Data Science
Learn what data science is, the data science process, key tools, and how data drives modern decision-making.
What is Data Science?
Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines statistics, computer science, domain expertise, and machine learning.
The Data Science Process
- Problem Definition — What question are we trying to answer?
- Data Collection — Gathering data from databases, APIs, files, and web scraping.
- Data Cleaning — Handling missing values, removing duplicates, fixing errors. (Often 80% of the work!)
- Exploratory Data Analysis (EDA) — Visualizing and summarizing data to find patterns.
- Feature Engineering — Creating and selecting the most useful variables.
- Modelling — Applying machine learning algorithms.
- Evaluation — Measuring model performance with metrics.
- Deployment & Communication — Putting the model into production and sharing insights.
Key Tools & Technologies
- Python — The dominant language for data science. Libraries: Pandas, NumPy, Scikit-learn.
- R — Statistical computing language. Popular in academia.
- SQL — Essential for querying databases. See our SQL Tutorial.
- Jupyter Notebooks — Interactive coding environment for exploration and visualization.
- Tableau / Power BI — Data visualization and business intelligence tools.
- Matplotlib / Seaborn / Plotly — Python visualization libraries.
Statistics Fundamentals
- Mean, Median, Mode — Measures of central tendency.
- Standard Deviation & Variance — Measures of data spread.
- Probability — The foundation of predictive modelling.
- Hypothesis Testing — Determining if results are statistically significant.
- Correlation vs Causation — A critical distinction in data interpretation.
What's Next?
Learn Python for data science in our Python Tutorial, or explore how massive datasets are handled with Big Data.