Homework

1.3.3. Homework#

  • Use this assignment to practice the chapter’s concepts and coding skills in runnable Python code.

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  • Write and run your code directly in the coding cells.

  • Multiple submissions are allowed. The highest score is kept.

1. Data Science in a Retail Decision

A retail team combines sales records, Python summaries, statistics, merchandising knowledge, and a manager-facing recommendation to decide which products to reorder. Calling this work data science is appropriate.

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### Solution
# True
# The work combines data, computing, statistics, domain expertise, and communication for a decision.

2. Data Without Context

A marketing analyst has click data but does not know the campaign goal, product margins, or customer segment. The analyst can still decide the best campaign from the data alone.

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### Solution
# False
# The data needs domain context before it can support a useful decision.

3. Descriptive Questions

A store manager asks, “How many returns did each product category have last month?” This is mainly a descriptive data science question.

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### Solution
# True
# The question summarizes what already happened.

4. Predictive and Prescriptive Questions

An operations director asks, “Which shipments are most likely to arrive late next week?” This is mainly a prescriptive question.

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### Solution
# False
# The question estimates a future outcome, so it is predictive. A prescriptive question would recommend an action.

5. Formal Language Rules

A finance script has the right formula but is missing a closing quote in one print() statement. Because the business idea is correct, Python syntax rules will not affect whether the program runs.

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### Solution
# False
# Python is a formal language; syntax errors can stop a program before the business logic runs.

### 6. Classify Management Questions
# A customer success team is reviewing subscription cancellations.
# Store the correct label for each question: descriptive, predictive, or prescriptive.
# Then print each label with the question name.
#
# question_a: How many customers canceled last month?
# question_b: Which customers are most likely to cancel next month?
# question_c: Which retention offer should we send first?

question_a = ""
question_b = ""
question_c = ""

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### Solution
question_a = "descriptive"
question_b = "predictive"
question_c = "prescriptive"

print("Question A:", question_a)
print("Question B:", question_b)
print("Question C:", question_c)

Question A: descriptive
Question B: predictive
Question C: prescriptive

### 7. Write a Stakeholder Status Line
# The lab asked you to print technical values. Here, turn values into a concise status line.
# Use an f-string and the variables below to print exactly:
# North store has 7 late shipments out of 80.

store = "North"
late_shipments = 7
total_shipments = 80

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### Solution
store = "North"
late_shipments = 7
total_shipments = 80

print(f"{store} store has {late_shipments} late shipments out of {total_shipments}.")

North store has 7 late shipments out of 80.

### 8. Fix a Syntax Error
# The lab asked you to write correct statements. Here, practice recognizing broken syntax.
# The two lines below have syntax problems:
# print("DSM homework"
# print("Syntax fixed.")
# Write corrected code that prints exactly:
# DSM homework
# Syntax fixed.

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### Solution
print("DSM homework")
print("Syntax fixed.")

DSM homework
Syntax fixed.

### 9. Build an Inventory Code
# The lab printed separate number-system and character-code values.
# Here, combine those pieces into one inventory code.
# Use ord() and hex() with the variables below to print exactly:
# Inventory code: R-82-0x2d

category = "R"
item_id = 45

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### Solution
category = "R"
item_id = 45
category_code = ord(category)
item_hex = hex(item_id)

print(f"Inventory code: {category}-{category_code}-{item_hex}")

Inventory code: R-82-0x2d

### 10. Calculate Audit Metrics
# The lab calculated an order total. Here, calculate simple management metrics from case counts.
# Use the variables below to calculate open_cases and closure_rate.
# Print exactly:
# Open cases: 17
# Closure rate: 0.67

starting_cases = 20
new_cases = 7
closed_cases = 10
assigned_cases = 15

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### Solution
starting_cases = 20
new_cases = 7
closed_cases = 10
assigned_cases = 15

open_cases = starting_cases + new_cases - closed_cases
closure_rate = closed_cases / assigned_cases

print("Open cases:", open_cases)
print("Closure rate:", round(closure_rate, 2))

Open cases: 17
Closure rate: 0.67