'Cross-industry standard process for data mining' or CRISP-DM is one of the most widely used schemes for data scientists to organize their work. We employ it to get the job done efficiently. Six major phases include: Business Understanding: determine business objectives and plan; Data Understanding: collect, describe, explore data; Data Preparation: select and format data; Modeling: create and assess model; Evaluation: check results; Deployment: decide, how to deploy, maintain and monitor the project

Data Science UA Development Service

Q: How can businesses use Data Science?

Predicting market tendencies or finding emerging ones, Analyzing customers’ behavior, Better identifying target audience, Finding suitable recruits, Optimizing or automating business processes, etc.

Q: What is the Team Data Science Process (TDSP)?

An agile, iterative methodology for data scientists to organize their work. It’s a new approach, introduced by Microsoft in 2016. TDSP life cycle includes: understanding the problem, accessing the data, creating and comparing different models, deploying the best model, getting customer’s acceptance.

Data Science Development Service for businesses are immersed in the tremendous quantities of information about sales, website statistics, social media buzz, etc

Today, businesses are immersed in the tremendous quantities of information about sales, website statistics, social media buzz, etc. You, sure thing, do too. But do you use their full potential? It’s like having a powerful Thor’s hammer and not being able to lift it. The same goes for data. If you have it but don’t know what to make of it, it’s like having a sunroof on a submarine.

Data Science UA is exactly the Thor (perhaps, a little less sexier than Chris Hemsworth) you need to harness the power of those numbers. It will help you to grasp crucial insights to predict your products and services’ performance in the market, better understand your customers, adjust your strategy, etc. Data science development company like ours makes necessary instruments available for each business, leading it through data transformation.

Why are we a top Data Science Development company?

We provide complex data science product development. Our company is the only contractor you need, we’ll take care of everything or get support from our partners.
Our partners (software development companies, mobile/web apps developers and cloud-development companies) are ready to solve any data science-related tasks that you might encounter.
Our solutions are customized according to the client’s needs. We will find all the experts required to do the job and help you make your business thrive.
You won’t be left on your own with unfamiliar technologies. We provide the whole cycle of data science development services: from consulting to software development and creating technical documentation.

We can help you unlock the potential hidden in the data

Data Science UA, a business development data science company, specializes in harnessing the power of data to drive business growth and innovation for data science app development and other purposes. In today’s digital age, businesses generate vast amounts of data, but without the right tools and expertise, this data remains untapped potential. That’s where Data Science UA comes in.

Our team of skilled researchers, engineers, and developers understands the value of data and its role in gaining a competitive edge. We offer comprehensive services that enable businesses to unlock the hidden potential within their data and transform it into actionable insights. Here’s how we can help:

Data Analysis and Exploration: Our data scientists conduct thorough exploratory data analysis to uncover patterns, biases, and valuable insights within your data. By understanding the nuances of your data, we can identify opportunities for optimization, process improvement, and growth.
Machine Learning and AI: Data Science UA specializes in machine learning and AI development. We leverage advanced algorithms and models to create intelligent systems that learn from your data and make accurate predictions and recommendations. Whether it’s developing custom machine learning models, implementing deep learning techniques, or deploying AI-powered solutions, we have the expertise to drive transformative results.
Computer Vision: Our expertise extends to computer vision, a field within AI that enables machines to interpret and understand visual information. Through computer vision techniques, we can extract meaningful insights from images, videos, and other visual data sources. This opens up a world of possibilities for industries such as manufacturing, healthcare, retail, and more.
Data-Driven Decision Making: We believe that data should be at the core of decision-making processes. Our data science solutions provide businesses with the tools and frameworks necessary to make informed decisions based on data-driven insights. Whether it’s optimizing marketing campaigns, improving customer segmentation, or enhancing operational efficiency, our solutions empower you to make strategic choices backed by data.
Industry Expertise: Data Science UA serves a diverse range of industries, including marketing, e-commerce, manufacturing, IoT, FinTech, logistics, insurance, agriculture, cybersecurity, and healthcare. We understand the unique challenges and opportunities within each industry and tailor our solutions to meet their specific needs. By combining industry expertise with data science and AI capabilities, we drive tangible results for our clients.

At Data Science UA, we believe that data holds immense potential for businesses. Our mission is to unlock that potential and empower our clients to thrive in the data-driven era. Whether you need assistance with data analysis, machine learning, computer vision, or AI development, we have the knowledge, skills, and dedication to help you harness the power of your data and achieve success.

