Learn Data Science
Data science is the practice of turning raw data into decisions by moving it through a repeatable pipeline: collect, clean, explore, model, and evaluate, using a small stack of Python libraries built for exactly that.
What Is Data Science?
Data science is the discipline of extracting useful, reliable knowledge from data. It borrows from statistics (to reason about uncertainty), programming (to handle data at scale), and domain knowledge (to know which questions are worth asking in the first place).
It differs from plain software engineering in one key way: instead of writing rules by hand, you let patterns in the data suggest the rules, then you test whether those patterns hold up on new data.
The Data Science Workflow
- Collect - gather raw data from files, databases, APIs, or sensors.
- Clean - handle missing values, fix inconsistent formatting, and remove duplicates.
- Explore - summarize and visualize the data to find patterns and outliers.
- Model - fit a mathematical or statistical model that captures the pattern.
- Evaluate - measure how well the model performs on data it hasn't seen.
Tools of the Trade
Each of these libraries has its own dedicated tutorial on this site covering its syntax in depth. This course assumes you can read basic Python and instead focuses on how the libraries hand data to one another inside a real workflow.
Example
# A data science pipeline, one import per stage
import numpy as np # fast numeric arrays
import pandas as pd # tables for cleaning data
import matplotlib.pyplot as plt # charts for exploring data
from sklearn.linear_model import LinearRegression # models for predicting
# collect -> clean -> explore -> model -> evaluate
# this tutorial focuses on that hand-off between stages,
# not on re-teaching any single library from scratch.Why Python?
Python reads almost like plain English, which keeps the focus on the data rather than the syntax. Its numeric libraries are implemented in fast, compiled code under the hood, so you get the readability of Python without giving up performance on large datasets.
Exercise: Data Science Introduction
What best describes data science as a field?
Frequently Asked Questions
- What is the difference between data science and data analysis?
- Analysis explains what happened using existing data and reporting. Data science leans further into prediction and modelling, and usually involves more programming and statistics. The titles overlap heavily in practice, and many jobs advertised as one are really the other.
- Which should I learn first, Python or statistics?
- Run them together. Python gives you something to try immediately, which keeps motivation up, while statistics stops you drawing confident conclusions from noise. Learning tools without the statistics behind them produces analyses that look convincing and are wrong.
- Do I need a maths degree for data science?
- No, but you need the maths. Descriptive statistics, probability, distributions and the logic of hypothesis testing cover most applied work. Linear algebra and calculus become important when you move into modelling rather than analysis.
