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Basics of Data Handling
Understanding Data Types, Data Formats, and Data Quality is crucial for effective data handling. This section covers various aspects of data types, formats, and how to ensure data quality for meaningful analysis.
Understanding Data Types
Numerical Data: Quantitative data that can be measured and ordered.
Discrete Data: Countable values (e.g., number of students).
Continuous Data: Measurable quantities that can take any value within a range (e.g., height, weight).
Categorical Data: Qualitative data that can be categorized or labeled.
Nominal Data: Categories without a specific order (e.g., colors).
Ordinal Data: Categories with a meaningful order (e.g., ratings).
Data Formats
CSV Files: Comma-separated values, commonly used for storing tabular data.
Excel Files: Spreadsheets that can contain multiple sheets and complex data.
JSON Files: JavaScript Object Notation, used for storing structured data in a readable format.
SQL Databases: Structured Query Language, used for managing and querying relational databases.
Data Quality
Accuracy: Correctness and precision of data.
Completeness: Inclusion of all necessary data without missing elements.
Consistency: Uniformity of data across different datasets or sources.
Timeliness: Relevance and up-to-date status of data.
Example
Consider a company that stores customer information in a CSV file. The data includes names, ages, and purchase amounts. Ensuring the accuracy and completeness of this data is important for meaningful analysis.
Activity
Download a sample CSV file from the internet and open it in Excel. Explore the data and try to identify any errors or inconsistencies.
Quiz
1. What is the first step in data handling?
- a) Data visualization
- b) Data collection
- c) Data mining
- d) Data analysis
2. True or False: Data cleaning involves removing duplicate entries and fixing errors.
- a) True
- b) False
3. What does ETL stand for in data handling?
- a) Extract, Transform, Load
- b) Extract, Test, Load
- c) Edit, Transform, Load
- d) Edit, Test, Load
4. Which of the following is NOT a method for data integration?
- a) Data blending
- b) Data warehousing
- c) Data misalignment
- d) Data virtualization
5. What is the purpose of data transformation?
- a) To change data into a different format
- b) To visualize data
- c) To collect data
- d) To store data
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