Bayes specify informative and uninformative prior I would be really thankful for any hint
Let $x_1,\dots x_n$ be a sample from a geometric distribution with probability parameter $\theta$
$f(x_i\mid \theta)=\theta(1-\theta)^{x_i}$
The likelihood is given by $L(\theta\mid x_i, \dots, x_n)=\prod_i f(x_i\mid \theta)$
Assume we have observed a sample of size n=5 with values $x_i=5,6,9,5,4$. Specify a) an informative prior, (b) and uninformative prior, and determine the posterior distribution for each setting. Make a plot of prior, likelihood and posterior distribution. 
 A: An informative prior would require some prior knowledge about $\theta$: for instance, if you know both the mean and the variance on $\theta$, you can use a Beta prior
$$
\theta \sim f(\theta) = \dfrac{
\theta^{\alpha-1}(1-\theta)^{\beta-1}}
{B(\alpha,\beta)}
$$
and pick $(\alpha,\beta)$ according to this prior information...
A noninformative prior is derived from a formal rule like Jeffreys', which leads to the Beta $B(1/2,1.2)$ prior.
A: 
Where to put the data

The data values go into the likelihood. The likelihood is a product of $f(x_i\mid \theta)$, one for each data value. You just substitute the data into the formula you gave!
Your data are $x_i$ values. The first data point is $x_1$, and so on. Your likelihood is a product of $f(x_i\mid \theta)$. You gave a formula for $f(x_i\mid \theta)$. You substitute each data value in for its particular $x_i$. 

and how to make a plot of prior, likelihood and posterior distribution.

Wait, you don't know how to draw plots of functions? All three are simply functions of $\theta$. You should plot those functions for values that are interesting (where they're not really small) - for the likelihood and posterior, this will be values reasonably close to $\hat{\theta}$
