How to Learn Python for Data Science?

Introduction to Python for Data Science

Python for data science has now emerged as the preferred language to be used by many data scientists around the world. It is considered as a high-level language which is considered as a good choice for object-oriented programming. It offers huge functionality for dealing with mathematics, scientific functions, and even statistics. There are extraordinary libraries to deal with the data science application. The main reason for the growing popularity of Python is that it is massively used in the scientific and research communities due to its ease of use and simplicity of syntax. It is because of this that Python is being adopted by people who do not even have an engineering background.

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The people from academia and industry believe that the deep learning frameworks which are available with the Python APIs and other scientific packages have made Python very versatile and productive. Thus, there has been a huge rise in learning the Python frameworks in recent times. Even in application areas, Python is preferred by the ML scientists as well. In the case of applications like natural language processing NLP and other sentiment analysis etc. developers also opt for Python as the latter offers a great number of libraries that help to solve complex problems quickly.

In this blog we are going to discuss how to learn python for data science, what all python for data science handbooks are available  and python for  data science tutorials

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Why Should you Learn Python for Data Science?

Python has over the years developed a dedicated community of users and an even faithful following with the professionals working with data science.

• If you have to rate the quality of Python in terms of simplicity, it does really well on that scale. Its simplicity is based on its accurate and methodical syntax. It has the beauty of finishing the same tasks as done by other languages but with much less code. This implements solutions that are really fast.
• Python has developed a highly varied and colorful community of data scientists which means that there is no shortage of python data science handbooks, and,python for data science tutorials, fixes for commonly occurring bugs, code snippets, etc.
• Additionally, Python is also endowed with a state-of-the-art library for data analysis and machine learning which considerably brings downtime needed to give relevant results.
• The code is easily extendable by the addition of new modules that are formed in other languages like C++ or C. As Python is considered as an expressive language which means that it is possible for offering programmable interface by embedding into the applications.
• It also lets the developer make use of the code on any platform like Windows, Mac OS X, UNIX, etc.

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The Python software is free which means there is no cost involved in usage and downloading of Python data science handbook or adding to its applications.

Version Battle: Python 2.7 v/s 3.4

This has become the most discussed topic, and if you plan to learn Python for data science, this topic will definitely cross your eyes especially if you are a beginner in the field.

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Python 2.7

• It comes with the wonderful support of the community which is essential in the early days. Python 2 which had been released since late 2000, has been in use for over 15 years now.
• There is a warehouse of third-party libraries. Although many libraries have given 3.x support, there are still many modules which work only on version 2.x. Thus, if you plan to use the Python for some particular applications like web-development with heavy dependence on other outside modules, 2.7 is a better option for you.
• Many features of 3.x are endowed with backward compatibility and thus can easily work well with the 2.7 version.

Python 3.4

• This is cleaner and quicker. Although there were some inherent glitches, they were also fixed by the developers. Many other small drawbacks were also fixed for setting a stronger foundation for the future.
• 2.7 is the last of the 2.x family, so ultimately everything must move to the 3.x family. Many stable versions have also been released by Python 3 for the last five years and will continue to remain the same.

Learning Python

To learn Python, it is advisable to follow the following steps:

1). Learning Core Programming Skills:

Efficient programming means not just memorizing the syntax but instead learning a new way of thought or approach. You have to invest your time and resources in building a strong base in concepts of core programming. Such a foundation helps translate solutions in mind itself.

Irrespective of the fact that you are completely new to the field of programming or already know any other language and have just got to only memorize the Python syntax, after this level you should have proper answers to all of the following:

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• Difference between an integer, float, and string
• Using Python in place of a calculator
• Structure and use of a for loop
• Use of conditional statements
• The functioning of Import statements
• Primary structure of a function

Additionally, you can also check the following resources to practice these concepts further.

• Code Fights: It is a platform that offers you short coding challenges that can be completed in 5 minutes.
• The Python Challenge: It is one of the most interesting challenges on the internet. It has 33 levels which can be accomplished by the Python scripts.
• PracticePython.org: This forms a collection of short practice problems for Python for data science tutorials which are updated weekly.

2). Data Science Libraries:

Libraries are basically collections of pre-existing functions and objects which can be imported into the script for saving on time. Python has line-up libraries for data science. Here are a few steps which need to be followed when you want to pick a new library.

• You should open a fresh Jupyter Notebook
• Further down, you should be thoroughly going through the documentation for getting a proper introduction about the modules.
• Then you have to import the library in your Jupiter Notebook.
• Here you have to go by the stepwise quickstart tutorial to see how the library works
• Finally, you may just review the documentation to learn about other capabilities.

A Jupyter Notebook is a favorite among data scientists. It is basically a lightweight IDE and is recommended for many projects.

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3). Data Science Portfolio:

For all the beginners in data science, it is important that you build a data science portfolio. Latter can comprise projects which have several datasets and should be able to leave the readers with amazing insights. Such efforts, reflect your interest and the time you have given to learn the language and other vital skills for programming. It is not essential that you build your portfolio around any specific theme. It is also a good idea that you develop your soft-skills and knowledge about statistics alongside.

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4). End-to-End Projects:

Thus, a basic understanding of the core programming concepts and the salient features are libraries are enough to get started with Python. However, to consolidate your knowledge, you may want to go through various projects for practice.

• Kaggle Competitions: It is actually a website that hosts many competitions for data science. The most important feature of Kaggle is that each project is self-contained and you are being presented by a dataset, a goal and few guidelines to start. However, they do not really replicate real-time data science.
• DIY Projects: The primary benefit of these projects is that they represent real-world data science more closely. You have to set your own goals, collect data, engineer the features, etc. But, for this to be successful it is important that you know the workflow of data science.
• Advanced Techniques of Data Science: The learning in data science is never fully complete, and there will always be some advanced course which you may consider to sharpen your skills further. You may consider taking courses on statistics to get acquainted with regression, classification and k-clustering models. Alternatively, you can also go for machine learning for streamlining models. Thus, the idea is to keep learning.

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How long does it take to Learn Python?

In the case of data science, it takes almost three months to 1 year of regular practice. Some people go by the process very slowly so ultimately it will depend on your goals and the time you can spare constantly to learn python .

Commonly Used Libraries in Data Science

• Numpy: It provides mathematical functions for handling the large dimensional arrays. It also gives various methods for arrays like linear algebra, metrics, etc. It stands to denote the numerical Python.
• Pandas:  It is basically used for manipulation of data and its analysis. It gives large data structures and helps in the manipulation of numerical tables and time-series data. It is considered an apt tool for data wrangling.
• Matplotlib: It is used for visualization of data which is vital for every organization. It lets for quick line graphs, pie charts, etc. and can be used for customization for every aspect of the figure.
• Scipy: It is used for data science and provides amazing functionality to both scientific mathematics and programming in computing.
• Scikit-learn:  This library is dedicated to machine learning as it provides many algorithms that are useful in the same way. It is endowed with some very simple tools for data mining and analysis.

Conclusion

Python for data science is a very useful language that has found its use in a variety of applications. It is a favorite for engineers, data scientists, academia, etc. Learning Python needs commitment and a plan. It is not very difficult to learn if you have experience with other languages.

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