Programming languages, APIs and frameworks that we use

Programming languages, APIs, and frameworks play a crucial role in the development of data science and AI solutions. At Data Science UA, we utilize a diverse set of tools and technologies to create innovative and effective solutions that drive business development and leverage the power of data science. Let’s explore these programming languages, APIs, and frameworks in-depth:

Python: Python has emerged as a popular programming language in the data science and AI community. Its simplicity, extensive libraries such as NumPy, Pandas, and scikit-learn, and wide-ranging community support make it a go-to language for various data science tasks. We harness the capabilities of Python to develop robust machine learning models, perform data analysis, and build AI-powered applications.
R: R is another powerful programming language extensively used in statistical analysis and data science. It provides a rich ecosystem of libraries and packages, making it suitable for tasks such as data visualization, statistical modeling, and exploratory data analysis. We leverage R to extract insights from data, conduct rigorous statistical analysis, and create visualizations that aid decision-making processes.
TensorFlow: TensorFlow is an open-source framework developed by Google for building and deploying machine learning models. It provides a comprehensive ecosystem for designing and training deep neural networks, making it ideal for tasks such as image recognition, natural language processing, and speech recognition. We harness the capabilities of TensorFlow to create cutting-edge AI models and deploy them at scale.
PyTorch: PyTorch is a popular deep learning framework known for its dynamic computational graph and flexibility. It is widely adopted for tasks such as natural language processing, computer vision, and generative modeling. With PyTorch, we develop advanced neural network architectures, perform model training, and facilitate rapid prototyping of AI solutions.
Scikit-learn: Scikit-learn is a versatile machine learning library in Python that provides a wide range of algorithms and tools for tasks such as classification, regression, clustering, and dimensionality reduction. It offers a user-friendly interface, making it accessible for data scientists and machine learning practitioners of all levels of expertise. We leverage scikit-learn to build robust and scalable machine learning pipelines.
RESTful APIs: APIs (Application Programming Interfaces) are essential for integrating different components of a data science or AI solution. We utilize RESTful APIs to facilitate communication between various systems, enabling seamless data flow, model deployment, and interaction with external services. APIs allow us to create scalable and modular solutions that can be easily integrated into existing business infrastructures.

By employing these programming languages, APIs, and frameworks, Data Science UA ensures that we stay at the forefront of technology and deliver state-of-the-art data science and AI solutions. Our expertise in these tools empowers businesses to unlock the full potential of their data and drive business development through actionable insights. We combine the power of programming languages, APIs, and frameworks with our domain knowledge and industry experience to create tailored solutions that address unique business challenges and unlock new opportunities for growth.

Get Started with Data Science Today

To better understand, where we are going and what we should achieve, our first step is to conduct preliminary research and identify objectives. We will guide you right where you need to be – our data science business development is in sync with your goals and business processes. Your products are in our focus, and are what matter to us.

Is technology all that’s needed?

Data science software development alone is half the story – our goal is to make new instruments (for example apps or customized software) which slot into your business neatly. To master the endless flows of data we will arm you with not only data science apps and software but also with data culture.

What industries does Data Science UA work with?

Insuarance

AI can help insurance companies deliver high-quality service as it has done for major leaders in other industries such as Healthcare, Fintech, etc.
We can help with implementation of AI for industry needs:
- create a more personalized service;
- predict the repair costs from historical data;
- provide a selection of better investments based on risks, preferences, and spending patterns;
- improve claims analysis.

Banking

Companies from banking sector usually work with sensitive information and have high-security standards. We take all necessary precautions to keep their data safe. Data Science UA can assist such businesses in:
- credit scoring;
- recommendation systems for both new and prospective clients.

Marketing

Marketing teams tend to have lots of data about advertising, web analytics, customer behavior, etc. We can fine-tune all data analysis solutions to run like clockwork and free up more of your marketing team’s time to be strategic and effective. Our team uses machine learning to:
– forecast sales;
– recommend products;
– analyze assortment and so on.

Retail (E-commerce)

Retail usually accumulates large amounts of data and is eager to use data analytics.
We can help with:
- customer analysis;
- assortment analysis;
- sales forecasts;
- marketing and advertising budgets optimization;
- increase the efficiency of merchandising and supply chain management.

Manufacturing

Generation of optimized plans that enable predictive maintenance is one of the key goals for AI in manufacturing, as well it helps in:
- optimizing production lines and logistic chains;
- forecasting revenue;
- determining optimal employee workloads;
- setting up automated systems for monitoring compliance with safety regulations.

Agriculture

Farmers aim to maximize production and profits using innovative software and data collection and analysis. We can make the analysis of historical and real-time images & data collected from databases, satellites, drones, IoT sensors that can help to:
- increase the yield of farmlands;
- ensure serviceability of farm equipment;
- monitor fields conditions, irrigation, soil moisture, etc;
- predict weather conditions.

