📊 Types of Data & Data Collection Methods in Data Science (Part 2)

Understanding data is the first and most important step in data science.

Before analysis, modeling, or machine learning, a data scientist must know what type of data they are working with and how it was collected.

In Part 2 of our Statistics for Data Science series, you’ll learn:

• Different types of data used in data science
• How data is collected in real-world projects
• Sampling methods and their importance
• Hands-on examples to classify datasets

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🎯 Goal of This Post

Understand your data before analyzing it.

Incorrect data understanding leads to:

• Wrong statistical methods
• Poor model performance
• Misleading insights

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📌 Types of Data in Data Science

Data can be classified in multiple ways depending on its nature and usage.

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🔹 Qualitative vs Quantitative Data

📘 Qualitative Data (Categorical Data)

Qualitative data describes qualities or characteristics and is non-numeric.

Examples:

• Gender (Male/Female)
• Product category
• Customer feedback (Good, Bad, Average)
• City names

📌 Used for:

• Classification
• Sentiment analysis
• Grouping and segmentation

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📗 Quantitative Data (Numerical Data)

Quantitative data represents numbers and measurable values.

Examples:

• Age
• Salary
• Temperature
• Number of purchases

📌 Used for:

• Statistical calculations
• Regression models
• Forecasting

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🔹 Discrete vs Continuous Data

📘 Discrete Data

Discrete data consists of countable values.

Examples:

• Number of customers
• Number of defects
• Number of website visits

📌 Values are whole numbers.

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📗 Continuous Data

Continuous data can take any value within a range.

Examples:

• Height
• Weight
• Time
• Temperature

📌 Can have decimal values.

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🔹 Structured vs Unstructured Data

📘 Structured Data

Structured data is organized in rows and columns.

Examples:

• Excel files
• SQL tables
• CSV datasets

📌 Easy to analyze using SQL, Excel, Python, or BI tools.

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📗 Unstructured Data

Unstructured data has no predefined format.

Examples:

• Text documents
• Emails
• Images
• Videos

• Social media posts

📌 Requires advanced processing (NLP, Computer Vision).

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📌 Data Collection Methods in Data Science

Understanding how data is collected helps assess data quality and bias.

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🔹 Common Data Collection Techniques

1️⃣ Surveys & Questionnaires

• Online forms
• Feedback surveys
• Market research

📌 Risk: Response bias

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2️⃣ Observational Data

• Website click tracking
• User behavior logs
• Sensor data

📌 Real-time and unbiased

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3️⃣ Experiments (A/B Testing)

• Marketing experiments
• Product feature testing

📌 Controlled and reliable

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4️⃣ Transactional Data

• Sales records
• Banking transactions
• E-commerce logs

📌 Highly structured and reliable

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5️⃣ Third-Party Data

• Government datasets
• APIs
• External vendors

📌 Verify credibility and freshness

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📌 Sampling Methods in Statistics

Sampling allows us to study a subset of data instead of the entire population.

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🔹 Types of Sampling Methods

📘 Random Sampling

• Every unit has equal chance
• Reduces bias

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📘 Stratified Sampling

• Population divided into groups (strata)
• Sample taken from each group

📌 Used in surveys and finance

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📘 Systematic Sampling

• Every nth observation selected

📌 Simple and efficient

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📘 Convenience Sampling

• Easily available data

📌 Risk: High bias

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📌 Why Sampling Matters in Data Science

• Saves time and cost
• Makes large datasets manageable
• Enables faster experimentation
• Supports inferential statistics

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🧪 Hands-On: Classify Sample Datasets

Let’s classify real-world datasets.

Dataset	Qualitative / Quantitative	Discrete / Continuous	Structured / Unstructured
Customer Gender	Qualitative	Discrete	Structured
Monthly Salary	Quantitative	Continuous	Structured
Product Reviews	Qualitative	N/A	Unstructured
Number of Orders	Quantitative	Discrete	Structured
Website Session Time	Quantitative	Continuous	Structured

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🧠 Key Takeaways

✔ Always identify data type before analysis
✔ Choose statistical methods based on data nature
✔ Understand data collection to avoid bias
✔ Sampling impacts accuracy and conclusions

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🔗 What’s Next in This Series?

👉 Part 3: Descriptive Statistics – Mean, Median, Mode & Variability