If you are a Data Science enthusiast or willing to take the path of being one, then you are at the right place to start with. The main aim of this article is to provide an individual as many details as possible and in a comprehensive way, guide them through the right path at least on the learning side. This article also discusses the steps that one has to take on the learning path of Python for data analysis. If you are already aware of these components and have followed a different approach to achieving this, then please feel free to let us know as well. But for the greater good let us start with this article on the assumption that the readers have almost no knowledge on this area at all.
Python, a programming language is a general-purpose programming language which is getting popular by the day. One other reason for which it is getting popular over these days is the provision for programming on Data Science. Organizations worldwide use Python to gain more and more insights from the data that is available to them already and try to look for avenues to get a competitive edge over others. Unlike any other Python tutorial, this won’t talk much about the examples as such as it takes more than just an article to cover everything in it. Let us discuss further on ways to store data and do some manipulations over it, start our own analysis – maybe?
Now that we are here and have made up our mind to spend some time in understanding these technologies, let us also try to set up our machines as well. Easiest of the ways is to download Anaconda from continuum.io and install it, as it covers most of the things that are required for us to start with. If it is your first time with this step then it hardly matters as it would try to update all the required libraries for the setup and stuff like that, if you have already a setup – then it might require your attention on what to update and what not to. With all that, let us know what is it that we need to learn for performing Data Science related analysis on Python – it is NumPy and many other data science libraries available in Python. We will take a look at each of these, the installation and the basics of these as well.
If you are a total novice, then there will be a definite question dwelling in your mind on why to use Python only. There is no hard and fast rule to use Python only, you can also use R as a language to perform data science related analysis and many other frameworks apart from just these two. The main reason that one can say is that Python is an easier Programming language that can be used for both general purpose programming and also for data science related analysis as well. Now that we have decided to go with learning Python (if you are a novice with Python as well), then we might have to follow the below steps:
Most of the Python learning should be done here starting from what the programming language is all about, to install the pre-requisites, understand the language, do some homework on the coding side, understand the data structures etc. Once you have good knowledge of Python basics, then we might extend the understanding of the libraries and data structures. The outcome of this learning should at least cover Lists, Tuples, Dictionaries, List comprehensions, Dictionary comprehensions. There is a lot of resources online for you to start up with all of these, but we would be suggesting to use the interactive Python tutorials from DataCamp as it provides all these details ground up in a very pleasing manner.
Once you get all the information that is required to get started with Python, you should spend as much time as possible on your coding skills with Python. Believe me, when I say, coding with Python is a cakewalk if you know the things that you ought to know.
We suggest that you spend at least 2 days on this activity in understanding the basics, understanding the other details and stuff like that. Once you gain some confidence in the subject, then you can prove your mettle by taking up any programming challenges that are available online in pure Python itself.
Regular expressions will be applied a lot when you are planning to do some data cleansing activities, data manipulation activities on text data. While going through the concepts altogether, you can start preparing a cheat sheet for Python’s regular expressions or you can find them online very easily. It is suggested that you prepare it all by yourself so that you get a chance to use each and every of the regular expression.
Related Page: Lists concepts in Python
We suggest that you spend at least one day with this activity to gain more knowledge, experience and comfort factor. For further practice sessions you could follow tutorials on text cleansing, helps you on various steps involved during the data cleansing/wrangling processes.
Isn’t this the topic of the day? This where the fun all begins – let us now try to go through a brief introduction into each of the scientific libraries that are available with Python.
NumPy, SciPy, and Matplotlib – all these libraries put together are considered a replacement for Matlab, a popular platform for technical computing.
NumPy is a package available with Python which stands for ‘Numerical Python’. It can mention as a library that consists of all multidimensional array objects and also routines for processing these array objects. Using NumPy, developers can perform the following operations:
SciPy is a collection of all the available mathematical algorithms and functions that are built on NumPy extension of Python. This adds significant power to Python by providing additional functionalities, commands, and classes that help in manipulating and visualizing data that is available. This also helps in developing sophisticated and specialized applications making Python a powerful programming language. With this library, there are provisions to do parallel programming, web programming and also database level subroutines.
Matplotlib is yet another library for making the 2D plots of arrays in the Python programming language. Most of these commands have their origin from the MATLAB graphic commands, it is yet independent of MATLAB but still, be used in a Python program in an object-oriented manner. This library is purely written in Python and this again uses NumPy heavily along with it the other extension code to provide better performance on larger array objects. The design for this library was to achieve the plots with fewest commands possible.
This forms the core of all the processes where Scikit-learn is the most powerful Python library for Machine learning. Here in this step, you should be learning more about the algorithms like regressions, decision trees, ensemble modeling and non-supervised learning algorithms like clustering. You could definitely go through one book for sure – Programming Collective Intelligence, which is a classic one on the whole subject. Here you will understand almost all the topics that are required for a data scientist to know and here there is no suggestion that one can give on how many days an individual has to spend. This is all about learning, practicing and mastering the concept altogether.
If you have come to this step, then you should be a master in almost all the above steps – considering the assignments and practice sessions can be further done to improve on your skills on this concepts. But once you have a good command over Machine learning concepts, you are now ready for your next and your last step to be successful in becoming a successful data scientist. You’ve guessed it right, you’ve got to give Deep Learning a shot with your existing knowledge.
Having said that, you should already be knowing what Deep Learning is from the concepts above. You could definitely be learning more in detail and more in specific when you focus your time on this. Here you would also be getting your hands dirty with Neural Networks as well. There is no definitive knowledge base that you can follow in this category, it is more of your own self-learning put to use and few of the recent additions to the online learning courses on DataCamp or Coursera.
In this article, we have tried to understand the need of Python as a programming language – both as a general programming language and also as a programming language in conjunction with data science libraries. Though we have not gone through any specific examples here, we have gone through almost all the important details that one would necessarily require gaining the most from Python and the data science libraries with Python. Having said that, we have also gone through the basics of Machine Learning and also Deep Learning – the process to gain more details about it and how to put use Python and the data source libraries.
Hope you’ve got all the required details from this article itself and the article was descriptive and clear enough in providing the details. Please help us with your valuable feedback so that we can improve our further coming ones on similar topics.
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