Consultant with 11+ years of experience in Technology & Services. I bring customer-centric mindfulness that enables firms to innovate and thrive. Certified in Data Science, Machine Learning, Artificial Intelligence & Alteryx
In today’s AI-driven world, Data Science has been imprinting its tremendous impact, especially with the help of the Python programming language. Owing to its simple syntax and ease of use, Python for Data Science is the go-to option for both freshers & working professionals. Python also finds its use in academic research and building statistical models adding to its versatility. So, before moving forward with learning Python for a successful Data Scientist career, one must understand its significance first.
It’s undisputed truth that, we’re living in an information age and the speed at which we’re generating data has never been heard or seen before. Just for your reference, every day we create roughly 2.5 quintillion bytes of data. (That’s humongous !!!!). But why we care ??
Well, because we have access to such huge amount of data, we also have tons of possible opportunities to extract some of the unusual & interesting facts from data and use it for variety of purposes like increase business revenue, reach more customer, help human kind, create super user experience, create medicines for diseases and automat things etc.
But the question comes, if you had access to a such large dataset, would you be able to find the answers you seek?
The short answer is “Yes”.
The long answer is, we need to apply certain framework or procedures on data and pass it through customized pipelines where at each level, we process data and at the end of it, we derive the expected results.
We can apply these frameworks in few possible ways and “Python” is one of the best, easy and effective way of doing it and this is where the topic of “Python for data science” comes into play.
The journey of Data Science begins with programming language or in other words, programming language is critical and most important component of DS. Now that programming language can be anything from Python, R, Scala, Java, Go, SQL and few others.
However, among all languages that you can select from, Python is the most popular language for Data Scientists. It’s not “the most popular” just for saying or by stats but there are fundamental reasons which make Python as most popular and first choice for large crowd.
Python is the easiest programming languages to start your journey. Also, its simplicity does not limit your functional possibilities. We can certainly say, beginners and experts can work with Python very easily to become productive quick. Python language is free & open source, which contributes very heavily to the success of the language.
Python offers free access to hundreds & thousands of open sources third party libraries or packages. These packages are built by community and using these libraries results in effective results and huge savings on time & efforts. Some of the most popular libraries are NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch, NLTK etc.
Python along with libraries has demonstrated tremendous capabilities of implementing the most common & critical functionality of ML. Libraries like Scikit-Learn and TensorFlow are the backbone of it. Implementing ML & its processes has become super easy & effective with Python.
Python is light weight and super portable. It allows developer to do cross functional programming like SQL, Java and Unix quite easily. Python can run on any OS including Windows, Unix, iOS & Solaris.
One of the exciting parts of Python is Raspberry Pi. Using this combination, user can create robots, cameras, remote-controlled toys, or arcade machines.
Python is the single language in which you get speed at multiple dimensions, like: -
It’s a surprise to many new comers that Python is fully grown language that even supports web-development. Yes, that’s right. Python has extensive support for Web-development framework like Django, Flask, Pyramid, Web2py among many others. Companies like Twitter & Instagram is using Python heavily in their web applications.
Apart from these fundamental reasons, Python also offers following: -
There are 8.2 million+ Python developers in the world, so you can imagine how big and strong the community is. During your learning curve, this community plays critical role.
Python is versatile language which has large number of applications, starting from simple application development to automation to DS/ML application to web-development. Once you learn the language, you can opt for many additional roles.
We can list down many more reasons to select this powerful programming language, so it’s up to your which reason resonate with you the most. We strongly suggest trying this language due to its endless possibilities which will help you to build amazing products and help businesses.
We know that even though Python is powerful and the super easy, it’s not the only programming language for ML/DS. We’ve few other choices but more often than not, ‘R’ programming language is considered as another choice for doing DS/ML.
