You need a strong foundational understanding of applied statistics, not a PhD-level deep dive. Here are the core topics you should master:

Descriptive Statistics 
Topics – Mean, Median, Mode, Range, Variance, Standard Deviation
Why – Helps summarize and understand data distributions

Probability
Topics – Basic probability, combinations/permutations, conditional probability, Bayes’ Theorem (intro only)
Why – Useful in predicting outcomes and understanding uncertainty

Distributions
Topics – Normal, Binomial, Poisson
Why – Essential for making assumptions and running models

Inferential Statistics
Topics – Hypothesis testing, confidence intervals, p-values, z-test, t-test
Why – Helps make conclusions about populations from samples

Correlation & Regression
Topics – Correlation, linear regression, multivariate regression
Why – Essential for discovering relationships and predictions

Data Sampling
Topics – Types of sampling, bias, sample size, central limit theorem
Why – Important for data collection and validity of results

ANOVA & Chi-Square
Topics – Basics of ANOVA and chi-square test
Why – Useful for comparing groups or categories

Learning Strategy (So You Don’t Feel Overwhelmed)
-Start with structured courses — e.g., Google Data Analytics, IBM Data Analyst, or Udemy/LinkedIn courses.

-Apply what you learn — Use free datasets (like on Kaggle) and build small projects.

-Stick to one concept at a time — Don’t try to learn Python, SQL, and stats all at once.

-Join communities — Reddit, Discord, and LinkedIn groups for data analytics are great for help and mentorship.