Artificial Intelligence, Machine Learning & Data
Government or privateData Analyst
Entry track: Skills + portfolio + hiring assessments · Entrance/qualification path + employer/institute recruitment
Where are you right now?
Typical time from here: 8–20 months
How you get in
JEE/engineering entrances, CUET, GATE CS/DA/EC/EE, JAM/statistics routes, postgraduate admissions, internships, coding/data assessments and research recruitment
Exam pages on StudyBddy
What you must have
B.Tech/B.E., B.Sc./M.Sc. in CS, mathematics, statistics or related fields; MCA/M.Tech/PhD for specialised research roles
The gate that decides it
Portfolio
First evidence to build
Sales/operations dashboard
Your first 90 days
Days 1–30: eligibility and diagnostic in Statistics; spreadsheets. Days 31–60: core practice in Data cleaning; exploratory analysis. Days 61–90: complete Sales/operations dashboard and obtain one external review.
Realistic first roles
- Data analyst
- BI analyst
- reporting analyst
Core tools and platforms
The 9-phase route for this post
Data Analyst follows the Data Analytics & Business Intelligence route.
Phase 0 · Target, Eligibility & Reality Check
Explore the work through career videos, informational interviews and one mini-task. Select school subjects/degree route that supports Statistics; spreadsheets; SQL; business reasoning; data visualisation; communication. Record age, medical, licence or degree conditions from official notices.
Typical duration: 1–2 weeks
Phase 1 · Foundation & Academic Base
Strengthen school/college fundamentals in Statistics; spreadsheets; SQL; business reasoning; data visualisation; communication. Use textbooks plus one structured course; solve weekly problems and practise communication.
Typical duration: 6–16 weeks
Phase 2 · Core Curriculum / Skill Stack
Complete a structured curriculum covering Data cleaning; exploratory analysis; dashboarding; metrics; experimentation basics; storytelling. Pair each topic with a lab, case, answer-writing exercise or practical task.
Typical duration: 3–9 months
Phase 3 · Applied Practice, Projects & Field Exposure
Build progressively harder evidence: Sales/operations dashboard; cohort analysis; SQL case study; business recommendation deck. Seek internships, labs, clubs, competitions, volunteering or apprenticeship where appropriate.
Typical duration: 2–6 months
Phase 4 · Exam, Credential, Licence or Advanced Qualification
Plan prerequisites early. Use official syllabus/PYQs and preserve academic performance required for Portfolio; vendor BI certification optional; analytics assessments.
Typical duration: 3–18 months, route-dependent
Phase 5 · Experience, Network & Professional Evidence
Use college projects, AICTE internships, NCS, apprenticeships, research labs, volunteering and competitions aligned to Data Analytics & Business Intelligence.
Typical duration: 2–6 months, may overlap
Phase 6 · Portfolio, CV, Applications & Selection Preparation
Create a one-page student CV, project portfolio and concise introduction. Practise aptitude, subject and behavioural questions relevant to Data Analytics & Business Intelligence.
Typical duration: 4–12 weeks
Phase 7 · Selection, Joining & First 90 Days
During internship/first role, learn SOPs, safety, tools and team expectations. Keep a weekly learning and contribution log.
Typical duration: First 90 days
Phase 8 · Growth, Specialisation & Position-to-Position Progression
Understand the long ladder early: Senior analyst; analytics engineering; product analytics; data science. Choose experiences that compound rather than random certificates.
Typical duration: 1–5 years per progression step
Other posts on the same route
Check it at the source
- https://gate.iitk.ac.in/
- https://pandas.pydata.org/docs/
- https://learn.microsoft.com/power-bi/
- https://www.kaggle.com/learn
These roles normally require separate private, campus, research-lab, or government recruitment after education and skill development. Verify current eligibility, dates and recruitment rules from the official notification.