In this topic we have covered introduction of vector, addition, subtraction, dot product, coordinate system, arithmetic operations, Eigenvectors and eigenvalues etc

In this topic we have covered it’s type, property, rank and nullity etc

Starting with an introduction, we have covered all its cases.

We have covered norms, changing coordinates, independent vectors and basis vectors.

In this section we have covered functions, derivatives, integration, maxima and minima and gradient descent.

In this part we have covered mean median and mode.

After it’s introduction we have covered how we use it’s mean, variance, and standard deviation of one to find other.

We have covered it’s introduction and after that types of events with examples, types of probability (Conditional, marginal ) , random variable.

We have covered prbobability distribution, normal distribution, binomial distribution, Poisson distribution.

One of the famous theorem of statistics which states that any distribution follows normal if taken enough samples(>30).

In this part we will discuss what is hypothesis and why do we need it

In this section we will cover z test, t test, one – sample t test, two- sample t test, paired t test, chi2 test etc

We will cover what is ANOVA test, types of ANOVA etc

Here we will cover what are these terms and how it’s relevant in data science.

In this assignment we will apply the learnings of central tendency, variability on the housing dataset.

This assignment it used to check the variability of the dataset and suggest the best mobile phone according to the requirement of the customer.

It will help you to implement conduct an independent two sample t-test to check if you hypothesis is accepted or rejected.

This assignment will conduct t-test on samples and use z score to see if hypothesis is accepted or rejected.

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