Understanding Data: Collect, Store, Process

What is data?

Data is a collection of raw facts and figures — numbers, words, measurements, observations, images or sounds. On its own, data may not mean much. When it is organised and processed, it becomes information that helps us understand something or take a decision.

Why data matters

Types of data

Data can also be qualitative (descriptive: colour, grade, city) or quantitative (numeric: height, price, temperature). Quantitative data is either discrete (counted: number of students) or continuous (measured: height 162.5 cm).

Data collection

Data collection means gathering facts from relevant sources so that they can be analysed. First decide what data you need and why.

Data storage

Collected data must be stored safely so it can be used later. Small data may be kept in files (text, CSV, spreadsheet); large or shared data is kept in databases (MySQL, which you study in Unit 4). Storage can be on local devices (HDD, SSD, pen drive) or in the cloud (Google Drive, OneDrive, company servers).

📁 survey_project/
├── raw_responses.csv     # data exactly as collected
├── clean_data.csv        # after removing errors
├── analysis.py           # Python program for processing
└── report.pdf            # final information with charts

Data processing

Data processing is the set of operations that convert raw data into useful information.

Cleaning and processing data in Python

Real survey data is messy: extra spaces, words instead of numbers, blanks and impossible values. Python can clean it:

# Raw survey data: hours of screen time per day
responses = ['3', '5', ' 2', 'four', '6', '', '3', '25']
clean = []
for r in responses:
    r = r.strip()
    if r.isdigit() and 0 <= int(r) <= 24:     # keep only valid hours
        clean.append(int(r))
print('Raw responses :', len(responses))
print('Valid values  :', clean)
print('Average hours :', round(sum(clean) / len(clean), 2))

Turning data into information:

# Data -> information: classify readings
readings = {'Mon': 41, 'Tue': 38, 'Wed': 44, 'Thu': 36, 'Fri': 45}
for day, temp in readings.items():
    label = 'Heatwave' if temp >= 40 else 'Normal'
    print(day, temp, label)
hot = [d for d, t in readings.items() if t >= 40]
print('Heatwave days:', len(hot), hot)

Summary & practice

• Data = raw facts; information = processed, meaningful data.
• Structured vs unstructured; qualitative vs quantitative (discrete/continuous).
• Primary data is collected by you; secondary data already exists.
• Processing: collect → clean → organise → analyse → present.