While learning about Fourier Transform after Fourier Series, That we can calculate Fourier transform of periodic signals too. If we can take the Fourier transform of periodic signal too then my question is why we Fourier series if Fourier transform can be calculated for both periodic and aperiodic?
You can't take the Fourier transform of a periodic signal, the integral diverges for all multiples of the period.
This can be handled by the theory of distributions, but the Fourier series is a better fit.
The difference between a series and a function is, algebraically speaking, the field over which you define the mapping, and important concepts like convergence and differentiability. Your choice of whether you're after the Fourier Series representation or the Fourier Transform doesn't depend on what you want to analyze, but what you want to do -- these are two separate concepts, and you shouldn't confuse them.
For example, in Physics you'll need the Fourier transform to convert a crystal grid from spatial domain to impulse domain -- although crystals are assumed to be perfectly periodic, having but a series won't cut it as soon as you want to do functional analysis on it.
On the other hand, in digital signal processing, you typically neither have infinite nor perfectly periodic signals, so your Discrete Fourier Transform (DFT) gives you a finite series.
The point here is that you write yourself:
While learning about Fourier Transform after Fourier Series
Keep learning. Sooner than later, you'll understand the significance of both concepts.
It's all the same thing. They are just different flavors based on signal properties to make the math work properly.
If a signal is periodic than its transform is discrete and vice versa. If the signal is aperiodic the transform is continuous and vice versa. So there are a total of four flavors
time frequency name cont. aperiodic cont. aperiodic Fourier Transform cont. periodic discrete aperiodic Fourier Series discrete aperiodic cont. periodic Discrete Fourier Transform discrete periodic discrete periodic Discrete Fourier Series or Discrete Time Fourier Transform
The difference in the formulas is simply due to the fact that you need summation for discrete signals and integration for continuous signals.
Fourier series and Fourier transform basically involve the decomposition of the signals in terms of sinusoidal (or complex exponential) components. With such decomposition, a signal is said to be represented in the frequency domain. Most signals of practical interest can be decomposed into a sum of sinusoidal components. For the class of periodic signals, such a decomposition is called the Fourier Series. For the class of finite energy signals the decomposition is called a Fourier Transform.