Data Analytics Course | Vybhava Technologies

ONLINE DATA ANALYTICS COURSE | SQL, TABLEAU, PYTHON, POWER BI

New graduate or switching careers? Become a job-ready Data Analyst.

Employers in the US, UK, Canada, Germany, the EU and Australia hire people who can turn data into decisions. Learn advanced SQL, Tableau, Python, Power BI and Looker from zero, with a hands-on lab in every module. No coding background needed. Open to students worldwide.

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10Modules

10 weeksDuration

70%SQL focus

30%Tableau focus

Your degree is the start. Skills get you hired.

Many graduates and career changers have never written a real business query or built a dashboard. This course gives you exactly that: portfolio-ready work on a real e-commerce case study.

Why Data Analytics

• Every industry uses data: retail, banking, health, IT
• Works with any degree or background
• Start from zero: we teach from the basics
• Finish with a capstone project to show recruiters

Data Detective Challenge

5 quick questions. No signup. See your score, then share it with our counsellor.

Learn data analytics from the US, UK, Canada, Germany, EU or Australia

The same skills show up in analyst job adverts worldwide: SQL, Tableau, Power BI, Excel and Python. Ask us about batch timings for your time zone.

United States

Entry-level roles: Data Analyst, Business Intelligence (BI) Analyst.

United Kingdom

Entry-level roles: Data Analyst, Insight Analyst, Reporting Analyst.

Canada

Entry-level roles: Data Analyst, BI Analyst, Business Analyst.

Germany

Entry-level roles: Data Analyst (Datenanalyst), BI-Analyst.

European Union

Entry-level roles: Data Analyst, Business Analyst, Analytics Consultant.

Australia

Entry-level roles: Data Analyst, Reporting Analyst, BI Analyst.

Datenanalyse lernen: SQL, Tableau und Python für Einsteiger. Online-Kurs in englischer Sprache.

Who is this for

Audience

• Complete beginners, no coding needed
• Graduates and career switchers from any degree
• Aspiring Data Analysts in e-commerce, sales and marketing

Job skills you will gain

• Extract, clean and validate data with SQL
• Advanced SQL: CTEs, window functions, tuning
• Interactive Tableau dashboards
• KPIs: conversion rate, AOV, customer lifetime value
• Data-driven business recommendations

How we teach

• Concept, guided lab, then exercises
• One real e-commerce case study throughout
• Capstone project and interview preparation

Data analytics course roadmap

M0 Prerequisites setupM1 Analytics 101 and business KPIsM2 SQL fundamentalsM3 JoinsM4 Advanced SQLM5 PerformanceM6 Tableau dashboardsM7 Python for prep + Power BI and Looker introM8 Business analysis and recommendationsM9 Data qualityM10 Capstone

Data analytics course syllabus: module by module

M0Prerequisites setupDay 0

What you will learn

• Laptop check: 8 GB RAM, 10 GB free disk
• Install PostgreSQL 16 and DBeaver
• Install Tableau Public and Anaconda (Jupyter)
• Power BI Desktop and Looker Studio access

Hands-on lab

1. Install PostgreSQL and keep port 5432
2. Connect DBeaver to PostgreSQL and test the connection
3. Create the database: CREATE DATABASE shopdb;
4. Run schema.sql and import the CSV files
5. Verify: SELECT COUNT(*) FROM orders;

Exercises

• Screenshot the row counts of all five tables
• Run SELECT * FROM customers LIMIT 10;
• Open orders.csv in Tableau Public

M1Analytics 101 and business KPIsWeek 1 | 3 sessions

What you will learn

• What analysts do; the analytics lifecycle
• E-commerce data sources: orders, loyalty, marketing, inventory
• KPIs: conversion rate, AOV, CLV, repeat rate
• Excel basics: pivots for quick checks

Hands-on lab

1. Open orders.csv in Excel/Sheets, note columns and types
2. Build a pivot of sales by month
3. Compute AOV = revenue / orders by hand
4. Write each KPI definition in a one-line glossary

Exercises

• E1: Compute conversion rate from sessions and orders
• E2: Which month has the highest AOV?
• E3: List 5 questions a marketing manager would ask

M2SQL fundamentals (beginner)Weeks 2-3

What you will learn

• SELECT, WHERE, ORDER BY, LIMIT
• Aggregates: COUNT, SUM, AVG; GROUP BY, HAVING
• Dates and CASE WHEN

Hands-on lab

1. SELECT order_id, total FROM orders LIMIT 20;
2. Filter: WHERE order_date >= '2025-01-01'
3. Monthly sales: SELECT date_trunc('month',order_date), SUM(total) ... GROUP BY 1
4. Add HAVING to keep months above a threshold
5. Label customers: CASE WHEN total>5000 THEN 'High' ...

Exercises

• E1: Top 10 products by revenue
• E2: Orders per city, sorted high to low
• E3: Share of orders using a coupon

M3Joins, data cleaning and validationWeek 4

What you will learn

• INNER/LEFT joins across 5 tables
• NULLs, duplicates, wrong types
• Validation rules and reconciliation

Hands-on lab

1. Join orders to customers and products
2. Find duplicates: GROUP BY order_id HAVING COUNT(*)>1
3. Handle NULLs with COALESCE and trim text
4. Reconcile: SUM(order_items) vs orders.total
5. Save a clean view: CREATE VIEW clean_orders AS ...

