-- Power BI — core DAX measures

Total Revenue =
SUM(Sales[Revenue])

Orders =
DISTINCTCOUNT(Sales[OrderID])

Avg Order Value =
DIVIDE([Total Revenue], [Orders])

Revenue YoY % =
VAR CurrentRev = [Total Revenue]
VAR PrevRev =
    CALCULATE(
        [Total Revenue],
        SAMEPERIODLASTYEAR('Date'[Date])
    )
RETURN
    DIVIDE(CurrentRev - PrevRev, PrevRev)

Top Customers =
RANKX(ALL(Customers[Name]), [Total Revenue])

// Power Query M — clean sales data

let
    Source = Csv.Document(
        File.Contents("sales.csv"),
        [Delimiter=",", Encoding=65001, QuoteStyle=QuoteStyle.Csv]
    ),
    Promoted = Table.PromoteHeaders(Source),
    ChangedTypes = Table.TransformColumnTypes(
        Promoted,
        {
            {"OrderDate", type date},
            {"Revenue", type number},
            {"Customer", type text}
        }
    ),
    RemovedBlank = Table.SelectRows(
        ChangedTypes,
        each [OrderDate] <> null
    )
in
    RemovedBlank

# Flask — a simple web app
from flask import Flask, render_template

app = Flask(__name__)

@app.route("/")
def home():
    return render_template("index.html", title="Hello, Flask!")

@app.route("/about")
def about():
    return "
About
A tiny Flask app.
"

if __name__ == "__main__":
    app.run(debug=True)

# Flask — a small JSON API for sales data
from flask import Flask, jsonify, request
import sqlite3

app = Flask(__name__)

def query(q, params=()):
    conn = sqlite3.connect("sales.db")
    conn.row_factory = sqlite3.Row
    rows = conn.execute(q, params).fetchall()
    conn.close()
    return [dict(r) for r in rows]

@app.route("/api/orders", methods=["GET"])
def orders():
    city = request.args.get("city")
    if city:
        return jsonify(query(
            "SELECT * FROM orders WHERE city = ?", (city,)
        ))
    return jsonify(query("SELECT * FROM orders LIMIT 100"))

@app.route("/api/revenue", methods=["GET"])
def revenue():
    return jsonify(query(
        "SELECT city, SUM(amount) AS revenue "
        "FROM orders GROUP BY city ORDER BY revenue DESC"
    ))

if __name__ == "__main__":
    app.run(debug=True)

Power BI — 30-day beginner plan:
Week 1: Install, import data, basic charts
Week 2: Slicers, filters, relationships
Week 3: DAX — SUM, COUNT, DIVIDE, CALCULATE
Week 4: Build a full sales dashboard
        + publish to Power BI Service

Result: Interview-ready for analyst roles.

Flask — 30-day beginner plan
(assumes Python basics):
Week 1: Setup, routes, "Hello World"
Week 2: Templates (Jinja2), forms, static files
Week 3: SQLite, JSON APIs, error handling
Week 4: Build a small CRUD app (notes/tasks)
        + deploy to Render or PythonAnywhere

Result: A real web app on your resume,
        ready to grow into full-stack.

Not recommended without Python basics.

Data / BI path (recommended order):
1. Excel              (1-2 months)
2. SQL                (1-2 months)
3. Power BI           (1-2 months)
4. Python + Pandas    (2-3 months)

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

Web / backend path (recommended order):
1. Python basics      (1-2 months)
2. HTML + CSS basics  (2-3 weeks)
3. Flask              (1-2 months)
4. Databases (SQL)    (1 month)
5. Deploy + Git       (2-3 weeks)

Roles: Backend Developer, Python Developer,
       Full-Stack Developer, ML Engineer.