Hey! I’m a Software Engineer having some decent experience with C/C++ and Web technologies. The field of Data Science looks very promising and hence I tried to learn about it a little.
Without having any prior experience with Python or R, I decided to dive into the field of Data Analytics just like that and then learn the things on the go.
Because of various reasons, I preferred to go with Python because being an OOP language it’s basic structure is similar to C++ which I’m pretty experienced with. This would mean learning Python first and then with the usage of Python libs start exploring and examining different datasets.
If anyone is confused between going with R or Python, this might help:
For practicing Python online go to:
You can also set up a Python interpreter in your terminal.
So once, I was sure I am going with Python. I learned the basics like:
- Control flow tools like loops.
- Functions and Classes.
- File Input Output stream.
For learning Python in an interactive manner while practicing I’ll recommend this :
Also, the python documentation is pretty well written:
These are some fairly basic things which shouldn’t take much time if you have a coding background.
Decent basic knowledge of python makes understanding the use of libraries quite simple. After that, I thought of learning about some of the popular Python libs used for Data Analysis.
The most popular ones being:
- Numpy: http://www.numpy.org
- Pandas: http://pandas.pydata.org
- Matplotlib: http://matplotlib.org/contents.html
- Scipy: https://docs.scipy.org/doc/
- Scikit-learn: http://scikit-learn.org/stable/documentation.html
With these, you’ll able to play around with your dataset pretty well.
Each library has its specific use which I’ll explain in further posts separately. There are a lot of MOOCs available which nicely explains their usage and makes you work with them simultaneously. Of the million options available on the internet I tried and find these particularly helpful (and free of course) :
These should get you started with using Python for Data Science. This is just a small piece of the huge cake. A lot of active communities maintain these libs if any issue is faced.
A well-curated list of MOOCs, blogs, books and other sources one might find helpful:
In the coming posts, I’ll try to explain some basic things we can do with the help of these libraries. In case anyone faces any problem with any of the things above said, feel free to contact me.