Test a course syllabus against ten real job descriptions

Pick ten descriptions that match your real application

The method only works if the ten descriptions are ones you would actually apply to. Filter them before counting:

• Same role family. "Data analyst", "MIS analyst", "reporting analyst", "BI analyst" are close enough to mix. "Data engineer" is not.
• Your experience band. A fresher comparing against 5-year roles will see skills they will not be asked for yet.
• Your city or remote. Tool preferences vary by employer type; stay close to where you will apply.
• Posted in the last month. Older listings may be filled or copied forward without edits.

Save each description as text, not a screenshot. You will search inside it.

Extract the course side honestly

From the syllabus, write each skill with its live teaching hours, not the number of times it is mentioned. If the syllabus lists "Power BI" in one line of a 12-hour module on visualisation, estimate the Power BI share or ask the counsellor. A skill named without hours is a skill you cannot evaluate.

Keep a zero for tools the course does not teach. The zeros are part of the answer.

Tally in a spreadsheet or in a few lines of Python

A spreadsheet with one row per skill and one column per description works fine. If you prefer code, this script does the same tally on synthetic excerpts; replace them with your ten descriptions and the course's hours.

# Synthetic excerpts standing in for ten job descriptions you collected.
jds = [
    "SQL, Excel, Power BI dashboards, stakeholder reporting",
    "Advanced Excel, SQL joins, DAX, weekly MIS",
    "Python pandas, SQL, A/B testing, product metrics",
    "Power BI, Power Query, SQL, data modelling",
    "SQL, Tableau, Excel, KPI definitions",
    "Excel, VBA, SQL, reconciliation reports",
    "SQL, Python, statistics, experiment analysis",
    "Power BI, DAX, SQL, row-level security",
    "Excel, SQL, Google Sheets, dashboarding",
    "SQL, Python, Airflow, data quality checks",
]
# Skills you extracted from the course syllabus, with live hours per skill.
syllabus_hours = {"excel": 6, "sql": 30, "power bi": 18, "dax": 6,
                  "python": 24, "statistics": 12, "tableau": 0}

skills = ["sql", "excel", "power bi", "dax", "python", "statistics",
          "tableau", "power query", "a/b testing", "vba", "airflow"]

print(f"{'skill':<12}{'JDs':>4}  {'course hrs':>10}  verdict")
for s in skills:
    n = sum(s in jd.lower() for jd in jds)
    hrs = syllabus_hours.get(s, 0)
    if n >= 5 and hrs < 10:
        verdict = "GAP: common in JDs, thin in course"
    elif n >= 3 and hrs == 0:
        verdict = "missing: ask about it"
    elif n <= 1 and hrs >= 10:
        verdict = "heavy for a rare ask"
    else:
        verdict = "ok"
    print(f"{s:<12}{n:>4}  {hrs:>10}  {verdict}")

On this synthetic data the output flags Excel: it appears in five of ten descriptions, but the course gives it only 6 live hours. SQL appears in all ten and gets 30 hours, which is proportionate.

Where keyword counting misleads you

The tally is a starting point, and it has three blind spots you should correct by reading.

1. 1Implied skills. In the example, "statistics" appears once literally, but "A/B testing" and "experiment analysis" both require it. The script calls the course heavy on statistics for a rare ask; a human reading the descriptions would say statistics is needed in about three of ten. Group related terms before judging.
2. 2Wish lists. Some descriptions list every tool the team has ever touched. If one description names eleven tools, give it less weight than a description that names four and describes the work.
3. 3Level of depth. "SQL" in a fresher description usually means joins, grouping and filtering. In another it means window functions and query tuning. Read the responsibilities, not only the skills line.

Turn the tally into questions

Once you have the table, sort gaps into three kinds and take each kind to the counselling call.

The third question is not an accusation. A course may spend time on statistics because it underpins later work, which is a defensible answer. An institute that cannot explain its own hour allocation has a weaker syllabus than one that can.

A worked reading of the result

Suppose your ten descriptions show SQL in ten, Excel in eight, Power BI in six, Python in three, and Tableau in two. A course that teaches Python for 40 hours and Power BI for 6 is built for a different job than the one you described. That does not make it a bad course; it may suit a data science path. It makes it the wrong fit for this search.

Repeat the tally for the second course you are considering, using the same ten descriptions. Comparing two courses against one fixed set of jobs is far more informative than comparing their brochures against each other.

For the logistics side of the same decision, see questions to ask before paying for a data course. If you want to see how your own current resume compares against a target role before choosing a course, the free skill check gives a quick first reading. You can run this tally on our Data Analyst programme too; ask a counsellor for the live hours per module if the page does not show them.