Unit 1: Introduction to data analytics
Basics of Data Analytics using Spreadsheet notes · PTU syllabus (UGDSE101)
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Unit summary
Data is now one of the most valuable resources of any organisation. Data analytics is the process of examining data to find patterns, answer questions and support decisions. This unit introduces data and its types, what data analytics is, its four types, and why it matters across industries.
After this unit you can
- Define data and classify it by type
- Explain what data analytics is and its process
- Describe descriptive, diagnostic, predictive and prescriptive analytics
- Give applications of data analytics in different fields
PTU syllabus topics
- Understanding data and its types
- what data analytics is
- types
- importance and applications of data analytics
- Prescriptive
What should we do?
- Predictive
What is likely to happen?
- Diagnostic
Why did it happen?
- Descriptive
What happened?
Topic 1
Understanding data and its types
Data is a collection of facts, numbers or observations. It becomes information when processed.
Quantitative (numerical)
Discrete (count of students) or continuous (height, temperature)
Qualitative (categorical)
Nominal (city, gender) or ordinal (rating: poor, good, excellent)
Structured
Rows and columns: spreadsheets, databases
Semi-structured
JSON, XML, emails
Unstructured
Images, videos, social media posts
Exam tip
Know the difference between nominal (no order) and ordinal (order matters) data — it decides which statistics can be used.
Topic 2
What is data analytics?
Data analytics is the process of collecting, cleaning, transforming and analysing data to discover useful information and support decision-making.
- 1
Define the question
What decision must be made?
- 2
Collect data
- 3
Clean and prepare
Fix errors, missing values
- 4
Analyse
Statistics, charts, models
- 5
Interpret and visualise
- 6
Act and communicate
Report and decide
Topic 3
Types of data analytics
- Prescriptive
What should we do? Recommends actions
- Predictive
What will happen? Forecasts
- Diagnostic
Why did it happen? Finds causes
- Descriptive
What happened? Summarises past data
Example
A college: descriptive — 20% of students failed Maths; diagnostic — most had low attendance; predictive — students below 60% attendance are likely to fail; prescriptive — start extra classes for them.
Topic 4
Importance and applications
| Field | Use of data analytics |
|---|---|
| Business and retail | Sales trends, inventory, customer segments |
| Banking and finance | Fraud detection, credit scoring |
| Healthcare | Patient records, disease prediction |
| Education | Student performance and dropout prediction |
| Sports | Player performance and strategy |
| Government | Census analysis and policy planning |
Benefits: better decisions, cost savings, understanding customers, spotting problems early and gaining a competitive edge.
Key terms
- Data analytics
- Examining data to support decisions
- Structured data
- Data organised in rows and columns
- Descriptive analytics
- Summarising what happened
- Predictive analytics
- Forecasting what will happen
- Prescriptive analytics
- Recommending what to do
Quick revision
- Quantitative (discrete, continuous) vs qualitative (nominal, ordinal).
- Process: question → collect → clean → analyse → interpret → act.
- Four types: descriptive, diagnostic, predictive, prescriptive.
Important exam questions
Practice questions written to the PTU exam pattern for this unit's syllabus: short answers (Section A style) and long answers (Sections B and C style).
Short-answer questions
- Q1.Differentiate between qualitative and quantitative data.
- Q2.Define data analytics.
- Q3.Differentiate between descriptive and predictive analytics.
- Q4.Give two applications of data analytics in banking.
- Q5.What is unstructured data?
Long-answer questions
- Q1.Explain the types of data with examples.
- Q2.Explain the four types of data analytics with an example from any industry.
- Q3.Describe the data analytics process and its importance for organisations.
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