Data science development services

Our approach to delivering data science services

We have a structured process in place for our data science and development services to foster understanding and alignment between our experts and your company’s team. This standardized framework enables us to optimize resource utilization, minimize errors, and consistently deliver high-quality results across various projects.

• Problem definition

  Discovery. Syndicode’s data science team initiates stakeholder meetings and interviews to understand your specific business objectives, challenges, and needs. Goal setting. Drawing from the insights gained, they collaboratively define clear and measurable goals for the project or data science consulting services. These goals will serve as the guiding principles for subsequent steps in the process. Scope definition. Next, our data science consultants establish the scope of the project, including the data sources, variables, and metrics to be analyzed, as well as any constraints or limitations. This ensures alignment between project objectives and available resources and timelines.

• Data preparation

  Data collection. Our data science team gathers relevant data from various sources, such as databases, APIs, spreadsheets, and external sources. This may entail data Extraction, Transformation, and Loading (ETL) processes to consolidate and prepare the data for analysis. Data cleaning. Next, the team cleans and preprocesses the data to ensure its quality and integrity, priming it for further analysis and modeling. Feature engineering. Leveraging advanced techniques, the team extracts, transforms, and creates relevant features from raw data. This may involve dimensionality reduction, feature scaling, and transformation techniques to enhance the predictive power of the models.

• Model training

  Model selection. Our team selects machine learning algorithms and techniques tailored to the problem, data characteristics, and project goals. This entails evaluating different algorithms, tuning hyperparameters, and experimenting with diverse modeling approaches. Model training. The team trains machine learning models using the prepared data, splitting the data into training, validation, and test sets. This process involves fitting models to the training data, optimizing performance metrics on the validation set, and iteratively fine-tuning the models. Model evaluation. We rigorously assess the performance of the trained models using appropriate evaluation metrics to ensure they generalize well to unseen data and align with defined project objectives.

• Deployment and support

  Model deployment. Our team seamlessly transitions the trained models into production environments, enabling real-time predictions. This includes integrating the models into existing systems, APIs, or web and mobile applications and setting up monitoring and logging mechanisms to track performance. Continuous monitoring. We vigilantly monitor the deployed models to ensure they perform as expected in real-world scenarios. This involves tracking model drift, performance degradation, and data quality issues and taking corrective actions as needed. Support and maintenance. We offer continuous support and maintenance for deployed models, resolving issues, incorporating feedback, and updating models as new data becomes available or business requirements evolve. Should the client require further consulting on big data or seek further enhancements for their data systems, we stand ready to offer tailored assistance upon request.