Data science is a hybrid skill set that combines mathematics, software engineering, and business analytics. Data science relies heavily on the rapidly advancing fields of machine learning (ML) and artificial intelligence (AI). These new technologies enable algorithms and models to learn from historical data in order to predict the future, identify trends or groups, and detect anomalies.
By applying modern statistical methods and ML to historical datasets, data scientists can:
The most successful data science projects start with small objectives and develop iteratively based on intermediate findings. This agile approach can exponentially add value by compounding upon success. Simple results can also help catalyze new ideas and applications for business transformation.
Successful outcomes also depend on experience and best practices in ML engineering and MLOps. Without a strong pipeline for deploying ML models, many innovations fail to reach an operational state. Surrounding data science teams with savvy engineering and operations teams is important to make sure your models make it to production rather than dying in PowerPoint.
The Machine Learning Lifecycle
Every data science project looks a little bit different, since no two organizations are the same in how they treat data and analytics. Our data science services are tailored to help your company at any stage of data science adoption.
Data scientists apply skills from software engineering, mathematics, and statistics to complex business problems. Typically, a data scientist will start by exploring available data using a programming language like Python or R to uncover trends and validate assumptions. Data is then used to build statistical models with machine learning or mine for patterns with other AI technologies. These models and patterns can then be used to augment business processes through automation or decision support.
First and foremost, data science requires good data. Good data depends on robust data pipelines and good metadata systems to know how data was collected in the first place. In addition to data, organizations should be ready for disruption that may occur as processes are automated and decisions are made with new information uncovered through data. For more information, see our guide on building a data-driven culture within your organization.
Every organization has unique needs and projects can be scoped to fit. Some organizations start with a simple conceptual engagement to develop use cases; these can last just a few weeks and range from $25k-$50k. In other cases, we’ve seen organizations with robust datasets and operations who are ready to develop sophisticated models and applications. Larger engagements could cost upwards of $300k.