

Models the relationship between variables using a straight line to predict the value of a dependent variable based on one or more independent variables.

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Logistic regression

Used for classification tasks to predict binary outcomes. Models the probability of a binary response based on one or more predictor variables.

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Neural networks

Mimic the human brain to recognize complex patterns in data. Make decisions for tasks like image and speech recognition.

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K-means clustering

Partition data points into k distinct clusters based on similarity. Useful for exploratory data analysis and identifying natural groupings in data.

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Decision trees

Use a tree-like structure to make decisions based on data. Splits data into branches to visualize decisions and predict outcomes.

Professional AI guidance

Studies indicate a high failure rate of 70% among data science projects

Data science technologies

Artificial Intelligence

AI leverages data science to create intelligent systems that can learn and adapt.

Use cases:

01

Automated customer service with chatbots

02

Predictive analytics for business forecasting.

03

Personalised marketing strategies.

04

Enhancing product recommendations.

Cloud Computing

Cloud platforms provide the infrastructure needed to store and process large datasets.

Use cases:

01

Scalable data storage solutions.

02

High-performance computing for data analysis.

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Collaborative data science projects.

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Real-time data processing and analytics.

Internet of Things (IoT)

IoT devices generate vast amounts of data that can be analysed to gain insights.

Use cases:

01

Predictive maintenance for industrial equipment.

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Smart home automation and energy management.

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Real-time health monitoring with wearable devices.

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Optimising supply chain logistics.

Quantum Computing

Quantum computing offers the potential to solve complex data problems much faster than classical computers.

Use cases:

01

Optimising complex supply chain logistics.

02

Accelerating drug discovery processes.

03

Enhancing cybersecurity measures.

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Solving complex financial models and simulations.

Get in touch

Drop us a line about your project and we will contact you within a business day

Our locations

New York

HQ

521 Fifth Ave, NY 10175

+1 805 491 9331

London

Sales

9 Brighton Terrace, SW9 8DJ

+44 1922 214429

Warsaw

R&D

Towarowa 28, 00-847

info@devico.io

Lviv

R&D

Uhorska str. 14, 79034

info@devico.io

Questions & answers

From predicting customer churn to optimizing operations, data science services turn raw data into better decisions – faster, smarter, and at scale.

If you’re sitting on data but not getting value from it – or making decisions based on gut, not facts – then yes, a data science company can help.

No. Startups and mid-sized companies often benefit the most – they move fast and need insights to scale with confidence.

By starting with your objectives, not the algorithms. Good data science services focus on real impact, not just nice charts.

Yes. Most real-world data is messy. Cleaning and structuring it is part of the job – not a blocker.

Both. Some models are black-box, but we can also build explainable ones if interpretability is key to your team or regulators.

Clear goals, data access, and someone who understands the business side. A solid data science company handles the rest.

Yes. A proof of concept is a great way to show value fast without going all-in from day one.

Not drastically. A good data science company builds tools that fit into your workflows – not the other way around.

Yes. Whether it’s a CRM, ERP, or data warehouse – integrations are part of the delivery.

Strict protocols, secure infrastructure, and privacy-first design. Data stays safe, period.

Yes. Domain knowledge helps, but smart models are adaptable. We focus on solving your specific problems – not just using templates.