Why Delhi Is Becoming a Hub for Digital Marketing Training

# Track small practice projects you offer to local businesses.
projects = [
    {"business": "Local bakery (example)", "task": "Google Business Profile update", "status": "done"},
    {"business": "Tuition centre (example)", "task": "Instagram content calendar", "status": "in progress"},
    {"business": "Friend's shop (example)", "task": "Basic SEO audit", "status": "proposed"},
]

for p in projects:
    print(f"{p['status']:12} {p['business']:28} {p['task']}")
print(sum(p["status"] == "done" for p in projects), "completed for portfolio")

Hello,

I am learning digital marketing and building a portfolio.
I would like to help your business with one small task free of charge:
- updating your Google Business Profile, or
- planning two weeks of social media posts, or
- a short SEO review of your website.

I will share what I did and what changed. Would that be useful?

Thank you,
[Your name]

pages = {
    "/": "Fresh Cakes and Pastries in Delhi | Example Bakery",
    "/menu": "Menu",
    "/order": "Order Birthday Cakes Online for Same-Day Delivery Across Delhi NCR Today",
}

for url, title in pages.items():
    n = len(title)
    if n < 30:
        note = "too short: add a keyword"
    elif n > 60:
        note = "may be cut off in results"
    else:
        note = "ok"
    print(f"{url:8} {n:3} chars  {note}")

from urllib.parse import urlencode

def utm_link(base, source, medium, campaign, content=None):
    params = {"utm_source": source, "utm_medium": medium, "utm_campaign": campaign}
    if content:
        params["utm_content"] = content
    sep = "&" if "?" in base else "?"
    return base + sep + urlencode(params)

print(utm_link("https://example.com/offer", "instagram", "social", "diwali_sale", "story_1"))
print(utm_link("https://example.com/offer", "newsletter", "email", "diwali_sale"))

import pandas as pd

# Sample campaign export (made-up practice data)
df = pd.DataFrame({
    "campaign": ["search_cakes", "insta_story", "fb_lookalike"],
    "impressions": [12000, 30000, 25000],
    "clicks": [600, 450, 300],
    "leads": [48, 18, 9],
    "spend": [6000, 4500, 4000],
})
df["ctr_%"] = (df["clicks"] / df["impressions"] * 100).round(2)
df["conv_%"] = (df["leads"] / df["clicks"] * 100).round(2)
df["cpl"] = (df["spend"] / df["leads"]).round(0)
print(df.sort_values("cpl"))

Columns: A Campaign | B Impressions | C Clicks | D Leads | E Spend

F2  CTR %          =IFERROR(C2/B2*100, 0)
G2  Conversion %   =IFERROR(D2/C2*100, 0)
H2  Cost per lead  =IFERROR(E2/D2, "no leads")

Then sort by H (lowest cost per lead first) and write
one line per campaign: keep, improve, or pause, and why.

1. Which modules are covered (SEO, ads, social, content, email, analytics)?
2. Will I run a real or live practice campaign?
3. Which tools will I use hands-on?
4. How many projects go into my portfolio?
5. Who are the trainers and what have they worked on?
6. What are the batch timings and mode (classroom, online, hybrid)?
7. What are the current fees and refund terms in writing?

Criteria               Course 1   Course 2   Course 3
Modules complete       ____       ____       ____
Live campaign practice ____       ____       ____
Tools used hands-on    ____       ____       ____
Portfolio projects     ____       ____       ____
Trainer demo score     ____       ____       ____
Batch timing fit       ____       ____       ____
Fees in writing        ____       ____       ____

Weeks 1-2  Marketing basics, audience and funnels
Weeks 3-4  SEO: keywords, on-page, Search Console
Week 5     Social media and content calendar
Week 6     Paid search and social ads basics
Week 7     Email marketing and landing pages
Week 8     Analytics, reporting and portfolio write-ups