Learning Python for Beginners in Data Science
There’s so many good resources out there it’s honestly getting harder to go wrong. You just have to keep going onto the next one. You will have to solve a lot of problems with Python. In looking for ways to learn Python I believe in writing as much Python code as possible. Trying to solve smaller problems before directly moving into Data Science is very necessary. Building small projects using python makes oneself familiar with the different libraries
If one has a programming experience try to learn the Python hard way starting with difficult problems, building kernels of different machine learning algorithms on your own, implementing different ML algorithms using one’s own algorithm and trying to optimize the algorithms as much as possible.
Python is really amazing and I think you chose the language well. It has a steep learning curve (meaning you can learn a lot in a short amount of time and you see results fast), but it also doesn’t slow down. So the more time you put in, the better you get. When you are finished with the basics, you start with a library. When you are finished with the library, you go to the next.
If you are learning Data Science, pretty soon you will meet Python. Why is that? Because it’s one of the most commonly used data languages.
It’s popular for 3 main reasons:
- Python is fairly easy to interpret and learn.
- Python handles different data structures very well.
- Python has very powerful statistical and data visualization libraries.
- First start learning the basics of Python (if you haven’t already) in Codeacedemy
Next, when you have done that, take any tutorial of basic Python you want and read over it. Skim the parts that you have already learnt in the Codeacedemy course and IMPLEMENT the parts that you haven’t dealt with yet. Don’t just read a text, try to really work with new techniques to learn them. Here is one I find good: ”Python Programming Tutorials ”
- Data Science in Python
The amazing things with libraries in Python is, you can learn by doing. That means you don’t need to spend a billion hours getting familiar with it before you can start your own project, the python libraries can take a lot of work away from you.
That’s why I would advise you next to go through this page: ”Python Programming Tutorials”
For tutorials one can follow YouTube videos of Sentdex for python used in Data Science.
For courses one can learn from Udacity and Udemy which provide great courses based on Data Science for beginners using python .
One can also use GitHub to learn code for smaller projects and contribute in Github to increase ones knowledge.
If you like books, a few of my favourite books for getting started:
- Python for data analysis Wes Mckinney
- Data Science from scratch by Joel Grurs.
Learning python is very easy but deciding which kernel or library would optimize the result is more important in data science.
Participating in coding Competitions which host a bit tougher problems which have a lot of constraints have to be tried to be solved using python. Practicing python programming on daily basis is very necessary. Building the smaller projects and trying to optimize on them before handling huge loads of data is necessary. One should be accustomed with necessary libraries of python as mentioned above. Python is a very easy language but we always have to remember that it is 5x Slower than other languages but still it is used because of its flexibility. So my recommendation would be to use the flexibility of python programming to its maximum level. There are many projects being developed on python it being very flexible but only few of them provide a good solution.
So python is a language which can be self taught and learned very easily the more one practices.