Media analytics near Noida Film City: skills newsrooms use

A media cluster in old Noida

Noida Film City is in Sector 16-A, in the older western part of the city near the Delhi border. It began as a film and studio district, and over time many news channels and media businesses set up offices there. This article describes the kind of data work such organisations generally do. It does not claim that any particular company there is hiring. Media hiring comes and goes with news cycles and budgets, so check live postings yourself.

For a learner, the value of the cluster is that it gives you a clear, specific domain to build a portfolio around. Newsroom data is easy to simulate and easy to explain, and it raises real analytical problems.

The metrics a newsroom argues about

In interviews, the most common question in this area is some version of: "Story A had twice the page views of story B, so was A more successful?" A good answer asks about engaged time, referrer mix and the length of each story before reaching a conclusion.

Rank stories by what readers actually did

Here is a small, synthetic event log. The query ranks stories by average engaged seconds per view. It then filters out stories with too few views to rank, which is the step most beginners forget.

import sqlite3

con = sqlite3.connect(":memory:")
con.executescript("""
CREATE TABLE views (story_id TEXT, section TEXT, referrer TEXT, engaged_sec INTEGER);
INSERT INTO views VALUES
 ('S1','politics','social',12), ('S1','politics','social',8),  ('S1','politics','search',95),
 ('S1','politics','social',5),  ('S1','politics','social',10), ('S1','politics','direct',140),
 ('S2','city','search',180),    ('S2','city','direct',210),    ('S2','city','newsletter',160),
 ('S2','city','search',150),
 ('S3','sport','social',30),    ('S3','sport','social',25),    ('S3','sport','search',60),
 ('S3','sport','direct',45),    ('S3','sport','social',20),
 ('S4','business','search',400);
""")

query = """
SELECT story_id,
       section,
       COUNT(*)                     AS views,
       ROUND(AVG(engaged_sec), 1)   AS avg_engaged_sec,
       ROUND(100.0 * SUM(referrer = 'social') / COUNT(*), 0) AS pct_social
FROM views
GROUP BY story_id, section
HAVING COUNT(*) >= 4
ORDER BY avg_engaged_sec DESC;
"""
for row in con.execute(query):
    print(row)

The output ranks S2, the city story, first with about 175 engaged seconds from only four views. The politics story, S1, has the most views but is dragged down by social visits that left within seconds. S4 has the highest single figure, 400 seconds, but it drops out entirely: one view is an anecdote, not a result. The HAVING COUNT(*) >= 4 threshold is arbitrary. In a real newsroom you would agree a minimum with the editors and state it next to the chart.

The pct_social column shows why S1 performs badly. Two-thirds of its views came from social links, and those readers barely stayed. An editor can act on that: the headline may be drawing clicks that the story does not satisfy.

Turning the query into a portfolio project

Take the example above and extend it one step at a time:

1. 1Generate a larger synthetic log. Use a few thousand rows across sections and hours, with a realistic skew so that some stories take off on social and most do not.
2. 2Add time. Look at engaged time by hour of publication, and ask whether morning stories behave differently.
3. 3Normalise for length. Add a word count per story and compare engaged time with expected reading time.
4. 4Write the editor's note. Three sentences on what the desk should do differently tomorrow. This is the part most portfolios leave out, and the part a newsroom reads first.

Present it as a single-page dashboard plus the note. Label the data clearly as synthetic and describe your assumptions, such as how you defined "engaged".

Skills beyond the dashboard

Media analysis also rewards two skills that do not show up in SQL. The first is speed with accuracy under deadline. Numbers for an election night or a budget story are needed within minutes, and they must be right. The second is careful handling of reader data. Returning-reader figures depend on identifiers and consent. Know that India's Digital Personal Data Protection Act, 2023 exists, and treat personal data as something to minimise, not collect.

Many newsrooms are also experimenting with generative AI for tasks such as tagging, summarising and headline testing. Knowing how to check such output is a strong companion skill. The Generative & Agentic AI add-on covers that side in three months. The core querying and reporting work is in the Data Analyst programme, which runs for 3–4 months, live online with optional labs at our Noida centre in Sector 4, not far from Film City. For other sectors with local clusters, see which NCR industries use analysts.