Data analytics involves collecting, cleaning, analyzing, and interpreting data to inform business decisions or solve problems. Analysts use tools (e.g., SQL, Python, Power BI) to transform raw data into actionable insights on customer behavior, operational efficiency, risk, and more. This role matters because organisations across industries increasingly rely on data to make strategic choices—making analytics integral to digital transformation .
Ideal candidates have:
Strong analytical and statistical mindset
Proficiency (or willingness to learn) in technical tools like SQL, Python, Excel, Power BI, Spark, cloud platforms
Sharp communication and data‑storytelling skills
Curiosity, attention to detail, problem-solving, and ethical awareness
In India: Usually requires 10+2 (science, maths), admission via JEE Main/Advanced or specific state/university entrance tests .
Abroad: For US undergrad—4-year degree in Data Analytics or related (CS, Stats, Maths, Economics). International students need English proficiency (TOEFL/IELTS).
India:
JEE Main/Advanced, plus institute-specific tests (e.g., SNAP/XAT/JET for business/data analytics)
Global equivalent:
For master's programs—GRE/GMAT (GRE ~310+, GMAT 580‑700), English tests (TOEFL 100+, IELTS 7+)
Portfolios: International schools don’t usually require creative portfolios, but emphasize strong transcripts, SOPs, LORs, research projects, and work/internship experience .
IITs (Patna, Delhi, Kharagpur) offer B.Tech or dual degrees in Data Science/Analytics—admission through JEE-Advanced
Other top institutes like BITS Pilani, IISc, IIITs also offer relevant programs (CS, Data Science)
Leading MS programs in Data Science/Business Analytics (QS 2025):
MIT
Carnegie Mellon
Oxford
UC Berkeley
NTU Singapore
Harvard
NUS
ETH Zurich
Yale
University of Toronto
For Business Analytics masters: MIT Sloan, UCLA, ESSEC, HEC Paris, LSE, Columbia, Duke Fuqua, Imperial, ESCP, IE
Typical curriculum (3–4 years for bachelor, 1–2 years for master):
Core: Statistics, Probability, Data Structures, Databases, Machine Learning, Data Visualization
Tools/Software: Python, R, SQL, Tableau, Power BI, cloud platforms
Components: Project work, labs, electives (e.g., domain-specific analytics), sometimes mandatory internships or capstone projects
Data Analyst
Business/Data Consultant
BI Developer
Analytics Manager
Progression into Data Scientist, Data Engineer, Chief Data Officer
Business Intelligence
Operations Research
Financial/Marketing Analytics
MSc in Data Analytics/Science
MBA with Analytics specialization
Certifications (e.g., Google Data Analytics, Microsoft PL-300/Power BI)
Technical: domain knowledge, design, analysis, lab work
Soft: problem-solving, teamwork, communication, project management, adaptability to AI tools
Languages: Python, R, SQL
Visualization: Tableau, Power BI
Big Data: Spark, Hadoop
Cloud: AWS, Azure
Databases: relational & NoSQL systems.
PROS:
Growing demand and good pay
Applicable across sectors
Pathways to advanced roles/data science/leadership
Engaging work combining tech and insight
CONS:
Can be repetitive (reporting tasks)
Risk of burnout with heavy analytics load
Need continual upskilling due to AI changes