To accomplish this goal, data science<\/strong> professionals need the finest tools to leverage advanced techniques that can turn data into actionable insights. There\u2019re some prominent languages like Java, C, C++ etc that can be used to make meaning out of data. However, Python<\/strong><\/em><\/a> has emerged as the most popular programming language used for data science<\/strong>, a StackOverflow survey revealed.<\/p>\n In<\/em> the coding world, Python<\/strong> is considered as a kind of Swiss Army Knife.<\/span> It supports structured programming, object-oriented programming, functional programming patterns, and more. For example, Google has developed TensorFlow, a deep learning framework that has been created using Python as the primary language.<\/p>\n Apart from Google<\/strong><\/em><\/a>, other tech giants like Netflix, Facebook, NASA etc have been using Python<\/strong> as a prominent language for a long time. There\u2019re some particular situations where this language is the most appropriate data science<\/strong> tool to perform the job. For example, it\u2019s perfect when statistical code is needed to be incorporated into the production database or when data analytics tasks need integration with web apps. The full-fledged programming nature of this language makes it an ideal fit for implementing algorithms.<\/p>\n Let\u2019s<\/em> consider the goal of data science<\/strong> professionals once again \u2013 derive actionable insights from data. To accomplish this, some computational tasks are needed to be performed. Here, Python<\/strong> libraries like NumPy and Pandas can be used to perform the job quickly.<\/p>\n Data may not be readily available to data science<\/strong> professionals, so it needs to be scraped from the web. Here Python<\/strong> libraries like BeautifulSoup<\/strong><\/em><\/a> can be used to extract data from the web.<\/p>\n In order to drive insights, visualization of the data is a must. Here, libraries like Matplotlib<\/em><\/strong> are used to represent data in the forms of pie charts, graphs, and other formats.<\/p>\n The next stage is machine learning where tasks are made efficient and easy by using Python<\/strong> libraries like Scikit-learn<\/strong><\/em>.<\/p>\n Python <\/strong><\/em>is open source and free, and thus anyone can write a library package in order to extend its functionality.<\/span> And data science<\/strong> is the field that has experienced the advantages of these extensions. Just to give you an idea of the popularity of Python<\/strong> in the data science<\/strong> field \u2013 66% of data scientists reported using it daily, in 2018. Now, you may ask that what\u2019s so special about Python<\/strong>? Let\u2019s have a look.<\/p>\n Python<\/strong><\/em><\/a> is widely considered as a beginner\u2019s language because it doesn\u2019t have any difficult learning curve, and a developer with fundamental knowledge can work with Python<\/strong>.<\/span> If you compare it other languages used in data science<\/strong> like R, Python<\/strong> comes with a shorter learning curve and beats the competition by offering an easy-to-understand syntax. In addition, code implementation is less in Python<\/strong>, so data science<\/strong> professionals can spend more time to focus on the algorithms.<\/p>\n One<\/em> of the major factors that helped Python<\/strong> to take the most sought after place in the data science<\/strong> field is its wide range of libraries that can be used for analysis, visualization, scientific computing etc. Let\u2019s quickly discuss some of them.<\/p>\n Python<\/strong> <\/em>has emerged as a scalable language compared to other languages like R.<\/span> Python\u2019s<\/strong> scalability lies in its flexibility that it offers to solve problems. As a result, it has been used by different industries to develop tools and applications of almost every kind.<\/p>\n One<\/em> of the biggest reasons behind the exponential growth of Python<\/strong> is its massive community. There\u2019re millions of users who\u2019re happy to offer suggestions or advice when a Python<\/strong> learner get stuck on something. And chances are, someone else has already been stuck there at some point of time.<\/p>\n As<\/em> Python<\/strong> has become extremely prevalent in the field of data science<\/strong>, there\u2019re lots of resources which are specific to using Python<\/strong> in the context of data science<\/strong>.<\/span> Meetup groups for data science<\/strong> professionals using Python<\/strong> can be found across the globe.<\/p>\n In<\/em> the data science<\/strong><\/em><\/a> field, machine learning<\/span><\/em><\/strong><\/a> is one of the major elements utilized to maximize the value from data.<\/span> With Python<\/strong> as a major data science<\/strong> tool, exploring the fundamentals of machine learning becomes effective and easy. Put simply, machine learning heavily encompasses mathematical optimization, statistics and probability, and Python<\/strong> has become one of the most sought after machine learning tool that lets aspiring professionals do the math easily.<\/p>\n Apart from all these, Python<\/strong> comes with varied visualization options that help in creating graphical layouts, web-ready plots, charts, among others.<\/p>\n Today<\/em> it\u2019s evident that the future is extremely bright for data science<\/strong> professionals and learning Python<\/strong> is just the right thing to get your journey toward the field started. Let\u2019s have a look at the steps.<\/p>\n First<\/em> of all, you need to get the basics right to learn Python<\/strong>. There\u2019re lots of ways to accomplish this \u2013 from taking a course to self-teaching to watching tutorials. However, we strongly suggest taking a course for this purpose. And if you\u2019re looking to enter the data science<\/strong> field, look for courses that are particularly designed to teach you Python<\/strong> in the data science<\/strong> context.