WISDOM

Geophysicist | data analyst | tutor | lover of God | Chelsea fan
[email protected]

[WISDOM @obeto_wisdom13]

@Blue_Footy We lack midfielders
If you cannot control a game like this when you have possession expect nothing
Couldn't play through the press

How do you have 125m and can't get a good midfielder

[WISDOM @obeto_wisdom13]

@Blue_Footy Two very good ball playing cm and this game is over
Barco is too light to control
Henderson can't do much

[WISDOM @obeto_wisdom13]

@litfiae 🤣

[WISDOM @obeto_wisdom13]

@Blue_Footy Colwill and hato are still in preseason

[WISDOM @obeto_wisdom13]

@Blue_Footy This guy needs to grow up
Too much quality but immature

[WISDOM @obeto_wisdom13]

@Blue_Footy No chance

[WISDOM @obeto_wisdom13]

@CasmirCodesData Date column is always something else

[WISDOM @obeto_wisdom13]

@PoojaMedia It's just preseason

[WISDOM @obeto_wisdom13]

@Buchi_Laba We'll give you garnacho no need to sleep

[WISDOM @obeto_wisdom13]

@Blue_Footy Athletico Madrid can still pay

[WISDOM @obeto_wisdom13]

@Blue_Footy Sometimes it's not always the big names

[WISDOM @obeto_wisdom13]

@Buchi_Laba

[WISDOM @obeto_wisdom13]

@mrbayoa1

[WISDOM @obeto_wisdom13]

@_VictorUgwu @ObohX @RxDono @AnalystEmmanuel @paschaldubem21 @Data_Chy 🤣🤣🤣

[WISDOM @obeto_wisdom13]

@indykaila @ChelseaFC Lolz

[WISDOM @obeto_wisdom13]

@Femiforge LEFT JOIN / WHERE IS NULL

[WISDOM @obeto_wisdom13]

@PoojaMedia 3 hrs later

[Ezekiel @ezekiel_aleke]

Learn❗️

[Shefali @Shefali__J]

Python Roadmap🔥

Open this🧵
Bookmark for later🔖

[Annie🦋 @DabereNnamani]

Data Analysts!!

The next time you’re looking for a dirty dataset to clean to practice your data cleaning skill, use this prompt to generate the data from Ai.

Save for later.

“You are a data generator simulating real-world datasets for data analysis practice.

Create a dataset with the following specifications:

1. Domain / Context:
- [INSERT DOMAIN: e.g., e-commerce, healthcare, banking, education, logistics]

2. Dataset Size:
- Generate [X] rows

3. Columns (with data types and meaning):
- Provide [10–20] columns including a mix of:
- Numerical (integers, floats)
- Categorical (nominal + ordinal)
- Text fields
- Dates/timestamps
- IDs (some structured, some inconsistent)

4. Intentional Data Quality Issues (VERY IMPORTANT):
Introduce realistic “dirty data” problems such as:
- Missing values (random + patterned)
- Duplicate rows and duplicate IDs
- Inconsistent formats (e.g., dates: DD/MM/YYYY vs MM-DD-YY)
- Typographical errors in categorical values
- Mixed units (e.g., kg vs lbs, USD vs NGN)
- Outliers and extreme values
- Invalid entries (e.g., negative ages, impossible dates)
- Inconsistent capitalization and whitespace issues
- Corrupted or partially truncated text fields
- Columns with mixed data types

5. Relationships:
- Include at least 2–3 meaningful relationships between variables
- Add some noise that weakens perfect correlations

6. Output Format:
- Provide the dataset as a table (CSV format preferred)
- Include column headers

7. Additional Context:
- Briefly describe what each column represents
- Mention key data issues intentionally inserted (but do not fix them)

8. Difficulty Level:
- Make this dataset suitable for intermediate to advanced data cleaning and exploratory data analysis

Important:
- Do NOT make the dataset perfectly clean
- Prioritize realism over neatness
- Ensure the dataset looks like something collected from real operations”