Probability & Statistics
​Coursera course on probabilities - for data science, actually quite good in explaining a lot of the basic tools,prob, conditional, distributions, sampling, CI, hypothesis, etc.
    I.e, Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events.
    The problems considered by probability and statistics are inverse to each other.
    In probability theory we consider some underlying process which has some randomness or uncertainty modeled by random variables, and we figure out what happens.
=> Underlying process + randomness and random variables -> what happens next?
    In statistics we observe something that has happened, and try to figure out what underlying process would explain those observations.
=> observe what happened -> what is the underlying process?
    Finally, probability theory is mainly concerned with the deductive part, statistics with the inductive part of modeling processes with uncertainty

Introduction to statistics

    1.
    ​Table of content
    2.
    ​Median​
    3.
    ​Mode - most freq
    4.
    ​Weighted mean​
    5.
    ​Geometric mean​
    6.
    ​Harmonic mean​
    7.
    ​Percentiles​
    8.
    ​Mean deviation​
    9.
    ​Correlation​
    10.
    15.
    ​Probability​
    16.
    18.
    ​Variation vs variance - a private case
    19.
    ​Std vs variance - std is in the same metric as the mean, is the root of variance., allows outliers to influence, will not result in samples cancelling each other without the square root in the formula.

Introduction to Probability

    1.
    ​Types of events​
    2.
    ​Independent events​
    3.
    ​Conditional proba​
    4.
    7.
    ​Bayes​
    8.
    ​Least squares regresssion It works by making the total of the square of the errors as small as possible (that is why it is called "least squares"
    9.
    ​Random variables​

More on Statistics

    1.
    ​25 concepts (part 2), 29 more concepts (part1) & part 3 in statistics.

Wiki

    1.
    2.
    ​Joint probability​
    4.
    ​Chain rule - derivatives using the chain rule, on khan​
    1.
    ​Another great course on probability, distribution types, conditional, joint, chain, etc.
    2.
    ​Kahn academy
    3.
    ​A really good intro to probability, conditional, joint, etc.
    what are the known facts? Inherent in both probability and statistics is a population,
    every individual we are interested in studying, and a sample, consisting of the individuals that are selected from the population.
    in probability: would start with us knowing everything about the composition of a population, and then would ask, β€œWhat is the likelihood that a selection, or sample, from the population, has certain characteristics?”
    In statistics: we have no knowledge about the types of socks in the drawer. we infer properties about the population on the basis of a random sample.
Some calculations to get you into probability:
    Finding out the probability of an event
    Of two consecutive events (multiplication)
    Of several events (sum)
    Etc..

STATISTICAL SAMPLING AND RESAMPLING

    2.
    ​
Last modified 2mo ago