How to Learn Python for Data Science?
September 12, 2020 | Data Science, Emerging Technologies, Python
The world has seen the emergence of many popular programming languages such as Java, C++, C#, R, Python, and COBOL. Of these, Python owes special mention owing to its passionate community of users spread across the globe, its simplicity, ease of use, and widespread usage. Python is even more popular within the data science profession. Ever wondered what makes Python so powerful? Is it the simplicity of the language? The dynamic semantics or flexibility? Simply put, Python is a powerful, expressive and dynamic programming language which is relatively simpler to learn. It is a dream language for coders. The widespread popularity of this language comes from the fact that it is extremely streamlined and easy to learn for beginners. Over the years, many languages have come and gone, but Python has stood its ground and has evolved by gaining new strengths. Popular websites such as Google, Spotify and Instagram are all built with the help of this dynamic language.
As mentioned before, Python is highly suitable for data science and analytics functions. The future of data science with Python is extremely promising as a lot of programmers prefer Python over Java, Scala, C++ and even R. It has become the go-to language for data scientists. Python is a major force in artificial intelligence, machine learning, big data and analytics. One of the main reasons for this immense popularity is the data-focused libraries offered by Python and Python syntax which can be used as a robust tool to visualize data.
With this blog, we focus not only on the benefits of learning Python for data science but also provide you with a guide on how to start this journey.
Steps to learn for data science:
- Set up your machine: We start with the most basic thing of setting up Python on your system. Setting up your machine by installing Python on your computer can be done easily. One of the easiest ways is to download Anaconda from Continuum.io. It comes as an Anaconda bundle consisting of most of the things you will need.
- Learn the basics of Python: The next step is to understand Python programming basics and get an introduction to data science. The foundation you build here will help you all along your journey in data science. Thus, it is imperative to have a solid understanding of core programming to practically translate the solutions into instructions on a computer.
You can follow the below tips to master those basics:
- One of the best tricks of the trade is to code every single day. They say practice makes a man perfect. This is absolutely true when it comes to perfecting your programming skill. Committing to coding every day can take you a long way on your Python for data science journey.
- When learning the basic Python data structures such as strings, lists, and dictionaries, the best learning tool is the interactive Python shell. The interactive Python shell is called Python REPL and needs to be installed on your computer.
- Make your learning process more collaborative and interactive by joining online tutorials and communities. This will help you in meeting like-minded people and enhancing your skills further. You can meet plenty of passionate Python learners at local events and meet-ups as well. You can share the tips and tricks and learn much more.
- You can also access additional resources and search for the best Python books. Some of the best books available are – Head-First Python (2nd Edition) by Paul Barry and Python Programming: An Introduction to Computer Science (3rd Edition) by John Zelle.
- Learn about data science libraries: Now we reach the interesting part. Python has an all-star line-up for data science libraries and this is where the real fun begins. The essential libraries are as follows:
- NumPy – Practising the NumPy tutorial provides you with a good foundation offering precompiled functions for numerical routines. NumPy allows an easy and efficient large array of numeric computations.
- Pandas – Pandas is a Python package containing high-level data structures. It is easier to handle missing data and avoids errors using Python on Pandas.
- SciPy – SciPy is a user-friendly Python module which provides fast and convenient N-dimensional array manipulation.
- Seaborn – Seaborn focusses on data visualizations and statistical models built on Matplotlib.
- Scikit-Learn – Scikit-Learn is focussed on machine learning and is built on SciPy. It provides a common set of machine learning algorithms, cross-validation and much more.
- Projects and practising – If you feel a bit overwhelmed right about now then relax your mind because you’re on the right path. This is the stage where you cross over from learning to getting started. Congratulations on making it. It is time for plenty of practice to hone those technical skills. Take your learning one step further and become innovative and assemble something of your own. You can structure your own projects by picking specific datasets.
This beginners’ guide scratches the surface of the vast ocean that is Python for data science. The language is growing and will keep evolving in multitudes, which means that the learning will be constant. There is a large amount of information available online and plenty of training and courses that you can enrol for. With these, you can figure out a structure and learning path for yourself. The professional possibilities and gain, once you cross the bridge are endless. There is a huge gap in the market for talented and skilled data scientists. Data Science is one of the most in-demand jobs and Glassdoor predicts data science and data analytics to make up more than 11 million jobs by 2026. Data science jobs have globally skyrocketed because the number of employers looking to hire from this talented pool of digital-savvy experts is increasing every day.
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