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Are you an international student looking for a dynamic and comprehensive degree programme that equips you with essential analytical and computational skills to analyze and interpret complex datasets in today's data-centric world?
The Bachelor of Science (BSc) in Statistics might be the perfect choice for you. Designed to meet the demands of various industries such as finance, healthcare, technology, and more, this programme offers an excellent pathway for those aiming to establish significant careers in data analysis and statistics. With a well-structured BSc Statistics syllabus covering a broad range of subjects from Probability Theory and Statistical Methods to advanced topics like Machine Learning and Data Mining, the BSc in Statistics not only opens up a wide array of job opportunities but also provides a competitive edge in terms of BSc Statistics salary and career advancement.
BSc Statistics Highlights
Aspect |
BSc Statistics Course Details |
Course Full Form |
Bachelor of Science in Statistics |
BSc Statistics Average Fees |
INR 3-4 LPA |
BSc Statistics Course Duration |
3 to 4 years |
BSc Statistics Degree Requirements |
10 + 2 pass with minimum 50% aggregate, Math as an essential subject |
BSc Course Admission |
Entrance exams: GSAT, SUAT, DSAT, BHU UET |
BSc Course Specialisations |
Decision Analytics, Data Science, Statistics and Applied Mathematics, Quantitative Finance, Bioinformatics, Computational Biology, Financial Mathematics, Statistical Biology |
What is BSc Statistics?
The BSc Statistics full form - Bachelor of Science in Statistics, underscores the programme's focus on providing a science-based statistical education. As a Bachelor in Statistics, graduates are recognised for their ability to interpret complex data sets and provide insightful analyses, skills that are highly valued in any industry.
BSc Statistics Syllabus
The BSc Statistics syllabus equips students with essential skills in statistical methods and data analysis, opening up job opportunities in various fields. It covers crucial subjects like probability theory and regression analysis, preparing graduates for data-driven challenges. The programme emphasises practical application, leading to competitive salaries and career growth. Eligibility requires a strong math foundation, ensuring readiness for statistics complexities. Graduates are sought after for their analytical prowess, making a BSc in Statistics a key to a successful career in data science. Here is the Statistics syllabus given in the table according to Semesters 1-6.
Semester 1 |
Semester 2 |
Demoivre’s theorem |
Differential Calculus |
Equations theories, Fundamental theorem of algebra and its consequences. |
Review of Differential Equations |
Inequalities |
Review of integration and definite integrals |
Semester 3 |
Semester 4 |
Statistical Methods |
Index Numbers |
Measures of location (or central tendency) and dispersion |
Demand Analysis |
Probability Theory |
Utility and Production Functions |
Random Variables |
Mathematical Finance |
The expectation of random variables and their properties |
Time Series |
Semester 5 |
Semester 6 |
Sample Surveys |
Numerical Analysis |
Basic sampling methods |
Inverse interpolation |
Stratified random sampling |
Numerical integration |
Fundamental theorem of algebra and its consequences |
General Linear models |
BSc Statistics Subjects
The curriculum of a BSc in Statistics includes key subjects like Probability Theory, Inferential Statistics, and Regression Analysis, essential for understanding statistical theories and applications. These BSc Statistics subjects are fundamental for students aiming to excel in the field and pursue a successful career with a Bachelor's in Statistics.
1st Year
- Analytical Solid Geometry
- Probability & Statistics
- Demoivre’s Theorem
- Differential Calculus
- Equations Theories
- Inequalities
- Review of Integration and Definite Integrals
2nd Year
- Real Analysis
- Mechanics
- Abstract Algebra
- Statistical Methods
- Index Numbers
- Demand Analysis
- Utility and Production Functions
- Mathematical Finance
- Probability Theory
- Time Series Analysis
3rd Year
- Sample Surveys
- Basic Sampling Methods
- Stratified Random Sampling
- Numerical Analysis
- Inverse Interpolation
- Numerical Integration
- General Linear Models
BSc Statistics Eligibility
The Bachelor of Science in Statistics (BSc Statistics) is an undergraduate programme that focuses on the principles of data analysis, including collection, analysis, interpretation, and presentation. This degree prepares students for diverse careers in sectors like finance, healthcare, and research, thanks to a comprehensive syllabus covering subjects from probability theory to regression analysis. With the increasing reliance on data-driven decision-making, the BSc in Statistics offers promising job opportunities and competitive salaries, highlighting its significant scope in the modern world.
- Minimum marks: Students must complete 10+2 with subjects Physics, chemistry, & Mathematics. And, to gain admission into the BSc Statistics programme, candidates must secure a minimum of 50% marks from a recognised Indian school.
- Core Subject: Applicants must have Mathematics as their core subject in class 12.
- Language Proficiency: A good score in English language proficiency exams like IELTS, TOEFL, etc., is often required for non-native English speakers.
- Standardised Tests: A minimum required score in SAT or ACT exams may be necessary for admission to some institutions.
- Entrance Exams: Some colleges and universities might also consider scores from their entrance exams or national or state-level entrance tests.
Top Universities to Study BSc Statistics Abroad
Studying BSc Statistics abroad offers students an unparalleled opportunity to immerse themselves in diverse cultures while gaining a world-class education in one of the most sought-after disciplines in today's data-driven world. The BSc Statistics syllabus at top universities abroad is designed to provide a comprehensive understanding of statistics subjects, blending theoretical knowledge with practical application to prepare students for the global job market.
Here are some of the top universities where you can pursue a BSc in Statistics and what makes them stand out:
- Stanford University, USA
- University of Oxford, UK
- University of Cambridge, UK
- Harvard University, USA
- University of California, Berkeley, USA
- ETH Zurich, Switzerland
- Massachusetts Institute of Technology (MIT), USA
- Imperial College London, UK
- University of Toronto, Canada
- Australian National University, Australia
Explore more universities with aecc Search
BSc Statistics Scope for International Students
The BSc Statistics degree is a gateway to numerous opportunities in today's data-centric world, emphasising its broad scope across various industries. The curriculum, enriched with a diverse range of subjects from the BSc Statistics syllabus, equips students with critical skills in data analysis and interpretation. This preparation opens doors to a multitude of job opportunities in sectors such as finance, health care, technology, and more, making BSc Statistics graduates highly sought after. The degree not only paves the way for exciting career paths but also positions graduates for success in various roles, from Data Scientists to Financial Analysts, reflecting the degree's versatility and the high demand for statistical expertise in decision-making processes across industries. Here’s the table that provides different career fields with their average salary.
BSc Statistics Jobs |
Average BSc Statistics Salary (USD) |
Data Scientist |
$122,000 |
Statistician |
$92,000 |
Market Research Analyst |
$65,000 |
Actuarial Analyst |
$81,000 |
Business Analyst |
$76,000 |
Biostatistician |
$95,000 |
Financial Analyst |
$85,000 |
Operations Research Analyst |
$84,000 |
Frequently Asked Questions
The BSc Statistics curriculum includes subjects like Probability Theory, Inferential Statistics, Regression Analysis, Time Series Analysis, and Statistical Computing, among others. These subjects provide a comprehensive understanding of statistical methodologies and data analysis techniques.