Refresher on Statistics - Issue 237
Key statistical concepts often used in data analytics.
Welcome to my Data Analytics Journal, where I write about data science and analytics.
Wrapping up the year with another “refresher”. This time, it’s about statistics - compiling all publications on statistics into one concise “take-home” guide.
Applying core statistics in analytics typically involves:
Distributions
Significance and Confidence
Correlations and Regressions
Causal inference
Law of Large Numbers and Central Limit Theorem
This publication is targeted at analysts, data scientists, and product owners who work with data and ML products.
Why do we need statistics? To bring certainty and confidence
The whole purpose of statistics (statistical theory, methods, and analysis) is to provide certainty out of uncertainty. In other words, when you don’t have a high degree of trust (in either quantity or quality of data), how do you make sure you make the right decision?
