Statistics focuses on collecting, analyzing, interpreting and visualizing data to inform decision-making across domains—including business, healthcare, sports, government and science. Statisticians help organizations make data-driven decisions
Skills & traits:
Strong foundation in calculus, linear algebra, probability, and mathematical reasoning
Analytical and critical thinking, problem-solving, attention to detail, and communication skills
Comfort with coding and computing—especially R or Python
Interests:
Enjoy working with data, spotting patterns, modeling uncertainty
Curious about real-world issues and their quantitative analysis
In India:
Must have studied Mathematics or Statistics at the 10+2 level
Minimum marks often around 50–60% in Class 12 science stream
Abroad:
High school diploma with strong math background; some require portfolio for applied/data-driven programs
Specific admissions requirements vary by university
In India:
CUET (for universities like DU, BHU)
ISI Admission Test (B.Stat/B.SDS)
Entrance tests: DUET, BHU UET, JNU, plus IPMAT, SET, NPAT for institute-specific programs
Abroad:
Standard tests like SAT/ACT for US bachelor's programs
Portfolios may be needed for data science-focused courses
Christ University, Bangalore
Fergusson College, Pune
Loyola College & Madras Christian College, Chennai
St Xavier’s, Kolkata
Jadavpur University, Kolkata
MIT
Harvard University
Stanford University
UC Berkeley
University of Michigan–Ann Arbor
University of Toronto
Johns Hopkins
Oxford
UCLA
UPenn
Core subjects: Probability, Statistical Inference, Regression, Time Series, Multivariate Analysis
Supporting mathematics: Calculus, Linear Algebra, Discrete Math
Practical components: Data analytics, computing labs, internships, project/studio work
Typically a 3-year program in India (B.Sc/B.Stat), 3–4 years in global systems
M.Sc/MA in Statistics or Applied Statistics
Specialized Master’s: Biostatistics, Data Science, Econometrics, Actuarial Science, Data Analytics
Diplomas/certificates in machine learning, big data, data engineering
Statistical modeling and data analysis
Proficiency in tools/languages: R, Python, SAS, S-Plus, Excel
Critical thinking, problem-solving, data visualization, communication of findings
Data Analyst, Statistician, Biostatistician, Data Scientist, Actuary, Quantitative Analyst
Roles in: Pharmaceuticals, Finance, Insurance, Government, Market Research, Tech
Positions in academic/government research and policy analysis
R and Python—core for statistical analysis
SAS, S‑Plus—enterprise-level environments
Excel, SQL, Tableau/Power BI for business analytics