What modules should a data analyst syllabus include?
Analyst work follows a simple chain: understand the business question, get the data, clean it, analyse it, show it, and recommend an action. A good syllabus teaches the modules in that order.
- Business metrics: revenue, conversion, retention, churn, unit economics, and how teams use them.
- Spreadsheets: lookups, PivotTables, conditional functions, cleaning, charts.
- SQL: SELECT, filtering, aggregation, joins, subqueries, CTEs, window functions, cohort queries.
- Python: pandas and NumPy for cleaning and exploratory analysis in notebooks.
- Statistics: distributions, sampling, confidence intervals, hypothesis tests, A/B test design.
- BI and dashboards: data modelling, measures, dashboard design and storytelling.
- AI-assisted analysis: using AI tools to draft and debug SQL or Python, and checking their output.
- Capstone and career prep: an end-to-end project, a portfolio, and interview practice.
Official credentials line up with this list. Microsoft's PL-300 Power BI exam, for example, weights preparing, modelling and visualising data at 25–30% each. Google's Data Analytics Certificate on Coursera covers spreadsheets, SQL, Tableau, R and Python across nine courses. Neither replaces a full syllabus, but both confirm what the basics are.
What does a 16-week data analyst syllabus look like?
Here is a model syllabus we use as a benchmark. It assumes a beginner with no coding background and about 12–14 hours a week. This is an ISS editorial template, not the syllabus of any specific course.
| Week | Topic | What you should be able to do | Output |
|---|---|---|---|
| 1 | Business metrics | Map a company's key metrics and how they connect | Metric map |
| 2 | Excel and Sheets | Clean data, use lookups and PivotTables, build a report | Excel analysis |
| 3 | SQL basics | Filter, sort, aggregate, group | Query set |
| 4 | Advanced SQL | Joins, CTEs, window functions, cohort queries | Cohort analysis in SQL |
| 5 | Python basics and pandas | Load, reshape and summarise data in a notebook | Python notebook |
| 6 | Data cleaning and EDA | Handle missing values, outliers and messy text; find patterns | EDA notebook with insights |
| 7 | Statistics for analysts | Describe distributions, compare groups, read confidence intervals | Short statistical memo |
| 8 | Experiments and A/B tests | Design a test, pick a metric, interpret the result | Experiment plan and readout |
| 9 | Data modelling for BI | Build fact and dimension tables, relationships, a date table | Data model |
| 10 | Dashboards | Measures, filters, drill-downs, layout | First dashboard |
| 11 | Data storytelling | Turn a dashboard into a recommendation for a manager | Five-slide insight deck |
| 12 | Product and marketing analytics | Funnels, retention curves, attribution basics | Funnel analysis |
| 13 | AI-assisted analysis | Use AI tools to write and debug queries, then verify them | Prompt log with checked outputs |
| 14 | Capstone: question and data | Frame a business problem and prepare the dataset | Project brief and clean data |
| 15 | Capstone: analysis and dashboard | Combine SQL, Python and BI into one analysis | Draft capstone |
| 16 | Capstone presentation and career prep | Present findings; prepare resume and interview stories | Final case study and portfolio |
Notice that SQL, Python and BI each get more than one week, and that every week ends with something you can show. Shorter courses compress this, which is fine if you already know some of it. If you are starting from zero, be wary of a course that covers SQL in a single weekend.
See how a real 16-week syllabus is laid out
The ISS Data & Business Intelligence program page shows Weeks 1–6 in detail, with case studies and outputs for each week. Check it against the benchmark above and the checklist below.
View Data & Business Intelligence curriculum →Example: how the ISS syllabus maps to this benchmark
We use our own program as one worked example because we can describe it accurately. The ISS program page publishes Weeks 1–6 in detail; Weeks 7–16 are in the downloadable brochure.
| ISS week | Topic on the program page | Case study | Output |
|---|---|---|---|
| 1 | Data Science Foundations & Business Metrics | Flipkart metrics | Metric map and business teardown |
| 2 | Excel, Sheets & Business Reports | SaaS revenue cohort | Excel business analysis |
| 3 | SQL Foundations (PostgreSQL, Cloudflare D1) | Orders database query | SQL query set |
| 4 | Advanced SQL & Data Modelling (joins, window functions, CTEs) | Seller activation | SQL cohort analysis |
| 5 | Python for Data Analysis (Colab, pandas, NumPy) | E-commerce orders dataset | Python notebook |
| 6 | Data Cleaning & EDA | Sales leakage audit | EDA notebook and insights |
The page describes the full 16 weeks as covering SQL, Python, BI dashboards, ML models and business intelligence. The capstone combines SQL, a Python notebook, a Looker dashboard and a business insight. The format is live online, with weekend sessions and a mid-week doubt-clearing session, about 12–14 hours a week. The fee is ₹69,999, or ₹66,499 if paid upfront.
