CH7: Data Processing Flashcards
(9 cards)
What is data processing and why is it important?
Data processing means turning messy and unorganized data into clean, useful information. This helps us use the data for things like AI, reports, or decisions.
Example:
If your sales data is full of errors or random symbols, you won’t get helpful insights until you process it.
What is the main challenge with modern data?
Most data today is messy — it’s unstructured, full of errors, or in weird formats that are hard to use without fixing first.
Example:
An online store might give you a file full of product data in HTML, which is hard to read until you clean and organize it.
What are the three pillars of data processing?
The three pillars of data processing are:
1. Parsing – to break raw data into useful parts
2. Formatting – to make it look consistent
3. Cleaning – to fix or remove bad data
Example:
You might parse a document, format the names, and clean out typos.
What is Parsing in data processing?
Parsing is breaking messy data into structured pieces so we can work with it. It helps us extract meaning and organize the data.
Example:
If you scan a driver’s license, parsing picks out the name, date of birth, and address and puts them in the right boxes.
What is Formatting in data processing?
Formatting is making the structure of the data clean and consistent. This means adjusting how things are written so tools can read them.
Example:
Fixing phone numbers so they all look the same (like (123) 456-7890) or changing all text to capital letters.
What is Cleaning in data processing?
Cleaning is fixing or removing bad data like duplicates, typos, or missing values. It makes your data trustworthy and accurate.
Example:
If two people have the same email by mistake or a form is missing someone’s age, cleaning will fix those issues.
When should you parse, format, or clean data?
Use:
- Parsing when your data is messy and you need to extract meaning
- Formatting when data is structured but inconsistent
- Cleaning when data has errors or missing parts
Example:
After downloading a file, you might parse it first, then format names, then clean any duplicates.
What are best practices for beginners in data processing?
Tips for beginners:
- Start with small, simple data sets
- Write down what steps you take
- Save your workflows so you can reuse them
- Always double-check your results
- Keep improving your process over time
Example:
If you fixed your contact list once, save that process for next time.
What’s a simple analogy for understanding data processing?
Data processing is like cleaning a messy kitchen:
- Parsing is sorting out your ingredients
- Formatting is chopping and labeling them
- Cleaning is throwing away the spoiled food
Example:
Only when your kitchen is clean can you cook a great meal — just like you need clean data to do analysis.
