What is data science?
Data science is the discipline of turning data into explanations and predictions that change decisions, using statistics, programming, machine learning, and knowledge of the business domain. A data scientist frames a business question as an analytical problem, obtains and cleans the data, explores it, builds and evaluates models, and communicates what was found. The output is a decision, a recommendation, or a model that other systems use.
How data science works
Most projects follow a loop rather than a line. The question is framed with the business owner, including the decision it will inform and the accuracy that would make a difference. Data is gathered from source systems, a data warehouse, or a data lakehouse and cleaned, which routinely takes the majority of the time. Exploration with summary statistics and data visualization shows what the data can and cannot support. Models are chosen for the problem rather than for novelty. Results are checked on held-out data and explained to the people who will act on them.
Data science sits beside several neighboring roles. Data engineers build the pipelines and platforms the data arrives through. Analysts answer defined questions with business intelligence tools. Machine learning engineers take models into production. In a mid-sized company one or two people often cover several of these roles, and the gaps between them are where projects stall.
Data science in practice
A regional trucking company has high driver turnover. A data scientist joins HR records, dispatch logs, and pay data, and finds that irregular home time predicts resignations far better than pay level does. The recommendation is a dispatch rule that protects home time for drivers in their first year. Operations tests the rule at one terminal and compares turnover with the others before rolling it out. Kirk Tech Solutions typically pairs a data scientist with a data engineer from the first week of such work, because the usual blocker is getting clean, joined data rather than choosing a model.
What to watch for
- Fund data engineering and governance first, since a data scientist without reliable data produces slides rather than results.
- Ask every project to name the decision it will change and who will make it.
- Plan the path from notebook to production before the model is finished.
- Be wary of platforms that promise data science without data scientists, because framing the question is the hard part.