Data Project Development
We work in Time & Material model: you pay only for time and resources spent on the project, so it is suitable for flexible management of different types of projects long and short-term.
Data Project Development
Let's put data to work for your company. We specialize in software development, focusing on data management. We use scenario-based and agile approaches to increase the transparency, predictability, and efficiency of projects.
Contact salesMVP
Minimum viable product (MVP) is a development technique in which a new product is introduced in the market with basic features, enough to get customer feedback for future product development. It is used to quickly launch a product, gather customer insights and validate a product idea quickly before investing time and money in development
PoC
Proof of concept is a demonstration of the feasibility of a product or solution in software development and is typically used to prove that a concept is possible. It is a prototype developed to validate a concept or process and is usually done before any development or coding begins.
R&D
We take care of the process of designing, creating, and testing software to meet customer needs and demands. Through this process, software developers can deliver innovative new products and services to the marketplace.
Platform maintenance and support
We provide ongoing improvements, bug fixes, and assistance for users to ensure the software is reliable and secure. It involves tasks such as regularly checking for software updates, addressing user inquiries and troubleshooting any issues that may arise.
How the project cycle looks like?
A project management life cycle consists of 5 distinct phases including initiation, planning, execution, monitoring, and closure that combine to turn a project idea into a working product.
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2
3
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Discovery and analysis of needs and expectations. Customer onboarding
Choosing technical strategy & approach. Building team of data specialists
Project start
Testing and consulting
Maintenance and support
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Get a quoteKey technologies and tools for Data Project Development
Data project development is the process of creating a data system that collects, organizes, and stores data in order to meet the needs of the organization. It is a complex process that requires the use of key technologies and tools to ensure the success of the project. We have enlisted here those we specialize at.
Python is a powerful programming language that is widely used in data project development. It is an interpreted language, meaning that it can run without being compiled. This makes it easier to debug and maintain code.
Python is popular for data projects because it has a wide range of libraries and frameworks that are useful for data manipulation and analysis. It also supports several popular data formats, including CSV, JSON, XML, and HDF5. Python makes it easy to process data from various sources and can be used to create data visualizations. Python uses Jupyter® Notebook to access data sources to explore reading and writing data for generating reports from the source data.
Pandas DataFrames make manipulating your data easy, from selecting or replacing columns and indices to reshaping your data. Pandas is a popular Python package for data science, and with good reason: it offers powerful, expressive and flexible data structures that make data manipulation and analysis easy, among many other things. The DataFrame is one of these structures.
D3 is a JavaScript library and framework for creating visualizations. D3 creates visualizations by binding the data and graphical elements to the Document Object Model. D3 associates (binding) the data (stuff you want to visualize) with the DOM. This allows the user to manipulate, change or add to the DOM.
SQL (Structured Query Language) is a powerful language used to store, manipulate and retrieve data from databases. It is used in many data projects, including data warehousing, data lakes, data integration, data management, and big data analytics. SQL is used to define and create database objects such as tables, views, and stored procedures.
It is a flexible language that can be used to create, manipulate, and analyze data from multiple sources. With SQL, data professionals can easily create database objects, query data, and create reports.
Keras is an open source deep learning library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, or Theano. Keras was designed to enable fast experimentation with deep neural networks, and focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of the project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).
Keras is used for building deep learning models, primarily for applications in areas such as computer vision and natural language processing. It provides a set of high-level APIs that are used to construct and train models. It provides a high-level and easy-to-use interface to the underlying computational libraries, making it easy to build complex neural networks.
TensorFlow is an open source software library for numerical computation using data flow graphs. It was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for machine learning and deep neural networks research.
TensorFlow is used for a wide variety of applications, such as image recognition, natural language processing, and even for self-driving cars. It is also used for training, deploying, and managing neural networks.
Offer advantages
Full control, transparency and predictability
You pay just for time and resources used on the project, so it is appropriate for flexible administration of various sorts of projects long and short-term.
Scenario-based
Using scenario-based methodology, project management and outsourcing of complex IT projects become effortless, predictable, and autonomous. When compared to pure waterfall or pure SCRUM, the scenario-based methodology performs better.
Reliable tech stack
Python, SQL, Pandas and TensorFlow are a good choice for companies that work under time constraints as well as corporations that build extremely complex apps that must meet the highest security standards.
Complex approach
A successful data project requires a comprehensive understanding of the data and its potential applications. A skilled team of data scientists, software engineers, and business analysts is essential for the successful completion
Data management success stories
Data management in healthcare
Glucoactive is a Research and Development start-up, with an estimated worth of above 6 million euros, feautered on Tech Crunch. It is working on a revolutionary medical care product that will change the way we treat diabetes.
The objective is to better manage data, facilitate the R&D process (both for the current device and those in the future), including support for data analysis, and maximize the quality of the resulting data.
Read more
Data Management software development for WoodWatch
WoodWatch has been named to the FT1000, the Financial Times ranking of Europe’s fastest-growing companies and listed by Forbes. Business intelligence software gave Customer’s teams a bird’s-eye perspective of the data that mattered to them and helped them distill the information into actionable insights.
Read moreUsage of data according to the industry sector
Retail
Data science is being used in the retail sector to identify customer buying patterns and develop personalized product recommendations to increase sales.
Retailers are also using data science to analyze customer feedback and identify potential opportunities to increase customer satisfaction and loyalty.
Manufacturing
Data science can help manufacturing companies to uncover hidden patterns in production data and optimize their processes for improved efficiency and cost savings.
Companies can use data science to develop predictive models to better forecast demand and plan production accordingly, resulting in improved customer service and reduced inventory costs.
Media & Advertising
Data science can be used to analyze consumer behavior in the media and advertising sector, allowing companies to better target their campaigns and increase their ROI.
By leveraging data science, media and advertising companies can gain a deeper understanding of customer preferences and create more effective ad placements.
E-commerce
Data Science can be used to analyze customer behavior in the e–commerce sector, allowing businesses to make better decisions about their marketing and product offerings.
Data Science can also be used to develop predictive models that can identify potential customers and target them with personalized marketing messages, increasing the likelihood of a successful sale.
Energy and utilities
Data science is being increasingly utilized in the energy and utilities sector for predictive analytics, to identify savings opportunities, and to optimize maintenance schedules.
Advanced data science techniques such as machine learning and artificial intelligence are becoming key components in energy and utilities operations, helping to improve efficiency, reduce costs, and enhance customer experience.
Logistics & transport
Data science is being used in the logistics sector to improve route optimization and delivery time, which helps to reduce costs and increase efficiency.
Data science is being used in the logistics sector to improve predictive maintenance of vehicles, helping to reduce breakdowns and improve safety.
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CEO & Co-founder
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