Analytics & BI: Track Setup

Complete the platform setup first if you haven't already. You should have a terminal, Claude Code, Git, and a GitHub account ready.

1. Create your track folder

mkdir -p ~/dev/analytics
cd ~/dev/analytics

2. Analytics tools: let Claude Code do it

Open Claude Code in your track folder:

claude

Paste this prompt:

I'm setting up an analytics and BI environment. Please:

1. Install Python 3.11+ via Miniconda, then create a conda environment called "analytics"
2. Install core packages in the analytics environment: pandas, jupyter, matplotlib, 
   seaborn, plotly, scipy, statsmodels, duckdb
3. Check if Docker is installed. If not, tell me how to install it (it needs admin access)

After each step, verify it worked and show me the result.

Note on Docker: Docker typically needs administrator access, and you'll need it for Metabase (the BI platform) in early projects. If Claude Code can't install it directly, it will tell you what command to run yourself.

Verify

Once Claude Code finishes:

conda activate analytics
python --version
python -c "import pandas; import matplotlib; import plotly; import scipy; import duckdb; print('All packages installed')"
jupyter notebook --version

You should see Python 3.11+, "All packages installed", and a Jupyter version number.

3. Your first look

Everything is installed. Before you start Project 1, see what Claude Code can do when you point it at an analytics problem.

Stay in your track folder with Claude Code open, and paste this:

Create a small CSV dataset of 500 e-commerce orders with columns: order_date, 
product_category, revenue, customer_segment, region, is_returned. Then load it 
into DuckDB, write SQL queries to find: total revenue by category, return rate 
by segment, month-over-month revenue trend. Produce 3 clean charts that a 
stakeholder could understand at a glance. Write a brief executive summary of 
the key findings.

In a few minutes, Claude will generate the data, run SQL analysis, produce stakeholder-ready charts, and write a summary. A complete analytics workflow from a single prompt.

As you work through the track, you'll learn why a single prompt isn't enough: why that revenue calculation might use the wrong definition, why those charts might be misleading, why "return rate by segment" might hide a Simpson's paradox, and why a stakeholder would challenge your conclusions.

But for now, look at what just happened. That's the starting point.

Ready

Start Project 1.