Exercises — Chapter 1: Why Python? The Business Case for Coding

Tier 1: Recall

1.1 List three categories of business tasks where Python has a clear advantage over Excel. For each, give one concrete example.

1.2 What are the four core characteristics of Python that make it well-suited for business professionals (according to Section 1.2)?

1.3 True or False: Python replaces the need for Excel in most business workflows. Explain your answer.

1.4 What does "open source" mean, and why does it matter for a business professional evaluating tools?

1.5 According to Section 1.4, what is the estimated break-even point for automating a 2-hour recurring task with Python?

Tier 2: Apply

2.1 ★ Think of a task you do regularly that involves data or files. Estimate: (a) how long it takes you currently, (b) how often you do it, (c) the annual time cost, and (d) what you could do with that time if it were automated.

2.2 Using the ROI framework from Section 1.6, calculate the 3-year return for automating a task that currently takes 4 hours and happens monthly, assuming 6 hours of Python work to automate it.

2.3 Compare Python and VBA for the following scenario: a company wants to automate a process that involves pulling data from a database, combining it with data from an API, and sending a formatted report via email. Which tool is more appropriate, and why?

2.4 Maya's invoice problem involves three types of costs: time cost, late payment cost, and decision cost. Which of these three do you think Python addresses most completely? Which least completely? Why?

2.5 A colleague says: "I tried learning Python two years ago and gave up. The examples were all about sorting numbers and I couldn't see how it applied to my work." Using what you've read in this chapter, explain why that experience is common and what a better learning approach would look like.

Tier 3: Analyze

3.1 ★ Priya's Monday morning report takes 2 hours. Her manager argues that Priya should spend that time on "higher-value work" instead of automating. Priya argues the automation itself is higher-value work. Analyze both positions. Who is right, under what conditions?

3.2 The chapter describes Python as complementing Excel rather than replacing it. Identify three specific scenarios where Excel remains the better tool even for someone who knows Python well. What properties of Excel make it superior in those scenarios?

3.3 The "Knowledge Dependency" problem (Section 1.4) describes what happens when only one person knows how to do a critical task. How does Python automation change — and how does it not change — this organizational risk? Consider the possibility that the Python script itself creates a new kind of knowledge dependency.

3.4 In Section 1.5, Python is compared to R. A data scientist argues that R is actually the better choice for business analytics because of its statistical depth and ggplot2 visualization library. Construct the strongest possible counter-argument for Python in a business (not academic) context.

3.5 The chapter says that Python democratizes data skills. But learning Python still requires significant time and effort. What organizational and structural factors might prevent the democratization promise from being realized, even if the tools are technically accessible?

Tier 4: Synthesize

4.1 Design an "Automation Audit" process for a business department of your choosing. What questions would you ask to identify the highest-ROI automation opportunities? What criteria would you use to prioritize which tasks to automate first?

4.2 The chapter mentions that "the data economy" is creating competitive advantage for organizations that can work with data better than competitors. Describe a specific industry (not technology) where you see this dynamic playing out, and explain what specific Python capabilities would be most valuable to a professional in that industry.

4.3 Write a one-page memo to a skeptical manager who believes Python is "just for tech people" and that the current Excel-based process is "working fine." Your memo should make the business case for investing in Python training for the analytics team. Use specific examples and quantify where possible.

4.4 Maya's story involves three connected problems: billing inefficiency, late payments, and business visibility. Design a minimal viable Python solution that addresses all three. You don't need to write code — describe the inputs, outputs, and logic for each component, and how they connect to each other.

Tier 5: Challenge

5.1 (Open-Ended) The chapter describes two archetypes: the business professional who hasn't learned Python (Analyst A / Maya's current state) and the one who has (Analyst B / Maya's future state). Identify a third archetype: a business professional who has Python skills but is applying them poorly or in the wrong contexts. What does that misapplication look like, and what causes it? What can the Tier 2 Python learner do to avoid becoming this archetype?

5.2 (Open-Ended) The democratization of technical skills often has second-order effects that aren't immediately obvious. For example, when Excel became widely accessible, it created both new capabilities and new failure modes (the "Excel hell" problem of uncontrolled spreadsheet proliferation, version chaos, and undocumented formulas). Predict the second-order effects of widespread Python adoption in business settings. What new problems might emerge that this chapter doesn't acknowledge?

5.3 (Research) Research one high-profile case of a business error caused by a manual data process (CSV exports, copy-paste errors, Excel formula mistakes, etc.). Analyze: (a) what went wrong, (b) what the consequences were, (c) whether Python automation could have prevented it, and (d) what safeguards would be needed in the automated version to ensure the same error class doesn't occur differently.