What does Data Science Involve
Data Science is often pictured as just "building models," but in practice, model building is only a small part of the job. Most of the work involves preparing data and communicating findings clearly.
Typical Activities in Data Science
- Data Collection - gathering data from databases, APIs, files, or web sources.
- Data Cleaning - fixing missing values, duplicates, and inconsistent formatting.
- Exploratory Analysis - summarising and visualising data to understand patterns.
- Modeling - applying statistical or machine learning techniques to make predictions or classifications.
- Evaluation - checking how well a model performs on new, unseen data.
- Communication - presenting findings through reports, dashboards, or presentations that non-technical stakeholders can understand.
The Skills Involved
A Data Scientist typically needs a working knowledge of programming (usually Python or R), statistics, data visualisation, and enough communication skill to translate technical results into business decisions.
Industry studies consistently show that a large share of a Data Scientist's time - often 60-80% - goes into collecting and cleaning data, not building the final model.
Coming Up Next
Next, you'll trace how the field of Data Science has evolved over time.