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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