Statistics is one of the critical FIVE pillars of Data Science & AI. Knowledge of statistics not only helps in model (Algorithms) understanding but also lets you improve the performance of the models through parameters fine-tuning.

Statistics is a vast subject, and knowing everything about it demands massive effort and dedication. Luckily all the concepts of statistics are not required when it comes to Data Science & AI. However, you need to know basic statistical concepts like hypothesis testing and some crucial tests along with the concepts of central tendencies and probability density functions.

Some of the essential terms (but not limited to) include mean, median, mode, standard deviation, variance, skewness, kurtosis, central limit theorem, various statistical tests, descriptive statistics analysis, etc.

Hypothesis testing is a form of statistical inference that uses data from a sample to conclude a population parameter or a population probability distribution. First, a tentative assumption is made about the parameter or distribution. This assumption is called the null hypothesis and is denoted by H0.

Practising the relevant questions and implementing the concepts taught in the course in real case studies and real datasets will help you improve your overall confidence in this field.