Free roadmap · 3 months · 2 phases · 6 stages
Data Analyst roadmap
Who it's for
- • Students and fresh graduates
- • Professionals switching from finance, sales, HR or operations
- • Anyone comfortable with basic computers and curious about numbers
Prerequisites
- • Basic computer skills
- • School-level maths (percentages, averages)
- • Around 10–15 hours of study per week
Tools you'll use
Phase 1 — Foundations
Build the base: spreadsheets, SQL and the statistics every analyst relies on. By the end of this phase you can clean data, query databases and explain numbers with confidence.
Phase 2 — Intermediate & Advanced
Level up: professional dashboards in Power BI, analysis in Python, and a portfolio that proves your skills to employers.
A suggested timeline for beginners studying consistently. Your pace depends on prior knowledge, study hours and practice.
Where this career takes you
The designations employers hire for, the cities with the strongest demand, and the duties you'll own day to day.
Job roles in the market
- • Data Analyst
- • Business Intelligence Analyst
- • Reporting Analyst
- • MIS Executive
- • Marketing Analyst
- • Junior Business Analyst
High-demand cities
- • Karachi
- • Lahore
- • Islamabad
- • Rawalpindi
- • Faisalabad
- • Gujranwala
- • Remote (worldwide)
Core duties
- • Collect and clean business data
- • Build dashboards and management reports
- • Write SQL queries for decision-makers
- • Track KPIs and explain changes
- • Present insights to non-technical teams
Learning plan
PHASE 1
Foundations
Build the base: spreadsheets, SQL and the statistics every analyst relies on. By the end of this phase you can clean data, query databases and explain numbers with confidence.
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What you'll learn in each tool
Excel
- Interface, worksheets and cell references
- Core formulas: SUM, AVERAGE, IF, SUMIF, COUNTIF
- VLOOKUP / XLOOKUP for combining tables
- Pivot tables and pivot charts
- Data cleaning: trim, dedupe, text-to-columns
- Building a simple one-page dashboard
Analytical thinking
- Turning a business question into a data question
- Reading a table before touching a formula
- Spotting errors, duplicates and outliers
Topics — tick as you learn
Practice exercises
- → Clean a messy sales spreadsheet
- → Analyse monthly revenue by product
- → Calculate product-level performance
- → Build a basic Excel dashboard
Recommended free learning resources
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PHASE 2
Intermediate & Advanced
Level up: professional dashboards in Power BI, analysis in Python, and a portfolio that proves your skills to employers.
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External resources are recommended for your learning journey and are not produced or endorsed by DataUpto.
Portfolio projects
Retail sales analysis
Clean a year of transactions in Excel and present monthly trends and top products.
Customer purchasing behaviour
Use SQL to find repeat buyers, basket size and churn signals.
Executive sales dashboard
A Power BI report with KPIs, drill-through and regional filters.
Marketing campaign EDA
Use pandas to compare campaign channels and recommend budget shifts.
Common mistakes to avoid
- Watching tutorials without practising on real data
- Learning five BI tools at once instead of mastering one
- Building dashboards without a business question
- Skipping SQL because Excel 'feels enough'
- Portfolios with no written explanation of decisions
Final self-assessment
Before applying for entry-level data analyst roles, you should be able to:
Interview preparation
Suggested next steps
- 01
Practise interview questions
- 02
Build your resume
- 03
Explore the Data Analyst Program for mentor support