Data Analytics & Statistics Fundamentals
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Modern business runs on data. However, collecting data is useless without the statistical frameworks required to process, analyze, and interpret it.
Whether you are optimizing a digital ad campaign or running supply chain forecasting, understanding statistics is non-negotiable. This resource page directly links to Arjan KC’s structured notes on both pure statistics and applied business analytics.
Core Statistical Concepts (MPA509)
Before utilizing advanced dashboarding tools, a solid grasp of statistical math is vital to ensure you are not misinterpreting correlations or variances.
- Introduction to Statistics: The basics of defining populations, samples, and variables.
- Probability Terminologies: Understanding the core logic behind chance, expected value, and distributions.
- Introduction to Hypothesis Testing: The backbone of A/B testing—learning to set up null and alternative hypotheses to find statistical significance.
Business Intelligence & Applied Analytics
How do these math concepts translate to a corporate dashboard? The following guides focus heavily on the ‘why’ and ‘how’ of business data.
- The Business Analytics Process: Mapping out how raw data is captured, cleaned, analyzed, and finally presented to stakeholders.
- Predictive Analytics: Going beyond reporting the past to forecasting future trends using historical markers.
- General Analytics Guide for Nepal: A region-specific look into the common challenges and implementations of analytics tools for local firms.
- E-Commerce Analytics: Tying metrics specifically to retail outcomes—tracking Cart Abandonment, Customer Lifetime Value (CLTV), and Return on Ad Spend (ROAS).
By bridging the gap between raw statistical theory and applied business analytics, these guides offer a robust learning roadmap for aspiring analysts and data-driven managers.
