'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 Development Services

Today, businesses are immersed in 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 sexy 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. A 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.

How to start?

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.

What technology to use?

Our company is technology agnostic – not ‘one size fits all’. We look at the problems, production, and business processes from all sides and views, selecting the most up-to-date instruments, best suited for the task at hand.

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) that 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?

IoT

Logistics & Warehouses

Manufacturing

Agriculture

Cybersecurity

Healthcare

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?

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

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.

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