Data science development services

Our data science consulting 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 and improving to be fully ready for analysis. With clean and prepared data, our data science service provider proceeds to the actual data analysis and builds data models and algorithms to generate insights for your business and assist in making business efficient decisions.

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 and data scientists are proficient in the Python and R programming languages to effectively perform 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 scientist conducts a comprehensive study of existing solutions that can be used for your custom project.

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.

To achieve high accuracy and reliability for the data models we build, our data science services team applies optimal data science services offerings and 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 outcomes 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

Our data science services company uses distributed computing frameworks like Apache Spark and Hadoop to break data into smaller chunks and process them in parallel, so even the largest datasets don’t slow things down.

We also choose the right data storage solutions depending on the project (PostgreSQL, MongoDB). For real-time analytics, our data engineering team works with stream processing tools like Apache Kafka, providing insights for the businesses as soon as the data comes in.

For enhanced interoperability, we set up ETL pipelines, which automate data movement. To ensure scalability, we enable solution deployment in the cloud using AWS, Google Cloud, or Azure so businesses can process as much data as they need without hitting performance limits.

One way we do this is by building APIs that let our models communicate with existing software, whether it’s a CRM, ERP, or any other business tool. Thus, companies can get predictions and insights without disrupting their workflow.

For even smoother integration, we use containerization tools like Docker and Kubernetes to deploy artificial intelligence models across different environments without compatibility issues. For the cloud environment, we optimize our models for platforms like AWS SageMaker, Google AI Platform, or Azure ML.

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 support the system fully.