Session 3: Experiencing Data Analysis and Additional Use Cases
What We’ll Do Today
Today we’ll work on two themes:
- Data Analysis: Analyze and visualize CSV files (spreadsheet-like data) with Claude Code
- Additional Use Cases: Try other business applications such as meeting minutes organization and manual creation
Today’s Schedule
Review of last session → Data analysis → Chart creation → Other use cases → Wrap-up
(5 min) (45 min) (25 min) (30 min) (15 min)Part 1: Data Analysis
What Is a CSV File?
A CSV file is a text file where data is separated by commas. It’s the same data format that appears as a table when opened in Excel. Today, we’ll analyze fictional sales data and customer survey data.
How It Works (Good to Know)
You give instructions in English
↓
Claude Code understands the instructions
↓
Automatically creates an analysis program (Python)
↓
The program runs on your PC
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Results are displayed- Python: A programming language used for data analysis. You don’t need to write it yourself
- pandas: A data analysis tool within Python. Think of it as a more powerful version of Excel
- The program runs on your own PC (your data is not sent externally)
Exercise 1: Examine the Data Contents
Let’s start by looking at what’s in the sample data.
claude
> Show me the contents of sales-data.csv.
> Display the first 5 rows and an overview of the whole dataset (how many rows there are, what columns it has)Things to check:
- How many rows of data are there
- What items (columns) are there
- What types of data are included
Exercise 2: Analyze Monthly Sales Trends
> Aggregate the sales amounts in sales-data.csv by month and display them in a table format that shows the trend.
> Also calculate the month-over-month change rate.Look at the analysis results and think about:
- Which month had the highest sales?
- Is there a pattern in the periods when sales grew?
Exercise 3: Product Category Analysis
> Calculate the total sales amount and composition ratio by product category.
> Also display the monthly trend for each category.Look at the analysis results and think about:
- Which category is the top earner?
- Are there categories that sell differently depending on the season?
Exercise 4: Outlier Detection
> Find any transactions in this data where the sales amount is
> extremely high or low compared to normal.
> Also explain why you identified them as outliers.Outlier detection can be used to discover data entry errors or fraudulent transactions.
Part 2: Data Visualization (Chart Creation)
Exercise 5: Let’s Create Charts
Choose one of the following and give it a try.
Task A (Basic): Create a Chart as an Image File
> From sales-data.csv, create a line chart showing monthly sales trends
> by sales channel (online, direct sales, distributor).
> Save the chart as an image file.Task B (Advanced): Create an HTML Report
> Analyze both sales-data.csv and customer-survey.csv,
> and create an HTML report that includes:
> - Monthly sales trend chart
> - Sales composition by category
> - Average score by customer satisfaction item
> Make the layout clean and easy to read.Once the HTML file is created, open it in your browser to check:
- Mac:
open filename.html - Windows:
start filename.html
Chart Customization Techniques
When you want to change the appearance of a chart, just give additional instructions:
- “Change the title to ‘FY2025 Monthly Sales Trends’”
- “Use a more business-appropriate color scheme”
- “Make the chart wider (landscape orientation)”
- “Move the legend to the bottom”
Part 3: Additional Use Cases
Mini Hands-on: Pick One and Try It
Choose one of the following and actually try it out. If you’re unsure, A. Meeting Minutes Organization is recommended.
A. Organizing and Summarizing Meeting Minutes
Use the sample meeting notes below, or use your own meeting notes.
> Organize the following meeting notes. Use this format:
> - Meeting overview (date, attendees, purpose)
> - Key points by agenda item
> - Decisions made
> - Action items (person responsible, deadline)
> - Items on hold
>
> Meeting notes:
> Monthly meeting in March. Attendees: Director Tanaka, Manager Suzuki, Sato, Yamada.
> Discussed next fiscal year's budget. Director Tanaka proposed a 10% increase over last year.
> Manager Suzuki advocated for increased IT investment. Specifically, a CRM tool implementation
> at $2,000/month. Sato will submit a cost-benefit analysis by next Friday.
> Yamada reported on the new office layout project. Expected completion by end of April.
> Social event date is on hold. To be decided at the next meeting.B. Creating an Operational Manual
> From the following procedure notes, create an operational manual for new employees.
> Include step numbers, cautions, and common mistakes.
>
> Procedure notes:
> Expense reimbursement process. First, collect receipts. Log in to the internal system
> and open the expense reimbursement menu. Enter the date, amount, and purpose. Upload
> a photo of the receipt. Send an approval request to your supervisor. Once approved,
> accounting processes it. Monthly cutoff, so submit by the 25th. Amounts over $50
> require department head approval. For transportation, IC card history is also acceptable.C. Creating a Routine Report
> From sales-data.csv, create a monthly report for the executive meeting:
> - Executive summary (3-line summary)
> - Monthly sales performance (table format)
> - Notable points (strengths and challenges)
> - Proposed actions for next month
> Save as a Markdown file.D. Translation
> Translate the following English text into Japanese.
> Make it natural Japanese suitable for business documents.
> Also create a parallel translation table.
>
> Text:
> Thank you for your continued support. We have reviewed internally the cloud
> service migration plan you proposed the other day. We agree with the basic
> approach in principle; however, we have a few questions regarding security
> requirements. We would be grateful if we could arrange a meeting sometime
> next week.Important Rules When Handling Data
| Rule | Explanation |
|---|---|
| Be careful with personal information | For data containing names, phone numbers, email addresses, etc., check your company’s rules before using it |
| Verify before using confidential data | For financial figures or unpublished financial data, check with your supervisor or information security department first |
| Specifying files is safer | Rather than pasting data directly into the prompt, it’s safer to specify a file path and say “analyze this file” |
| Practice with sample data | Before using production data, first try with sample or anonymized data |
Today’s Key Points
| What You Want to Do | Example Instruction for Claude Code |
|---|---|
| Understand data overview | ”Show me the contents of ○○.csv. Tell me how many rows and what types of columns” |
| Aggregate and analyze | ”Aggregate sales by month” “Calculate composition ratio by category” |
| Find outliers | ”Find unusual values and explain the reasons” |
| Create charts | ”Make a line chart and save it as an image” |
| Visualize in HTML | ”Create an interactive dashboard in HTML” |
| Organize meeting minutes | ”Organize the meeting notes. Separate into decisions and action items” |
| Create a manual | ”Create an operational manual from the procedure notes” |
| Translate | ”Translate English to Japanese. Make it natural for business documents” |
When Things Don’t Work
| Issue | Solution |
|---|---|
| Told the file can’t be found | Check the file location (path). Use ls to see what’s in the current folder |
| Japanese characters in charts are unreadable | Tell Claude Code: “Please recreate the chart using a Japanese font” |
| Got an error | Show the error message directly to Claude Code. It will fix it on its own |
| Processing won’t finish | For large data, instruct “Please try with only the first 100 rows” |
| Analysis results seem wrong | Ask Claude Code: “Please verify whether these results are correct” |
Preview of Next Session
Next time (Session 4) is the final session. You’ll use everything you’ve learned to tackle your own real business challenges.
Homework
- Decide on one “personal business challenge” to work on in the final session (e.g., automating a routine report, organizing data, creating a manual, etc.)
- If possible, prepare the data or files you’ll use (be mindful of confidentiality)
- Try the data analysis techniques you learned today on different data