First off, understand there is difference between developing full-fledged software and doing data analysis using Python as a programming language. Clearly, here your aim is to do data analysis using Python, so learning Python becomes imperative for you. Right? Well, most of the people new to ‘big data’ and ‘data science’ go pell-mell, as they do not know where the correct essence of learning lies. They think that learning Python from A to Z will make them smarter, may be it can, but that’s too much time consuming. As a new aspirant, you should be able to make out as what you should exactly learn for doing data analysis using Python.
In this post, we will go through the most-likely path which will make you self-confident in Python and subsequently in data analysis.
Step 1 – Basics:
Your learning process starts with rudimentary knowledge. Learning resources for general are different than selected learning. So, be it anything, you must learn the basics involved in Python. To learn basics, you can refer Python communities or try hands at DataFlair. The list is as follows:
- How to import
- How to install new package
You should try learning these basics as soon as possible. The faster you pick them up, the sooner you start working on initial projects.
Step 2 – Get the Latest Version of Anaconda:
This is very crucial step, as getting Anaconda means having your time saved by peeping into unnecessary libraries. Anaconda is better than PIP. With Anaconda open source distribution, you can use various libraries needed for data science and machine learning. Mind here, Anaconda is also used for R. Well, you can download Anaconda easily, for more updated versions, visit some videos on YouTube. Or wanted to go directly, here is the link: https://www.anaconda.com/download/
Step 3 – Learn Regular Expression:
You have to learn this as well, as it will help you in data cleansing. It senses and collects shady errors from record sets, table or say database. It recognizes erroneous, improper, unfinished and unrelated segments of data and then modifies, replaces or deletes that.
Step 4 – Vital Libraries of Data Science and Machine Learning:
Libraries in Python are auxiliary but important. While coding you can fetch or import a slew of libraries for any function or module, thus it saves your time writing code. For the reason of library concept, Python is considered the simplest programming language in the world. Well, for data science, you need not have all libraries; well here goes the list of important ones.
Image Credit: DataFlair
From a student’s point of view, steps discussed above are important to learn. Thereafter, one has to get into the work of ‘project doing’. While doing projects of your area of interest if you get into doubts, the best option is to switch back to community help. Doing projects of your own will give you ample amount of experience and practice and may hone your skills beyond your imagination.