Hey Developer, I’m back with a new series of blog posts on the foundation of statistics for data science and machine learning. Here will be covering all the topics that are essential for a good understanding of data science and machine learning concepts.

I will be updating the links of all the topics every day, so stay tuned and utilise this time to learn some new skills.

#5MinsDaily

#5MinsDaily can help you learn statistics for Data Science and Machine Learning.

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Statistics

A branch of mathematics dealing with the collection,analysis, interpretation, presentation and organisation of data.

How Statistics Work ?

By Example:

1) Formulate a hypothesis, a proposed explanation of something

  • Example. Smoking Causes Cancer

2) Make Observations

  • Get data regarding smokig habits and medical history of patients

3) Analyze data and make conclusions

  • Accept or reject hypothesis
  • Modify experiment, get more data etc..

List of Topics , We will be covering…

  1. Introduction To Sets
  2. Set Membership
  3. Cardinality, Set Complement and Set Laws
  4. Experiments, Events, Sample Spaces & Points
  5. Set Operations on event
  6. Independence and Dependence Events
  7. Introduction To Probability and Examples
  8. Rules of Probability
  9. Discrete and Continuous Probability
  10. Counting Sample Points
  11. Permutations
  12. Combinatorics
  13. Binomial Coefficients
  14. Multinomial Coefficients
  15. Probability of a union of events
  16. Conditional and Unconditional Probability
  17. Independence of Events
  18. Multiplicative and Additive Law Of Probability
  19. Laws of Total Probability
  20. Bayes Theorem
  21. Random Variables and Discrete Random Variables
  22. Discrete Uniform Distribution
  23. Binomial Distribution
  24. Geometric Distribution
  25. Hypergeometric Distribution
  26. Continuous Distribution and Continuous Uniform Distribution
  27. Normal Distribution
  28. Gamma and Beta Distribution
  29. Concept of Expectation
  30. Mean
  31. Variance
  32. Joint Distributions and Probability Mass Function
  33. Marginal PDF’s and Function of 2 or more random variables
  34. Covariance and Correlation
  35. Bernoulli Distribution
  36. Bernoulli Trials
  37. Poisson Distribution
  38. Normal Distribution
  39. LogNormal Distribution
  40. Multinomial Distribution

Hurray ! You did it.

Some of the course content and list of topics to be covered from Dmitri Nesteruk course on statistics. The course content was awesome so thought of sharing it through a series of blogs for everyone.

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