// Tableau — calculated fields
// Tableau is drag-and-drop, but calculated fields
// are what separate a basic dashboard from a useful one.

Revenue
-------
[Qty] * [Unit Price]

Order Size
----------
IF [Revenue] >= 10000 THEN "Large"
ELSEIF [Revenue] >= 3000 THEN "Medium"
ELSE "Small"
END

Profit Margin
-------------
IF [Revenue] = 0 THEN NULL
ELSE [Profit] / [Revenue]
END

// Interviewers rarely ask you to recite functions —
// they ask you to explain WHY a calculation belongs
// in the view vs in the data source.

// Tableau — Level of Detail (LOD) expressions
// The Tableau skill that interviewers test most,
// because it answers "per category" questions that
// simple aggregations cannot.

// First order date per customer
First Order Date
----------------
{ FIXED [Customer ID] : MIN([Order Date]) }

// New vs returning customers
Customer Type
-------------
IF [Order Date] = [First Order Date]
THEN "New"
ELSE "Returning"
END

// Total revenue per customer, on every row
Customer Revenue
----------------
{ FIXED [Customer ID] : SUM([Revenue]) }

// Rule of thumb:
// FIXED   -> ignore the view's filters/grouping
// INCLUDE -> add extra dimension to the view
// EXCLUDE -> remove a dimension from the view

// Tableau — table calculations
// These run on the RESULT set, not the raw rows.
// That single fact explains most "why is my number
// different?" questions in interviews.

Running Revenue
---------------
RUNNING_SUM(SUM([Revenue]))

% of Total Revenue
-----------------
SUM([Revenue]) / TOTAL(SUM([Revenue]))

Month-over-Month Growth
-----------------------
(SUM([Revenue]) - LOOKUP(SUM([Revenue]), -1))
/ ABS(LOOKUP(SUM([Revenue]), -1))

// Remember to set "Compute Using" to Month.
// Table calcs are correct only when the addressing
// direction matches what the business question means.

# Dockerfile — a small Python web app
FROM python:3.12-slim

WORKDIR /app

# Install dependencies first (better layer caching)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy the app code
COPY . .

EXPOSE 8000

CMD ["python", "app.py"]

# Build and run:
#   docker build -t my-app:1.0 .
#   docker run -p 8000:8000 my-app:1.0

# Two things interviewers look for:
# 1. Layer-caching order (dependencies before code)
# 2. Small, specific base images (slim, not "latest")

# Docker CLI — the commands you will actually use
# Build an image
docker build -t my-app:1.0 .

# Run a container in the background
docker run -d -p 8000:8000 --name myapp my-app:1.0

# See what's running
docker ps

# Follow logs
docker logs -f myapp

# Open a shell inside the container
docker exec -it myapp bash

# Stop and remove
docker stop myapp
docker rm myapp

# Clean unused images and containers
docker system prune -f

# docker-compose.yml — a small stack
# App + database, wired together

services:
  web:
    build: .
    ports:
      - "8000:8000"
    depends_on:
      - db
    environment:
      - DATABASE_URL=postgresql://app:app@db:5432/app

  db:
    image: postgres:16
    environment:
      - POSTGRES_USER=app
      - POSTGRES_PASSWORD=app
      - POSTGRES_DB=app
    volumes:
      - dbdata:/var/lib/postgresql/data

volumes:
  dbdata:

# Run the stack:   docker compose up -d
# Tear it down:    docker compose down

Tableau — 30-day beginner plan:
Week 1: Connect data, core chart types, formatting
Week 2: Filters, calculated fields, parameters
Week 3: Dashboards, actions, LOD expressions
Week 4: One polished dashboard on a public dataset
        + publish to Tableau Public

Result: A shareable portfolio link.
        Interview-ready for junior analyst roles.

Total effort: ~30 hours. No coding prerequisites.

Docker — 30-day plan:
(assumes Linux/CLI basics + one small app)

Week 1: Install, images vs containers, first run
Week 2: Dockerfile, build, run your own app
Week 3: Volumes, networking, docker compose
Week 4: Deploy a small stack + read logs, fix errors

Result: Confident Docker user — a skill that
        shows up in backend, DevOps, and MLOps roles.

Not recommended without:
- Comfort with the command line
- Having built and run a small app
- Basic Linux (files, permissions, processes)

Data / BI path (recommended order):
1. Excel                    (2-3 weeks)
2. SQL                      (4-6 weeks)
3. Tableau or Power BI      (4 weeks)
4. Python + Pandas          (4-8 weeks)
5. Statistics basics        (4 weeks)

Roles: Data Analyst, BI Developer, MIS Analyst,
       Reporting Analyst, Product Analyst

Docker is not required on this path — though it
helps if you later work on data platforms.

DevOps / engineering path (recommended order):
1. Linux + bash             (3-4 weeks)
2. One language             (4-8 weeks)
3. Git                      (1 week)
4. Docker                   (2-4 weeks)
5. Kubernetes               (4-8 weeks)
6. Cloud (AWS / GCP / Azure)(4-8 weeks)
7. Terraform + CI/CD        (3-4 weeks)

Roles: DevOps Engineer, Backend Developer,
       Platform Engineer, SRE, Cloud Engineer

Tableau is rarely part of this path — but if you
work on observability dashboards, some teams do
use it alongside Grafana.