Data Analyst Career Roadmap for Beginners
A stage-by-stage data analyst roadmap for beginners and freshers in India: spreadsheets, SQL, one BI tool, a project and interview practice.
Read the guide →Build your understanding of Data Analytics through a roadmap, worked examples, project ideas and interview practice.
5 guides to explore
A stage-by-stage data analyst roadmap for beginners and freshers in India: spreadsheets, SQL, one BI tool, a project and interview practice.
Read the guide →Practise filters, aggregation, joins and window calculations without losing data meaning.
Read the guide →Design measures, views and checks around one audience and decision.
Read the guide →Three reporting briefs that reward careful definitions and reproducible checks.
Read the guide →Fourteen data analyst interview questions for freshers, grouped by theme, with what strong and weak answers sound like.
Read the guide →Learn to clean a table, investigate a question and build reports that make business activity understandable. Good analytics connects a number to a definition, a source and a useful decision.
See the full Data Analytics track: modules, projects and interview questions →
Read in this order: the roadmap and concepts first, then skills, then projects and interview questions.
A stage-by-stage data analyst roadmap for beginners and freshers in India: spreadsheets, SQL, one BI tool, a project and interview practice.
Practise filters, aggregation, joins and window calculations without losing data meaning.
Design measures, views and checks around one audience and decision.
Decide whether to start with analytics or data science using your questions, coding comfort, statistics and the realistic first roles in India.
Compare hands-on analysis and reporting with KPI-led business recommendations, with tools, maths and the realistic first roles in India.
Three reporting briefs that reward careful definitions and reproducible checks.
Yes, spreadsheet practice and numerical reasoning are useful starting points. Check the programme's preparation requirements with the team.
SQL is widely useful for retrieving and summarising structured data. It complements spreadsheets and dashboards.
Analytics commonly centres on questions and reporting. Data Science often adds statistical investigation and predictive modelling.
No. Define measures, reconcile results and make the intended decision clear.