With Data Science emerging as the hot new career option for the 21st century, it is attracting both young aspirants and professionals like a moth to a flame. While a career in Data Science is highly promising, the part where freshers tend to get astray at the beginning itself.
If you’re just starting with Data Science, the question that will first pop up in your mind is:
Where do I begin?
We’ll put a rest to your confusion. You begin with Python.
Now, you might ask – Why learn Python? What’s so special about it?
Why choose Python for Data Science?
It might sound cliched, but Python is a perfect choice for beginners trying to get started in Data Science. There are numerous reasons for this. But before we dig in deeper into those reasons, let’s look at some stats to back our claim.
According to a recent study, Python is the most popular programming language choice among Data Scientists.
Python has been at the top for quite a while now – nothing surprising about that.
Why?
A report by Cloud Academy maintains that:
“Python is known to be an intuitive language that’s used across multiple domains in computer science… It’s easy to work with, and the data science community has put the work in to create the plumbing it needs to solve complex computational problems. It could also be that more companies are moving data projects and products into production. R is not a general-purpose programming language like Python.”
It is an intuitive language with simplistic vocabulary stacked with full-featured libraries (also called frameworks) which helps produce the desired results faster than any other language. Python is a high-level language that is independent in itself – you don’t need any prior programming experience to learn Python. And the best part – it can do everything.
Here are 5 reasons that’ll show you why Python is great for beginners!
- Easy to learn
The foremost reason that makes Python a perfect choice for beginners is its simplicity and smooth learning curve. Its syntax is very simple and beginner-friendly.
- Scalability
Python is a highly scalable language and is also much faster than other languages such as R, Stata, and Matlab. Its scalability further enhances its flexibility quotient, which is extremely useful in problem-solving and app development.
- Wide choice of libraries
When it comes to libraries, nothing can beat Python. The language is also an appropriate choice for Game Development. Python comes with a host of Data Science and Data Analytics libraries including Pandas, NumPy, SciPy, Scikit-Learn, StatsModels, and many more. Thanks to such a vast canvas of libraries, Python can always come up with great solutions for addressing specific problems.
- ActivePython community
An active and robust community backs Python. No matter what your issue is (we’re talking about coding problems here, not life issues!), you can always count on the Python ecosystem to help and support you. The Python community is regularly contributing, developing libraries, and creating new Python tools. This is one of the major reasons for Python’s popularity.
Also read: Learn python online free!
- Myriad options for visualization
Python is loaded with several visualization options. A good case in point – Matplotlib, that has further provided the foundation for the development of other libraries such as Pandas Plotting, Seaborn, and ggplot, to name a few. These rich visualization frameworks allow you to make sense of the data at hand and also visualize your findings through pie charts, graphical plots, graphs, and even web-ready interactive plots.
How to learn Python for Data Science?
Now we’ll show you how to learn Python in a few simple steps.
- Set up your machine.
You cannot possibly learn Python without prepping up your machine for it, can you?
The most convenient way to do it is to download Anaconda from Continuum.io, and you’ll be good to go since it is equipped with almost everything you’ll need down the road.
- Start with the basics of Python.
The best way to start learning Python would be to find a suitable Python course specifically designed for Data Science. Python courses introduce you to the fundamentals of Python, including variables, data types, functions, loops, operators, conditional statements, among other things. You will not only need to understand what these concepts are but also learn about their specific purpose.
- Get comfortable with Python Libraries.
As we mentioned before, Python libraries are immensely helpful in programming. So, once you’ve mastered the fundamentals of the language, you must move on to the next best thing – Python libraries. Some of the widely used libraries are Pandas, NumPy, SciPy, PyTorch, Theano, Scikit-Learn, Keras, and Eli5.
- Master Data Analysis, Manipulation, and Visualization with Pandas.
