Most of the time, Python is basically a person talking to a machine in English. If you want it to print something, you simply type print(“something”). Here’s how the same would look in Java:
Python is much more natural in both “vocabulary” and syntax. You would usually have less punctuation marks to place (and miss). Take a look at this example from jaxenter.
Especially for a non-technical person, less pesky nuances lead to more enjoyable learning. You can always move on to more annoying stuff later.
To prevent misunderstanding, a high number of job openings is not a bad thing at all. The way programmers work, it makes little sense to run a business on barely-trained and underpaid staff, which some retail companies do. Thanks to various remote work options, IT wasn’t shook by the coronavirus pandemic that hard. A 5-10% drop in revenue is quite mild by Covid standards.
First released in 1990, Python has been around since before personal computers truly became personal. The language was quickly adopted all over the world, so you can find technical documentation and broader guides in dozens of languages. First-party documentation from Python Software Foundation is quite vast as well.
Nowadays, Python is an optimal choice if you don’t want to be moving around too much. Python’s last major revision, 3.0, was released back in 2008. Since then, it has been mostly updated on an annual basis. Legacy support is quite good as well: Python 2.0 wasn’t discontinued until January 2020.
If you think about it, one can draw parallels between IT and healthcare. Just like not everyone has to be a doctor, not everyone has to be a software developer. There is work to do and money to make, all enjoying regular IT benefits, for a lot of other technical people. Automated Quality Assurance is a good option, as your Python knowledge will result in more value per man-hour compared to a Manual Tester.
Keep in mind that horizontal mobility is very much a thing in IT. Going to Automated QA is not a death sentence for your software engineering aspirations. In fact, you could prove more valuable (and better compensated) than pure software engineers, as you cover at least part of the coding load. Besides, passion projects are a huge mobility tool in the industry, so you can practice and improve your engineering skillset there.
If you want to work with Big Data and extract insights, Python is the language to use. You will be working with the data processing library pandas and a data visualization library matplotlib. The R programming language is a solid alternative to Python but I suggest we keep things flexible.
Similarly, Python libraries suit Machine Learning best. Should you choose this hot specialization, you will be using TensorFlow for image recognition and Natural Language Processing, leveraging Pytorch for performance-demanding tasks, and utilizing NumPy’s high-level mathematical functions. As a piece of trivia, a lot of cool things around working with data, including matplotlib visualizations, rely on NumPy.
While being beginner-friendly, Python also powers the world’s leading products of varying complexity. Google (search, YouTube), Facebook/Instagram, Spotify all rely on Python to run key parts of their operations. There also companies like reddit, who take inspiration and customize Python libraries for core services and authentication among other things. A lot of the time, Python-powered solutions contribute to the development of the language.
With big companies comes great (enormous) teams. You would literally have a team of several software engineers working on one aspect of Facebook Messenger notifications. Although such companies will be picky about background and portfolio among other things, you can totally land a job on Dropbox with Python alone.
Just like with sports, musical instruments or video games, you don’t have to be a pro at programming to enjoy it. Coding is about solving challenges, preferably in the most efficient way and possibly gaining insights from the results. You don’t need to build software for that: there are a ton of resources with small tasks that get harder as you progress.
You can leverage Python’s data libraries to find answers about the world around you. Whenever you see new trends in healthcare or economy, especially if they don’t make sense, you can challenge them with code. pandas library will help you find correlations between (for example) the number of smokers and updated age brackets. You can then use matplotlib to visualize the statistics and spot outliers.
You can choose self-learning or get started with our Python course. We cover the essentials over 4 months of real-time classes, give you the basic skills for backend development, and set you up for learning data tools. All classes are online.
We teach you to write simple apps and programs in Python, design from scratch or extend the existing code base, test it, convey some refactoring, and optimizing.Start studying
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