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<p>Python is one of the most popular programming languages used by Data Scientists around the world.</p>
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<p>Some of the features that make Python a favorite among programmers is that it is a high-level, interpreted, open-source language that provides great features for Object-Oriented Programming (OOPs). Python has amazing libraries that are suitable for Data Science applications. Today, the easiest and most straightforward way for someone to get into Data Science with no programming experience is by taking up a&nbsp;<a href="https://www.simplilearn.com/free-python-foundation-course-skillup">Free Python Course with Certificate</a>, because Python has very easy-to-learn syntax. In this article, we will discuss the scope of learning Python in Data Science.</p>
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<p><strong>Benefits of using Python for Data Science</strong></p>
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<p>Python language has become a favorite among Data Science professionals for the following main reasons</p>
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<li>Python is a free and open-source software. You can easily take a python course free of cost and start your data science journey with that knowledge.</li>
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<li>Python has an elegant syntax, making the programs easier to read.</li>
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<li>Python comes with a large standard library and enormous global community support.</li>
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<li>Python's interactive mode makes it a reliable tool for testing codes.</li>
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<li>It is simple to extend the code in Python by appending new modules even if they are implemented in other compiled languages like C++ or C.</li>
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<li>Being an expressive language, Python can be embedded into applications to offer a programmable interface.</li>
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<li>Python allows developers to run the code on any OS, such as Windows, UNIX, Mac OS X, and Linux.</li>
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<li>There are several Deep learning frameworks and scientific packages available with Python APIs, making it incredibly productive and versatile.</li>
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<li>Developers use Python for applications like sentiment analysis and natural language processing (NLP) because it provides a large collection of libraries for solving complex business problems easily and building strong systems and data applications.</li>
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<p><strong>Popular Python libraries for Data Science</strong></p>
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<p>Now let us look at some of the most popular Python libraries used for Data Science:</p>
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<p><strong>Pandas:</strong></p>
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<p>Pandas is an immensely favorite Python library among Data Scientists for data manipulation and analysis because it provides useful functions that are helpful in manipulating, aggregating, and visualizing a large amount of structured data. Pandas provide easy methods to perform analysis using large data structures and is extremely efficient in manipulating numerical tables as well as time series data. Moreover, it is a perfect tool for data wrangling. The two data structures in Pandas are –</p>
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<li>Series – For handling and storing data as one-dimensional data.</li>
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<li>DataFrame – For handling and storing two-dimensional data.</li>
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<p><strong>Numpy:</strong></p>
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<p>NumPy stands for Numerical Python. The Numpy Python library provides mathematical functions that can handle large-dimension arrays and provides several methods/functions for Array, linear algebra, and Metrics. It provides many useful features for performing operations on n-arrays and matrices in Python. Another feature that the Numpy library provides is the vectorization of mathematical operations on the NumPy array type, enhancing the performance and speed of execution of the operation. Numpy makes it easy to work with large multidimensional arrays and matrices.</p>
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<p><strong>Matplotlib:</strong></p>
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<p>For any organization, descriptive analysis and visualizing data are very important. Matplotlib, a Python library for Data Visualization, provides various techniques to visualize data in a more effective way. Matplotlib contains easy ways to make line graphs, histograms, pie charts, and other professional-grade figures and even customize every aspect of a figure. It has interactive features like zooming, planning, and saving the Graph in graphics format.</p>
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<p><strong>Scikit – learn:</strong></p>
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<p>Sklearn is a Python library that is specifically meant for machine learning operations, containing many algorithms and functions commonly used in machine learning. Built using NumPy, SciPy, and matplotlib, Sklearn provides simple tools for data mining and data analysis. Sklearn, by virtue of its consistent interface, provides users with a set of common machine-learning algorithms. Additionally, Scikit-Learn helps to implement popular algorithms on datasets and also solves real-world problems quickly.</p>
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<p><strong>Scipy:</strong></p>
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<p>Scipy, a popular Python library for data science and scientific computing, provides great functionality for computing programming and scientific mathematics. SciPy has sub-modules for optimization, linear algebra, interpolation, integration, FFT, signal, special functions, image processing, Statsmodels, ODE solvers, and other common science and engineering tasks.</p>
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<p><strong>Careers in Data Science using Python</strong></p>
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<p>As per a Kaggle study in 2018, 83% of data professionals use Python for data analysis. So, if you seek a switch to or starting in a Data Science career role, taking a python course free of cost will be the first step in that direction. To be a high-paid Data Scientist, one needs a mix of skills combining knowledge of Mathematics/Statistics, programming aptitude, and industry knowledge. The popular career roles in Data Science are:</p>
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<li>Data Analyst</li>
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<li>Data Architect</li>
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<li>Machine Learning Scientist</li>
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<li>Statistician</li>
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<li>Data Engineer</li>
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<li>Machine Learning Engineer</li>
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<p><strong>Conclusion</strong></p>
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<p>By now, it should have become pretty evident to you that Python has an irreplaceable role in the data science domain. Data Science using Python is a growing and rewarding sector. If you feel interested in pursuing a career in Data Science with Python, try Simplilearn's&nbsp;<a href="https://www.simplilearn.com/skillup-free-online-courses">free online courses</a>&nbsp;at SkillUp python course free of cost. Just click on the link given in the first paragraph to start your journey in Python programming today!</p>
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