If you wish to work with Python, you must know the nitty-gritty of Pandas. It comes with a high-performance data structure, known as a “DataFrame” that works best for different types of tabular data. In addition to that, it also has many useful tools for reading/writing data, handling missing data, filtering data, cleaning raw data, merging datasets, and visualizing data. Once you know Pandas inside-out, your efficiency will increase by leaps and bounds.
But there’s a catch – Pandas incorporates many functionalities for accomplishing the same task. Your goal should be to find the best practices.
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- Work on mini Python projects.
By the time you will reach this step, you will have known all the basics of Python, its libraries and their uses. Now’s the time to put your theoretical knowledge to practical use – working on Python projects. You don’t have to build something too complicated; you can start working with APIs and developing small applications with Python. You could also try automating small routine tasks with Python.
Bottom line – try to put your knowledge to good use and build something!
- Keep practising and upskilling.
“Practice makes a man perfect.”
It’s the same for Python as it for everything else. With regular practice, you’ll hone your programming skills. The more you practice, the better you’ll get. Apart from developing personal Data Science projects, you could always take part in Kaggle competitions, enroll in advanced online courses, attend Data Science and tech conferences/seminars, read journals and books, etc. There are many ways of learning – you have to be open to the idea of learning!
Check out all trending Python tutorial concepts in 2024.
Different Libraries of Python Used for Data Science
Python is used for data science primarily because of its libraries. Python has numerous libraries that make data analysis, data cleaning, data visualization, and machine learning tasks easier. Some popular Python libraries are as follows:
- NumPy: This library makes using Python for data analysis worthwhile by offering support for different mathematical tasks on multidimensional matrices and arrays.
- Seaborn: This data visualization tool offers visually-appealing statistical graphs. It helps you view distributions, confidence intervals, and other graphs.
- Pandas: The Pandas library is extremely popular and easy to use. It enables easy analysis and cleaning of tabular data.
- Matplotlib: This library in Python for data science enables the creation of static or interactive line graphs, box plots, bar charts, scatterplots, and more.
- Scipy: This library is useful for scientific computing to provide help with statistical tasks, optimization, and linear algebra.
- Requests: This library can help with data scraping on websites. It comes with a user-friendly and responsive method of configuring HTTP requests.
- Statsmodels: It is a statistical modeling library for statistical tests and models. It can help with generalized linear models, time series analysis models, linear regression, and more.
Apart from all the general data manipulation libraries, data science experts also find various powerful machine learning libraries in Python. These machine learning libraries make it easy for data scientists to offer solid, open-source libraries for any desired machine learning algorithm. These libraries make data analysis without compromising performance.
The different machine learning libraries can also help data scientists to build precise and powerful neural networks. A few popular machine learning and deep learning libraries in Python are as follows:
- Pytorch: Created by Facebook’s AI research group, Pytorch is a popular framework for deep learning. It offers a lot of flexibility and high speed. However, the low-level API of Pytorch makes it a little complex for beginners.
- Keras: It is a comparatively easier deep learning framework with a high-level API. It serves as an interface for the TensorFlow library. The framework can be used for building neural networks with a Tensorflow backend.
- Tensorflow: It is a high-level library for creating neural networks. It was primarily written in C++. But it also combines the simplicity of Python without compromising performance and power. However, Tensorflow is not appropriate for beginners.
- Scikit-learn: It is an extremely popular machine learning library to support your needs for supervised as well as unsupervised tasks.
What Does the Future of Python for Data Science Look Like?
Professionals will continue to leverage the power of data science using Python as the programming language becomes more popular. With advanced machine learning, deep learning, and similar data science tasks, you will notice an increase in the use of Python libraries. Even top companies are adapting Python libraries, and the programming language will be relevant in the industry for a very long time.
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To conclude…
Follow these steps and keep practising religiously, and you are sure to master Python in about three months. However, you must remember that Python is evolving every day, even as we speak – someone somewhere is actively contributing to the Python community. Python’s easy learning-curve, its high-scalability factor, and of course, its simplicity makes it a beginner’s language. And as it goes in programming, once you master one programming language, picking up other languages won’t be an arduous task anymore.
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