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.

Join this groups to start for this course to get regular updates :

MLAIT On WhatsApp: https://chat.whatsapp.com/IDTD8ONgeZw2InepJEKrM7

MLAIT On Telegram: https://t.me/mlait

### 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…

- Introduction To Sets
- Set Membership
- Cardinality, Set Complement and Set Laws
- Experiments, Events, Sample Spaces & Points
- Set Operations on event
- Independence and Dependence Events
- Introduction To Probability and Examples
- Rules of Probability
- Discrete and Continuous Probability
- Counting Sample Points
- Permutations
- Combinatorics
- Binomial Coefficients
- Multinomial Coefficients
- Probability of a union of events
- Conditional and Unconditional Probability
- Independence of Events
- Multiplicative and Additive Law Of Probability
- Laws of Total Probability
- Bayes Theorem
- Random Variables and Discrete Random Variables
- Discrete Uniform Distribution
- Binomial Distribution
- Geometric Distribution
- Hypergeometric Distribution
- Continuous Distribution and Continuous Uniform Distribution
- Normal Distribution
- Gamma and Beta Distribution
- Concept of Expectation
- Mean
- Variance
- Joint Distributions and Probability Mass Function
- Marginal PDF’s and Function of 2 or more random variables
- Covariance and Correlation
- Bernoulli Distribution
- Bernoulli Trials
- Poisson Distribution
- Normal Distribution
- LogNormal Distribution
- 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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