Exercises

• E1: Customers who never ordered (LEFT JOIN)
• E2: Find 3 injected data errors in the dataset
• E3: Write a 5-check validation script

M4Advanced SQL (core of the role)Weeks 5-6

What you will learn

• Subqueries and CTEs
• Window functions: ROW_NUMBER, RANK, LAG, running totals
• Cohorts and retention

Hands-on lab

1. Write a CTE for monthly revenue
2. Add LAG() to compute month-over-month growth
3. RANK() products within each category
4. Running total: SUM(x) OVER (ORDER BY month)
5. Build a first-order-month cohort table

Exercises

• E1: Top 3 products per category
• E2: Customer lifetime value with CTEs
• E3: Month-1 and month-3 retention by cohort

M5Performance, stored procedures, large dataWeek 7

What you will learn

• Indexes and EXPLAIN ANALYZE
• Stored procedures/functions, parameters
• Partitioning, incremental loads

Hands-on lab

1. Run EXPLAIN ANALYZE on a slow join query
2. CREATE INDEX on orders(customer_id, order_date)
3. Re-run and compare timings
4. Write a function sales_by_range(start, end)
5. Rewrite a subquery as a CTE/join and compare

Exercises

• E1: Tune a provided 40-second query below 2 seconds
• E2: Procedure to refresh a daily sales summary table
• E3: Document what you changed and why

M6Tableau dashboards (30%)Weeks 8-9

What you will learn

• Connections, dimensions vs measures
• Charts, calculated fields, LOD, parameters
• Filters, actions, dashboard design

Hands-on lab

1. Connect Tableau to the Postgres views (or CSV on Public)
2. Build: sales trend line, top products bar, city map
3. Calculated field: AOV = SUM(Sales)/COUNTD(Order ID)
4. Add filters and a parameter for date range
5. Combine into one dashboard; publish to Tableau Public

Exercises

• E1: KPI tiles: sales, orders, AOV, CLV
• E2: Campaign effectiveness view
• E3: Add a drill-down from region to city

M7Python for prep + Power BI and Looker introWeek 9

What you will learn

• pandas: read, clean, merge, group
• Read SQL into pandas
• Same dashboard in Power BI and Looker Studio

Hands-on lab

1. Jupyter: pd.read_sql(query, engine)
2. Clean with dropna, fillna, drop_duplicates
3. groupby to compute monthly KPIs
4. Load the clean file into Power BI; add 3 visuals
5. Rebuild one chart in Looker Studio

Exercises

• E1: Python version of the monthly sales query
• E2: Compare 3 tools on one KPI page
• E3: Write a table of pros and cons of each tool

M8Business analysis and recommendationsWeek 10

What you will learn

• Sales trends, seasonality, inventory
• RFM segmentation, campaign ROI
• Turning insight into a promotion, pricing or merchandising action

Hands-on lab

1. Compute RFM scores with NTILE(5) in SQL
2. Segment customers: Champions, At Risk, New
3. Compare campaign revenue vs spend
4. Find slow-moving stock vs fast sellers
5. Write 3 recommendations: finding, action, expected impact

Exercises

• E1: Pick a segment and propose a loyalty offer
• E2: Which campaign to stop or scale?
• E3: 5-slide story for a manager

M9Data quality, governance, D&A product supportWeek 10

What you will learn

• Data availability and mapping assessment
• Writing a business case and impact estimate
• Validating a data mart or feed against expectations

Hands-on lab

1. Fill a source-to-target mapping sheet for 10 fields
2. Check completeness, uniqueness, freshness with SQL
3. Write a one-page business case for a new data mart
4. Create UAT test cases and a sign-off checklist
5. Log and triage 3 data issues

Exercises

• E1: Data quality scorecard for orders
• E2: Mapping document for loyalty data
• E3: Review a peer's deliverable

M10 Capstone project

Brief

Reduce churn and raise average order value for an online retailer using the practice dataset.

• Extract and clean data with advanced SQL
• Build a Tableau dashboard with 5+ KPIs
• Write 3 data-backed recommendations
• Present in a 10-minute stakeholder talk

Grading

• SQL correctness and performance: 40%
• Tableau design and interactivity: 25%
• Insights and recommendations: 20%
• Data quality and documentation: 10%
• Communication: 5%

Frequently asked questions

Is this data analytics course suitable for complete beginners?

Yes. No coding background is needed. You start with the basics and build up to advanced SQL and Tableau dashboards.

Which tools will I learn?

SQL (PostgreSQL), Tableau, Python with pandas, Power BI and Looker Studio, plus Excel basics.

How long is the course?

The plan is about 10 weeks, with hands-on labs in every module and a final capstone project.

Can students from the US, UK, Canada, Germany, EU and Australia join?

Yes. Contact us to confirm batch timings for your time zone.

Which jobs can this course prepare me for?

Entry-level roles such as Data Analyst, Business Intelligence Analyst and Reporting Analyst.

Start your Data Analytics career

Talk to Vybhava Technologies about batches and fees. International callers: +91 96187 15457. Your quiz answers are not stored.

Call +91 96187 15457