Logistics & Warehouses

The transportation and warehouse industry is data-driven and needs analysis of historical and real-time data performed by intelligent algorithms. So our team can help with:
- traffic management improvements
- warehouse optimization,
- route optimization (“travelling salesman” problem),
- developing optimal loading systems and utilization systems for vehicles;

IoT

When artificial intelligence is working with IoT devices it means that data can be analyzed and decisions can be made without involvement by people. In a broad variety of industries where IoT is implemented, AI can help to identify patterns and detect anomalies in the data that smart devices and sensors transfer (for example, air quality, humidity, temperature, pressure, vibration, sound, and others).

Cybersecurity

Nowadays AI helps to deploy effective cybersecurity technology and allows businesses to solve major cybersecurity challenges: cyberattack, financial loss, or brand reputation damage. We can help cybersecurity teams to:
- analyze patterns in user behaviors and respond to changing behavior;
- identify cyber vulnerabilities and irregularities in the network.

Healthcare

AI is already transforming the healthcare industry—helping patients and hospitals optimize costs and increase care delivery through actionable insights. We can help to:
- manage and analyze data to provide;
- improve preventive care;
- create personalized treatments;
- make optimization of scheduling and bed management;
- detect and analyze patient patterns and correlations for better decision making.

AI in HR

Artificial Intelligence in HR will help save time by automating repetitive and less valuable tasks. Reap the benefits of AI in HR with Data Science UA.

AI in Telecommunications

No longer limited to providing basic phone and internet service, the telecom industry is at the epicenter of technological growth, led by mobile and 5G broadband services in the Internet of Things (IoT) era. This growth is expected to continue due to the rapid adoption of artificial intelligence (AI) in the telecom industry.

Energy Data Science

Data science in energy - we can help your biggest plan. Solving Problems with Data Science in Energy Sector

Artificial Intelligence in Science

We use AI in science to improve science and simplify the work of scientists. Artificial intelligence is changing science for the better.

AI in Financial Services

Data Science in Financial - we will help to solve your problems. We optimize tedious processes and significantly improve the quality of customer service

What clients and partners say about us

Olga Shevchenko

CEO, EVA

Now it's more than two years of our fruitful cooperation with Data Science UA. We ask our partners for help in closing important and difficult positions. Chief Data Officer, Director of Innovation Solutions, Head of Data Science — specialists are still with us to this day. We are impressed by the approach of our colleagues in Data Science UA to the search — they understand the profile of specialists as quickly as possible, evaluate candidates qualitatively, and are client-oriented and focused on the result. That is why we continue to work with Data Science UA now and plan to cooperate in the future.

Jake Diner

Founder and CEO, Elafris Inc

One thing I wanted to highlight about Data Science UA that I was particularly impressed with the ability to adjust based on our feedback. Hiring is a long iterative process and getting your recruiter on the same page takes time, but with Data Science UA, it was very easy and effective. I do recommend using this service and they are already looking for our next candidate.

Oleg Bilozor

CEO and Founder, Reply

The work with Data Science UA was a blast. Minimum number of questions, maximum number of highly skilled candidates delivered. At the end, instead of hiring one Engineer, we ended up hiring two, since both was great and good fit for us.

Michael Korkin, Ph.D.

CTO at Entropix, Inc.

Data Science UA is the go-to place if you are setting up or expanding your computer vision / deep learning/data science team. Whether you need a small team or a large R&D center — they are the best to work with, extremely reliable, very knowledgeable, and well organized! Data Science UA is at the heart of everything happening in data science, anywhere in the world. Their annual data science conferences attract presenters and attendees from all over the globe. I am always impressed with the quality of the presenters and the depth of the materials.

FAQ about Data Science Development

How can businesses use Data Science?

Businesses can leverage data science in numerous ways to gain a competitive edge and make data-driven decisions. Some key applications of data science in business include: predictive analytics, customer segmentation and personalization, operational efficiency, fraud detection and risk management, business forecasting and decision-making.

What is the Team Data Science Process (TDSP)?

The Team Data Science Process (TDSP) is a framework developed by Microsoft that provides a structured approach to data science projects. It outlines a set of guidelines and best practices for executing end-to-end data science projects in a collaborative team environment. TDSP encompasses various stages, including data acquisition and understanding, modeling, deployment, and operationalization, ensuring a systematic and repeatable process for data science projects. It promotes effective teamwork, clear communication, and efficient project management, enabling organizations to deliver successful data science solutions.

What is CRISP-DM?

‘Cross-industry standard process for data mining’ or CRISP-DM is one of the most widely used schemes for data scientists to organize their work. We employ it to get the job done efficiently. Six major phases include:

Business Understanding: determine business objectives and plan;
Data Understanding: collect, describe, explore data;
Data Preparation: select and format data;
Modeling: create and assess model;
Evaluation: check results;
Deployment: decide, how to deploy, maintain and monitor the project