Where it differs from the benchmark: the public page does not detail Weeks 7–16, so check the brochure for statistics and A/B testing depth. The page names Looker for the capstone dashboard and does not list Power BI; if you need Power BI specifically, ask admissions.
How do you check any course against this syllabus?
Ask for the full week-by-week syllabus before you pay. Then score it with these questions:
- Is SQL given at least two weeks, with joins, CTEs and window functions named? These come up often in analyst interviews. See our guide to choosing a SQL course.
- Does every module end with an output? A notebook, query set or dashboard, not just a quiz.
- Are the datasets business-like? Orders, subscriptions, marketing funnels and support tickets are better than toy examples.
- Is statistics taught for decisions? A/B tests and confidence intervals, not just formulas.
- Is there one BI tool taught properly? Power BI, Tableau or Looker. Our Tableau course guide and Power BI course guide explain how to choose.
- Who reviews the capstone? A reviewed end-to-end project is the most useful thing a course can give you.
- How are AI tools used? Good courses teach you to verify AI-written queries, not to copy them.
- Is the weekly time realistic for you? Compare stated hours with your job or college schedule.
If a course scores well on six or more, it is worth a closer look. Then compare fees, format and refund terms; our guide to choosing a data analyst course covers those.
What are the red flags in a data analyst syllabus?
- A long list of tools with no outputs. Fifteen logos on a brochure say little about what you will be able to do.
- No business context. If no week mentions metrics, stakeholders or recommendations, you will learn syntax without judgement.
- Only the first few weeks are visible, and the rest is vague. Ask for the full plan in writing before paying.
- Job or placement promises tied to the syllabus. A syllabus can prepare you; it cannot guarantee an offer.
- No time for revision or projects. A syllabus packed with a new tool every week leaves no room to practise.
One more practical check: ask to see a past learner's capstone, with personal details removed. It shows the real standard of the course better than any brochure.
What can a beginner syllabus safely leave out?
Deep learning, big-data engineering tools such as Spark, and advanced machine learning are useful later, but they are not needed for most entry-level analyst roles. A course that spends weeks on neural networks before you can write a join has its priorities backwards. Light exposure to regression or simple forecasting is enough at the start.
Frequently Asked Questions
What is the syllabus of a data analyst course?
A complete syllabus covers business metrics, Excel, SQL, Python with pandas, statistics and A/B testing, a BI tool such as Power BI, Tableau or Looker, AI-assisted analysis, and a capstone project with career preparation.
How long should a data analyst course be?
For a beginner, around 16 weeks at 12 to 14 hours a week is enough to cover the core modules with projects. Shorter courses can work if you already know Excel or SQL.
Which topic in the data analyst syllabus is most important?
SQL. Most analyst roles need you to pull and join data yourself, and SQL questions are a common part of analyst interviews. A syllabus should give it at least two weeks, including joins, CTEs and window functions.
Is machine learning part of a data analyst syllabus?
Only lightly. Basic regression or forecasting is useful, but deep learning and advanced machine learning are not needed for most entry-level analyst roles.
Do I need coding for a data analyst course?
Most complete syllabi include SQL and Python, but good beginner courses start from zero. You do not need a coding background to join one, only regular practice time each week.
Sources and methodology
External facts below were checked in September 2026.
- Microsoft Learn: PL-300 study guide: skills measured and weights as of 20 April 2026. Checked September 2026.
- Google Data Analytics Professional Certificate (Coursera): 9-course structure and tools covered. Checked September 2026.
- ISS facts come from the Data & Business Intelligence program page (Weeks 1–6, capstone description, schedule) and the fees page.
The 16-week benchmark syllabus, the checklist and the “what to skip” advice are ISS editorial guidance, not a survey of employers.
Next steps
Download the full syllabus of any course you are considering and score it with the checklist above. To see the ISS syllabus in detail, visit the Data & Business Intelligence program page and request the brochure.
Applying is free, and you pay only after you accept an offer. Start your application when you are ready.