Let’s try and understand the similarities and differences between R & Python: -
|Objective||R is primarily used for Data Analysis & Statistics.||Python is used for end-to-end system development, deployment & running it in production.|
|Primary Users||Scholars and R&D.||Programmers, Developers, ML engineers, Testers, Data Scientist.|
|Flexibility||R has different way of writing code then other languages, hence learning curve is difficult in beginning.||Python has very simple and institutive way of writing the code. Hence learning curve is smooth.|
|Integration||Run in local environment.||Has strong support for external apps and programming languages.|
|Tasks||It’s good for running & obtaining primary data analysis results.||Python is good for developing & deploying algorithms in PROD.|
|IDE||RStudio||Spyder, PyCharm, Visual Studio, Jupyter notebook, Eclipse.|
|Important Packages/Libs||ggplot2, caret, zoo is some of the most important libs in R.||Pandas, NumPy, Scikit-Learn, Seaborn are the most important libs in Python.|
I hope above table gives you clear and practical differences between two languages. We now know which language is more useful in which cases, however overall Python comes out as lucrative option and that's why you hear terms like “Data Science using Python”.
Data Science here is indeed an umbrella term, however, let’s try and understand, how Python is super helpful and integral part of end-to-end Data Science pipeline.
This image depicts a very gh-level pipeline for DS. Largely we are interested in each stage of this pipeline & how Python is associated with it.
Exploratory Data Analysis (EDA)
|Steps||Wrangle||Clean||Explore||Pre-process||Model Training||Model Validation||Model Deployment|
Above table showcases, Python has presence in each of the stages of DS pipeline.
Apart from this, Python has in-depth support for NLP (Natural Language Processing) & CV (Computer Vision) which are advanced domain of Machine Learning.
Python is adopted by organization of every size and domain, as it provides end-to-end coverage for DS pipeline and it has quite rich use-cases. Overall Python helps you achieve data science essentials as one stop shop.
In general, any programming language should be learned in “Practical or Hands-on way”. Learning programming language requires understanding of concepts, understanding of building blocks of the language and hard-core practice of each & every concepts.
Python or any other programming language can be learned either by self or by experts. In self-learning mode there are some challenges such as, you’ll need to explore and decide learning path by yourself, you’ll have to find relevant content for each topic and make sure its standard to industry and lastly you need immense motivation to keep it going without any external monitoring.
On the other hand, you can start learning with help of experts which is also recommended way if you’re fresher or new to Python or it’s eco-system.
The best way to learn Python for data science is to enrol yourself in online or offline courses offered by a lot of EdTech’s or even large organizations. This approach is more guided where you learn step-by-step in a controlled & monitored environment. The idea is to start learning Python basics for data science & then gradually move forward.
Now off course it you search on google; you’ll find tonnes of courses. However, we recommend to check Data Science with Python course offered by KnowledgeHut. This is 4-week comprehensive course and will take you through 42 hours of live classes and 6 projects.
KnowledgeHut also offers hands-on & case-study oriented courses on Data Science which you can explore at KnowledgeHut data science courses
By no means this is exhaustive list, and you may come across 100’s of other options. However, we would highly recommend choosing course which covers all modules with hands-on practice and provide you certification at the end. Once you start diving in it, you will start discovering best Python ides for data science.
As Python is open-source language, there are indeed free books available on internet which you can refer as and when needed. Following are some resources:
You can also read daily updates & events at: -
You can also be part of ever-growing Python community: -
And finally, you can also look at official web page of Python,
I can assure you that, combination of live course, book reading and doing honest practice is more than enough for mastering Python for data science.
Well, I guess if you put in honest efforts and spend 3-4 hours a day learning & practicing Python, I can assure you that you can master the language within 30 days.
Just imagine, if I ask you to learn Spanish or German. Do you think mastering it is possible in 30 days? I don’t think so.
But mastering Python is possible if you’re doing it rigorously.
Python is “the” important skill for any data science individual and time can’t get more excited than we’re living in. You can choose to learn this language for any reason but trust me, once you master it, you will open the doors of endless opportunities for yourself.
No. Python is such an easy language that anybody can adopt it easily.
Yes, if you master the language then you can get role as Python developer, Django developer, automation developer etc.
“Best” is subjective term but looking at support and diversity provided by Python language for every task of DS, we can conclude that Python does the job well.
Data Science is a practice which gives you framework for extracting patterns & insights from data and Python is just enabler for doing Data Science. Using Python, the tasks of Data Science can be easily & effectively done.
No. Python is fairly simple.
No, you don’t need it.
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