Data Analytics

Collecting big data is only half the story — the payoff comes from analysing it. Data analytics is the process of examining data sets in order to draw conclusions about the information they contain, with the aid of specialised systems and software.

Where data analytics is used

Data analytics technologies and techniques are becoming more popular by the day, in two broad arenas:

• Commerce and industry — organisations use analytics to make more informed business decisions, replacing guesswork with conclusions drawn from their data.
• Science and technology — researchers use analytics to verify or disprove scientific models, theories and hypotheses, testing ideas against real data.

A tool you will meet later: Pandas

For hands-on data analysis, the Python ecosystem provides Pandas — a library of the Python programming language that makes data analysis much simpler. With Pandas, tabular data can be loaded, examined and summarised in a few lines of code, for example:

import pandas as pd

marks = pd.DataFrame({
    "student": ["Asha", "Ravi", "Meena"],
    "score":   [78, 85, 91],
})
print(marks["score"].mean())
``` …