Data Analytics · Syllabus Walkthrough
Data Analytics Course Modules Explained Week by Week
Quick summary — data analytics course modules
Data analytics course modules, explained week by week, usually move from Excel and basic statistics in the first weeks, to SQL and a BI tool such as Power BI in the middle, and Python with a capstone project at the end. Each stage should finish with something you build, so your portfolio grows alongside your skills.
In this guide you will learn:
- Weeks 1-4 — Excel, data cleaning and statistics.
- Weeks 5-8 — SQL and Power BI dashboards.
- Weeks 9-12 — Python, Gen AI help and capstone.
- Weekly trackers — templates to check your progress.
- Tips — getting the most from each module.
SECTION 01Weeks 1-4: Excel, data cleaning and statistics
The first month builds habits you will use in every later module: cleaning messy data, summarising it and understanding what the numbers mean. Rushing through this stage makes SQL and Power BI harder later.
| Week | Module | You learn | You build |
|---|---|---|---|
| 1 | Excel essentials | Formatting, sorting, filtering, basic formulas | Clean a sales sheet |
| 2 | Lookups and logic | XLOOKUP, IF, SUMIFS, COUNTIFS | Merge customer and order data |
| 3 | Pivots and charts | Pivot tables, slicers, chart choice | Monthly sales summary |
| 4 | Statistics basics | Mean, median, spread, correlation | Short insight report |
=XLOOKUP(B2, Customers!A:A, Customers!B:B, "Unknown")
=SUMIFS(Orders!E:E, Orders!C:C, "Noida", Orders!D:D, ">=2026-04-01")
=COUNTIFS(Orders!F:F, "Returned", Orders!C:C, B2)
=IF(E2>=10000, "High value", "Standard")=AVERAGE(E2:E500) mean order value
=MEDIAN(E2:E500) middle order value (less affected by outliers)
=STDEV.S(E2:E500) spread of order values
=CORREL(D2:D500, E2:E500) relationship between discount and order value
Insight check: is the mean much higher than the median? -> outliers presentSECTION 02Weeks 5-8: SQL and Power BI dashboards
The middle weeks move you from spreadsheets to databases and dashboards — the core of most analyst jobs. This is usually where learners feel the biggest jump in confidence, and where practice volume matters most.
| Week | Module | You learn | You build |
|---|---|---|---|
| 5 | SQL basics | SELECT, WHERE, ORDER BY, aggregates | Answer 20 business questions |
| 6 | Joins and subqueries | INNER/LEFT JOIN, subqueries, CASE | Customer order history query |
| 7 | Power BI basics | Import, model relationships, visuals | First interactive report |
| 8 | DAX and design | Measures, time intelligence, layout | Sales dashboard with KPIs |
-- Customers with their order count and total spend
SELECT c.customer_id,
c.city,
COUNT(o.order_id) AS orders,
COALESCE(SUM(o.amount), 0) AS total_spend,
CASE WHEN COALESCE(SUM(o.amount), 0) >= 50000 THEN 'Gold'
ELSE 'Regular' END AS segment
FROM customers c
LEFT JOIN orders o ON o.customer_id = c.customer_id
GROUP BY c.customer_id, c.city
ORDER BY total_spend DESC;Total Sales = SUM ( Orders[Amount] )
Sales LY =
CALCULATE ( [Total Sales], SAMEPERIODLASTYEAR ( 'Date'[Date] ) )
Sales Growth % =
DIVIDE ( [Total Sales] - [Sales LY], [Sales LY] )SECTION 03Weeks 9-12: Python, Gen AI help and capstone
The final stage adds Python for automation and larger data, shows how to use AI assistants responsibly, and ends with a capstone that pulls every module together into one project you can present.
| Week | Module | You learn | You build |
|---|---|---|---|
| 9 | Python and Pandas | Load, clean, group, merge data | Automated cleaning script |
| 10 | Visualisation and automation | Charts, exporting reports | Weekly report script |
| 11 | Gen AI for analysts | Prompting for SQL drafts and summaries, checking outputs | AI-assisted analysis with verification notes |
| 12 | Capstone and interview prep | End-to-end project, presentation | Portfolio project and mock interview |
import pandas as pd
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
customers = pd.read_csv("customers.csv")
df = orders.merge(customers, on="customer_id", how="left")
df["month"] = df["order_date"].dt.to_period("M").astype(str)
report = df.groupby(["month", "city"])["amount"].sum().reset_index()
report.to_excel("monthly_city_sales.xlsx", index=False)Prompt (to an AI assistant):
"I have tables orders(order_id, customer_id, order_date, amount)
and customers(customer_id, city). Write a SQL query for monthly
revenue by city for 2026, highest first."
