Data Analytics
Data Analytics - A Simple Introduction
Data Analytics is the process of exploring raw data to find trends, answer user questions, and uncover insights. We can think of it as detective work for numbers - taking a messy pile of facts and turning them into a clear story so people can make smarter decisions.
Instead of guessing, data analytics uses actual data to guide the way.
General Process
As shown below, analytics follows a step-by-step path: it takes Raw Data, organizes it into Structured Data, cleans it up (Data Preprocessing), explores the patterns (Exploration Data Analysis), and finally delivers Insights, Reports, and Visual Graphs that anyone can understand.
Raw data-> Structured Data -> Data Preprocessing-> Exploration Data Analysis (EDA)-> Insights, Reports, Visuals
Simple Example
The Grocery Store (Market Baskets)
- The Raw Data: A list of 10,000 digital bill.
- The Analytics: A program notices that 70% of people who buy milk also buy potatoes at the same time.
- The Action: The store places milk right next to the potatoes so customers can find them easily, which increases sales, saves shopping time of cusotmers.
Streaming Apps (Personalization)
- The Raw Data: A history of every show you click on, skip, or watch to the end.
- The Analytics: The system clusters your viewing habits with millions of other users who share your exact taste in romantic comedies.
- The Action: The app accurately predicts and recommends a brand-new rom-com movie you will love on your home screen.
Fitness Trackers (Health Trends)
- The Raw Data: Thousands of individual, continuous heart rate measurements taken by a smartwatch throughout the week.
- The Analytics: The app calculates a rolling 10-day moving average of your resting heart rate.
- The Action: It sends an alert showing that your resting heart rate has steadily spiked over the last few days, giving you an early warning that your body is stressed or fighting off a cold.