Statistics

Studying Statistics equips students with analytical and critical thinking skills necessary to process and interpret data effectively. This subject helps students develop expertise in problem-solving, decision-making, and logical reasoning, which are essential for various roles in commerce, business, and research.

Overview

Statistics opens pathways to careers in data analysis, market research, business intelligence, and actuarial science. It provides a strong foundation for roles in finance, economics, and data-driven industries. Many students pursue professional qualifications like CFA or actuarial certifications or study advanced fields like econometrics or data science.

Career Opportunities

Our graduates work and intern in a range of jobs in a broad range of fields — public relations, marketing, advertising, social media, television, radio, magazines, human resources, event planning, education, nonprofits, and technology. We’ve also had graduates start their own businesses and others go on to graduate school in communication, law, public health, strategic communications, and education.

Learning Outcomes

Understand and apply statistical methods to solve real-world problems in business and economics.
Perform data analysis using statistical tools and interpret results effectively.
Demonstrate the ability to use probability models and hypothesis testing in decision-making.
Build foundational knowledge for advanced studies in data science, analytics, and related fields.

Programme

Semester 1
Introduction to Statistical Methods
Collection and Classification of Data Tabulation and Presentation of Data Graphical Representation: Bar Charts, Pie Charts, Histograms Frequency Distributions
Key Takeaways: Students develop an understanding of data collection and visualization, laying the groundwork for statistical analysis.

Semester 2
Focus Areas: Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode) Measures of Dispersion (Range, Quartiles, Variance, Standard Deviation) Correlation and Regression Analysis Sampling Techniques
Key Takeaways: Gain the ability to summarize, interpret, and analyze data, with practical applications in commerce.

Semester 3
Focus Areas: Probability and Distributions
Basics of Probability Theory Probability Distributions: Binomial, Poisson, and Normal Hypothesis Testing and Confidence Intervals Index Numbers and Time Series Analysis
Key Takeaways: Understand advanced concepts like probability and hypothesis testing, essential for predictive analysis in business.

Semester 4
Focus Areas: Applied Statistics
Statistical Quality Control (SQC) Decision-Making Techniques Using Statistics Linear Programming Applications Case Studies in Business Data Analysis
Key Takeaways: Equip students with statistical tools and techniques to make data-driven decisions in commerce and industry.