From problem definition to production.
We create business-driven data science solutions based on our understanding of your business needs, audience, and goals. We select the ML implementation approach that is cost-effective for your organization, implement the solution, and integrate it with your existing business processes.
Data Science Process at TekDatum
Problem Definition
The problem is defined in clear, measurable terms. We work with stakeholders to identify the business problem or opportunity, limit the scope of the project, and determine the success criteria.
Data Collection
We identify the data sources that will be used to solve the problem — collecting and storing data in a way that is consistent with project goals and business requirements.
Data Preparation
The data is transformed and cleaned to ensure it is accurate and ready for analysis. This includes removing duplicates, filling in missing values, and converting data types.
Exploratory Data Analysis
The data is explored to identify patterns, relationships, and trends relevant to the problem. This involves data visualization and statistical analysis to surface actionable insights.
Feature Engineering
We select and transform variables in the data to create new, meaningful features that can be used to build more accurate and reliable predictive models.
Model Building
Predictive models are developed using statistical algorithms and machine learning techniques. Models are trained on the data and evaluated to determine their accuracy and performance.
Model Deployment
Once developed, the model is deployed in a production environment where it can be used to make predictions or inform business decisions — integrated with existing processes.
Monitoring & Maintenance
As data changes over time, model performance can deteriorate. We monitor the model continuously to ensure it performs well and update it as needed to maintain accuracy.
