Data science explained
Data science combines domain knowledge, statistics and computing to extract useful evidence from data. The work may include defining a question, collecting information, cleaning records, exploring patterns, building models and communicating results.
The data-science workflow
A project starts with a measurable objective. Analysts inspect data quality, handle missing values, create useful features and visualize relationships. Models are evaluated on unseen data using metrics that reflect the real decision.
Tools used by data scientists
Python, SQL, Pandas, NumPy and Jupyter are common foundations. Matplotlib, Seaborn, Power BI and Tableau communicate findings. Scikit-learn supports machine learning, while Git and reproducible environments help teams review the work.
How to build a data portfolio
Choose realistic datasets and explain the problem, assumptions, cleaning, analysis, model evaluation and limitations. A clear report and reproducible code are more valuable than an unexplained high accuracy score.
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