Verification checklist before using the answer:
[ ] Table and column names match my schema
[ ] Date filter covers the full year correctly
[ ] Totals match a manual check for one month
[ ] No sensitive data was pasted into the promptSECTION 04Weekly progress trackers
Use these templates to check you are keeping pace with the modules, whichever course you join. For a related walkthrough, see How Much Does Data Analytics Training Cost?.
Week ___ check
Module: ______________
Classes attended: __ / __
Practice hours: __
I can now: ______________
Still confusing: ______________
Question for the trainer: ______________Skills tracker (tick when confident)
[ ] Clean data in Excel
[ ] XLOOKUP and SUMIFS
[ ] Pivot tables and charts
[ ] SQL joins and GROUP BY
[ ] Power BI model and DAX measures
[ ] Pandas cleaning and grouping
[ ] Verify AI-generated SQL
[ ] Present a project in 5 minutesCapstone plan
Business question: ______________
Dataset: ______________
Excel: initial cleaning and checks
SQL: core queries
Power BI: dashboard with 3-4 visuals
Python: automation of the data refresh
Presentation: 5 slides or a dashboard walkthroughSECTION 05Tips for getting the most from each module
Course structure helps, but your weekly habits decide the result. Learners who practise a little every day usually finish stronger than those who cram before assessments. You may also find Uncodemy vs upGrad: Data Analytics Course and Power BI Basics Explained for Complete Beginners useful.
- Practise the same day — repeat each class exercise within 24 hours while it is fresh.
- Use one dataset across modules — seeing the same data in Excel, SQL, Power BI and Python connects the tools.
- Ask early — a confusing join in week 6 becomes a blocker in week 8.
- Save everything to GitHub — your weekly work becomes a visible progress trail.
- Explain one insight weekly — write two sentences a manager would understand.
SECTION 06Interview Q&A — data analytics course modules
Q1Walk me through what you learned in your data analytics course.
Summarise the progression: Excel and statistics, SQL, Power BI, Python, then a capstone — with one example project for each stage.
Q2What is the difference between a LEFT JOIN and an INNER JOIN?
INNER JOIN keeps only matching rows. LEFT JOIN keeps all rows from the left table and fills unmatched right-side columns with NULL.
Q3Why use a date table in Power BI?
It enables reliable time intelligence calculations like year-over-year comparisons and consistent date filtering.
Q4When would you use Python instead of Excel?
For repeated tasks, large datasets and processes that combine many files, where automation saves time and reduces errors.
Q5How do you use AI tools responsibly in analysis?
Use them for drafts, verify every output against the data, and never share confidential information with public tools.
SECTION 07Test yourself — data analytics course modules quiz
Five questions. No sign-up.
0 / 5Pick an answer to see why it is right or wrong.
SECTION 08Frequently asked questions
What modules are included in a data analytics course?
Typically Excel, statistics, SQL, a BI tool such as Power BI or Tableau, Python with Pandas and a capstone project, plus interview preparation.
How many weeks does a data analytics course take?
It varies by institute and batch type. Ask for the module-by-week plan and total hours to compare courses.
Which module is the most important in data analytics?
SQL is often the most tested skill in analyst interviews, but Excel and a BI tool are equally used on the job.
Is Python included in data analytics courses?
Most current courses include Python with Pandas, usually after SQL and BI, for automation and larger datasets.
Do data analytics courses teach Gen AI tools?
Many 2026 courses now show how analysts use AI assistants for drafts and summaries, with emphasis on verifying results.
SECTION 09Related reads
Classroom & online · Noida
Follow a clear module-by-module path
Our Data Analytics Training Course covers Excel, statistics, SQL, Power BI, Python and Gen AI tools with a project at every stage and a capstone at the end.
₹15,500 · full programme- Excel to Python progression
- Project every module
- Capstone and portfolio
- Weekday & weekend batches