Purpose of Today:

Today, you will become a real data detective — learning how to:

• Find missing, wrong, or messy data,
• Clean it using Python,
• Prepare it properly for correct analysis and decision-making.

In real companies, bad data causes most analysis errors, wrong dashboards, and wrong decisions.
Strong cleaning skills = Trusted analyst.

Today’s focus:
You will practice real cleaning tasks using Python and understand how to validate your work carefully.

Today's Mission:

Master the skill of detecting, cleaning, and preparing data for analysis, using Python.
By the end of today, you will have solved real-world messy data problems hands-on and practiced explaining your cleaning process clearly.

"Before you find smart answers, make sure you’re asking clean questions."

Today's Action Plan (SPARK Method)

1. Structured Learning (S) — Deep Concept and Python Techniques

Step 1: Understand Core Data Cleaning Challenges

Ask U2xAI:
"Explain with examples: how missing values, duplicates, and outliers are handled."

Core Issues and Python Solutions:

• Missing Values
  • Cause: System errors, skipped fields, incomplete data entries.
  • Solutions:
    • Fill with default values.
    • Fill with averages (for numbers) or mode (for categories).
    • Drop rows if too much missing.

Python Example:

# Fill missing supplier names with "Unknown"
df['supplier_name'] = df['supplier_name'].fillna('Unknown')

# Drop rows missing critical information
df = df.dropna(subset=['purchase_order_id'])

• Duplicates
  • Cause: Double entries, repeated records during data transfer.
  • Solutions:
    • Detect duplicates.
    • Remove duplicates carefully.

Python Example:

# Find duplicate purchase orders
duplicates = df[df.duplicated(subset=['purchase_order_id'])]

# Remove duplicates
df = df.drop_duplicates(subset=['purchase_order_id'])

• Outliers
  • Cause: Data entry errors, real extreme values, or fraud.
  • Solutions:
    • Detect using summary statistics or visualization.
    • Investigate if outliers are real or mistakes.

Python Example:

# View unusually high purchase amounts
outliers = df[df['purchase_amount'] > 50000]  # Example threshold
print(outliers)

Highlight:

"Good analysts clean data carefully. Great analysts clean without deleting important truths."

2. Practical Case Mastery (P) — Clean a Real Messy Dataset

Step 1: Practice Cleaning a Supply Chain Dataset

Ask U2xAI:
"Generate a sample messy supply chain dataset."

Sample Dataset Columns:

• purchase_order_id
• supplier_name
• purchase_amount
• order_date

Simulated Problems:

• Missing supplier names
• Duplicate purchase_order_id
• Very high purchase_amounts (possible outliers)

Python Example to Load Data:

import pandas as pd

# Simulate messy data
data = {
    'purchase_order_id': [1001, 1002, 1003, 1002, 1004],
    'supplier_name': ['ABC Ltd.', None, 'XYZ Inc.', 'ABC Ltd.', 'Delta Corp.'],
    'purchase_amount': [12000, 5000, 55000, 5000, 7000],
    'order_date': ['2023-01-10', '2023-01-11', '2023-01-12', '2023-01-11', '2023-01-13']
}
df = pd.DataFrame(data)
print(df)

Your Tasks:

• Fill missing supplier names.
• Remove duplicate purchase orders.
• Identify potential outliers in purchase_amount.

Ask U2xAI after cleaning: "Review my cleaned dataset and suggest any missed issues."

3. Actionable Practice (A) — Solve 3 Cleaning Challenges

Assignment Set:

1. Handle Missing Supplier Names:
   • Fill with 'Unknown' or mode value.
2. Remove Duplicate Purchase Orders:
   • Keep the first occurrence, remove repeats.
3. Detect Outliers:
   • Flag purchase amounts > $50,000 for review.

Validate Results:

• Check .isnull().sum() for missing values after filling.
• Use .duplicated().sum() to confirm duplicates are removed.
• Summarize .describe() to spot any abnormal patterns.

Stretch Goal:

• Visualize purchase amounts using a simple plot.

Example:

import matplotlib.pyplot as plt

df['purchase_amount'].plot(kind='box')
plt.show()

Highlight:

"Small cleaning mistakes today = Big business disasters tomorrow."

4. Real Interview Simulations (R) — Practice Business Explanations

Mock Interview Question:

• "When you receive incomplete data, what is your process before starting analysis?"

Expected Good Answer:

• "First, I profile the data using .info(), .describe(), and missing value checks.
  Then, I prioritize fixing critical columns, document any assumptions, and clean missing/duplicate records carefully using Python."

Practice Other Questions:

• "Give an example where cleaning changed the business outcome."
• "What risks exist if we ignore outliers?"
• "How do you document cleaning steps for transparency?"

Ask U2xAI: "Score my answers based on technical clarity, business communication, and logic."

5. Killer Mindset Training (K) — Own Your Cleaning Superpower

Mindset Challenge:

• Many new analysts see cleaning as "boring" — but in reality,
  cleaning is the hidden superpower behind strong analytics.

Guided Visualization with U2xAI:

• Imagine being handed a messy spreadsheet full of missing suppliers, repeated purchase orders, and suspicious numbers.
• Visualize calmly cleaning the data step-by-step using Python.
• See yourself confidently presenting a clean, trustworthy dataset for analysis.

Daily Affirmations: "I turn messy data into valuable assets."
"I clean patiently, carefully, and professionally."
"I protect the truth inside the data."

Mindset Reminder:

"Behind every strong dashboard is a stronger cleaning process."

End-of-Day Reflection Journal

Reflect and answer:

• What part of data cleaning (missing values, duplicates, or outliers) did I find most natural?
• Where did I struggle or hesitate?
• How would I explain 'why cleaning matters' to a non-technical business stakeholder?
• How confident am I in running a full cleaning cycle now using Python? (Rate 1-10)
• What small improvement can I make tomorrow in my cleaning speed or quality?

Optional Bonus:
Ask U2xAI: "Give me 5 small messy dataset examples to clean and validate."

Today’s Learning Outcomes

By completing today’s tasks, you have:

• Learned how missing values, duplicates, and outliers impact analytics.
• Practiced solving real cleaning problems using Python hands-on.
• Built a strong cleaning checklist mindset.
• Simulated real-world interview conversations about handling incomplete data.
• Strengthened your professional confidence in cleaning and preparing datasets.

Closing Thought:

"Before a building stands strong, the ground must be leveled and cleaned. Before insights shine, the data must be prepared."