Introduction to Data Analytics
Data analytics means studying data to find useful information. Data is simply facts, numbers, or details that we collect in daily life. When we organise and study this data, we can understand patterns, make decisions, and solve problems.
For example, when you check which subject you score lowest in, you look at your marks and analyse them. This is a small form of data analytics. Companies use data analytics to understand customers, improve services, and earn more profit. This topic helps students understand how data is collected, stored, analysed, and used in real life and jobs.
Data analytics = studying data to find meaning
Helps in decision making
Used in business, education, health, and technology
Real-life example:
Online shopping apps suggest products
based on what you searched earlier. They analyse your data to do this.
Sources and Nature of Data
Data comes from many places and in many forms. Every time you use a mobile phone, browse the internet, or fill a form, data is created. Sources of data mean where data comes from. Nature of data means what type of data it is and how it looks. Some data comes from people, some from machines, and some from websites. Understanding sources and nature helps us choose the right way to store and analyse data.
User input (forms, surveys)
Machines (sensors, cameras)
Websites and apps
Company records
Real-life example:
When you register on a college
portal, your name, age, and course become data collected from a form.
Classification of Data
Data can be divided into three main types based on its structure. Structure means how neatly data is organised. Some data fits well into tables, while some does not. Knowing these types helps in selecting tools and methods for analysis.
Structured Data
Structured data is neatly organised in rows and columns. It looks like a table. This type of data is easy to store and search. Most databases use structured data. Marksheets, bank records, and attendance lists are good examples.
Stored in tables
Easy to search
Well organised
Real-life example:
Your exam result sheet with roll
number, name, and marks is structured data.
Semi-Structured Data
Semi-structured data is partly organised. It does not fit perfectly into tables but still has some order. It usually uses tags or labels to organise data. It is more flexible than structured data.
Partly organised
Uses tags or labels
Not in strict table form
Real-life example:
Emails. They have sender, receiver,
subject, and message body.
Unstructured Data
Unstructured data has no fixed format. It can be text, images, audio, or video. This type of data is difficult to analyse because it is messy and large.
No fixed structure
Hard to process
Large in size
Real-life example:
Photos on Instagram and videos on
YouTube.
Comparison Table
| Type | Organisation | Example |
|---|---|---|
| Structured | Table format | Marks list |
| Semi-structured | Partly organised | |
| Unstructured | No format | Video |
Characteristics of Data
Data has some special features that describe its behaviour. These features help us understand how big, fast, and useful the data is. These characteristics are often called the 5 V’s.
Volume
Volume means amount of data. Today, companies store huge amounts of data.
Example:
Millions of photos uploaded daily on Facebook.
Velocity
Velocity means speed of data generation. Data is created very fast.
Example:
Live chat messages on WhatsApp.
Variety
Variety means different types of data.
Example:
Text, images, videos, audio.
Veracity
Veracity means data quality. Data should be correct.
Example:
Wrong phone number in college database causes
problems.
Value
Value means usefulness of data.
Example:
Sales data helps company plan offers.
Introduction to Big Data Platform
Big Data Platform is a system used to store and process very large data. Normal computers cannot handle huge data easily. Big data platforms use many computers working together. They allow fast storage and fast processing.
Stores large data
Processes data quickly
Uses many computers
Real-life example:
Google stores billions of search
records every day.
Need of Data Analytics
Data analytics is needed because raw data alone has no meaning. We must analyse it to find patterns and answers. It helps organisations make better decisions. It saves time and money.
Better decision making
Find problems early
Improve services
Example:
College analyses attendance data to find
students who need help.
Evolution of Analytic Scalability
Earlier, data was small and simple. One computer could handle it. Today, data is huge. Systems must grow as data grows. This ability to grow is called scalability. Modern systems can handle more data by adding more machines.
Earlier: small data
Now: very large data
Systems grow with data
Example:
Cloud storage increases space when needed.
Analytic Process and Tools
The analytic process is a step-by-step method to analyse data. First, data is collected. Then it is cleaned, analysed, and results are shown. Tools are software used to perform these steps.
Collect data
Clean data
Analyse data
Show results
Example:
Excel is a simple data tool used in colleges.
Analysis vs Reporting
Analysis means finding meaning from data. Reporting means showing data in charts or tables. Reporting shows what happened. Analysis explains why it happened.
Comparison Table
| Aspect | Analysis | Reporting |
|---|---|---|
| Purpose | Find reason | Show facts |
| Output | Insights | Charts |
| Example | Why sales fell | Monthly sales chart |
Modern Data Analytic Tools
Modern tools help analyse big and complex data easily. Many tools also show results in graphs and dashboards.
Excel
Power BI
Tableau
Python
Example:
Power BI shows sales data in colourful charts.
Applications of Data Analytics
Data analytics is used in many fields. It improves life and work.
Business – sales prediction
Healthcare – disease detection
Education – student performance
Banking – fraud detection
Example:
Netflix recommends movies based on viewing
history.
Exam Tip 📝
-
Remember definitions of structured, semi-structured, and unstructured data
Know difference between analysis and reporting
Learn 5 V’s of data
Possible Exam Questions
Short Answer
Define data analytics.
What is structured data?
Explain volume and velocity.
Long Answer
Explain classification of data with examples.
Describe analytic process and tools.
Explain applications of data analytics.
Remember This 📌
Data analytics = turning data into useful information
Big data platforms handle large data
Tools help analyse and visualise data
Detailed Summary
Data analytics is the study of data to find useful information. Data comes from many sources such as forms, machines, and websites. Data can be structured, semi-structured, or unstructured. Data has special characteristics like volume, velocity, variety, veracity, and value. Big data platforms store and process huge data. Data analytics is needed for better decisions. Scalability allows systems to grow with data. The analytic process follows steps from collection to result display. Analysis finds meaning, while reporting shows facts. Many modern tools help analyse data. Data analytics is used in business, health, education, and many more fields. This topic builds a strong base for careers in IT and data-related jobs.
Key Takeaways
Data is everywhere
Analytics gives meaning
Tools make work easy
Used in many industries