<\/span> During this stage, try to join a learning community where you can find like-minded people passionate about Python<\/strong>.<\/p>\n Once<\/em> you\u2019ve gained a solid understanding of Python<\/strong> fundamentals, it\u2019s time to learn Python<\/strong> libraries that are used in data science<\/strong>. The most important of these include Pandas<\/strong><\/em><\/a>, NumPy and Matplotlib. If you get stuck somewhere, seek help to a Python<\/strong> community and most likely you\u2019ll get it.<\/p>\n Assuming<\/em> you\u2019re planning to enter the data science<\/strong> field, a proper portfolio is a must. Your experience in working on different datasets should be clearly mentioned. This not only gives your fellow learners something to collaborate on but also demonstrates the future employers that you\u2019ve actually invested your time to learn Python<\/strong>. During this stage, you should start working on developing other data science<\/strong> essentials like soft skills.<\/p>\n This<\/em> is the stage where you should be learning advanced Python<\/strong> and data science<\/strong> techniques. Ideally, you should take an advanced course from a reputed institute. There\u2019re different options available like taking free online courses, learning by reading books, attending an immersive data science<\/strong> bootcamp etc. However, if you truly want to ensure that you\u2019ve covered all the points and want to be job-ready quickly, enrolling with a data science bootcamp<\/span><\/em><\/strong><\/a> should be your best bet.<\/span> That way, you\u2019ll not only be able to pursue your dream at a relatively affordable rate but will be able to develop some greatly useful connections as well.<\/p>\n The<\/em> field of data science<\/strong> is evolving quickly and the technologies and skills that are necessary to become a data science<\/strong> professional may not be the same tomorrow. So, you need to continue learning for both Python<\/strong> and data science<\/strong> fields to maintain a competitive edge.<\/p>\n For<\/em> the above reasons and others, Python<\/strong> is so much beloved by data science<\/strong> professionals and programmers. Data science aspirants<\/em> <\/strong><\/a>often come from different backgrounds other than computer science and feel extremely overwhelmed by the difficulty level of the field. But Python\u2019s<\/strong> inherent simplicity and readability make it comparatively easy for them to pick up the learning pace. Also, the huge number of available dedicated analytical libraries means that data science<\/strong> professionals in almost every industry will find packages tailored to their needs already.<\/p>\n1- How is Python related to data science?<\/em><\/strong><\/h3>\n
![\"How](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/How-is-Python-related-to-data-science.jpg\")
<\/p>\n2- How is Python involved in every stage of data science?<\/em><\/strong><\/h3>\n
![\"How](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/How-is-Python-involved-in-every-stage-of-data-science.jpg\")
<\/p>\n3- Why is Python heavily preferred in the data science landscape?<\/em><\/strong><\/h3>\n
![\"Why](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Why-is-Python-heavily-preferred-in-the-data-science-landscape.jpg\")
<\/p>\n3.1- Easy to learn<\/em><\/h4>\n
![\"Easy](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Easy-to-learn-data-science-bootcamp-in-silicon-valley.jpg\")
<\/p>\n3.2- A wide range of data science libraries<\/em><\/h4>\n
![\"A](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/A-wide-range-of-data-science-libraries.jpg\")
<\/p>\n\n
3.3- Scalability<\/em><\/h4>\n
![\"Scalability](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Scalability-data-science-bootcamp-in-silicon-valley.png\")
<\/p>\n3.4- Python<\/em><\/strong> community<\/em><\/h4>\n
![\"Python](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Python-community-data-science-bootcamp-in-silicon-valley-300x79.jpg\")
<\/p>\n3.5- Huge amount of Python<\/strong> resources<\/em><\/h4>\n
![\"Huge](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Huge-amount-of-Python-resources-data-science-bootcamp-in-silicon-valley.jpg\")
<\/p>\n3.6- Python<\/em><\/strong> and machine learning<\/em><\/h4>\n
![\"Python](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Python-and-machine-learning.png\")
<\/p>\n4- Simple steps to learn Python for data science<\/em><\/strong><\/h3>\n
![\"Simple](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Simple-steps-to-learn-Python-for-data-science.jpg\")
<\/p>\n4.1- Master Python<\/strong> basics<\/em><\/h4>\n
![\"Master](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Master-Python-basics.-data-science-bootcamp-in-silicon-valley.png\")
<\/p>\n4.2- Learn Python<\/strong> libraries used in data science<\/strong><\/em><\/h4>\n
![\"Learn](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Learn-Python-libraries-used-in-data-science-300x156.png\")
<\/p>\n4.3- Develop a data science<\/strong> portfolio<\/em><\/h4>\n
![\"Develop](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Develop-a-data-science-portfolio.png\")
<\/p>\n4.4- Learn advanced data science<\/strong> techniques<\/em><\/h4>\n
![\"Learn](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Learn-advanced-data-science-techniques.jpg\")
<\/p>\n4.5- Keep on learning<\/em><\/h4>\n
![\"Keep](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/Keep-on-learning-data-science-bootcamp-in-silicon-valley-1.jpg\")
<\/p>\nIn conclusion\u00a0<\/em><\/strong><\/h3>\n
![\"final\"](\"https:\/\/academy.magnimindacademy.com\/wp-content\/uploads\/2019\/05\/final-300x225.jpg\")
<\/p>\n