Programming is one of the most profitable careers in the world. You can learn online or offline. If you have already decided you would like to start your adventure in the IT tech world, begin with learning Python. NIT DATA-No 1 Python training institute in Hyderabad is here to guide you how to learn Python faster at the same time effectively with which you can nurture career-relevant Python programming skills.
We think that the first step to learning any programming language is making sure that you know how to learn it. Learning how to learn is perhaps the single most important skill involved in computer programming.
Why is knowing how to learn so important? The answer is easy: As languages develop, libraries are created, tools are updated. Knowing how to learn is going to be crucial for keeping up with those changes, and becoming a successful programmer.
In this article, we are going to suggest some learning strategies to get you started on the path of becoming a rockstar Python programmer.
It is possible to learn Python faster. How fast depends on what you want to achieve with it, and on how much time you can dedicate to learning and practicing Python regularly. Before we dive in further, we would like to address some questions of how fast you should learn Python. If you are interested in learning Python programming basics, you can take up to two weeks to master with some regular practice. If you are interested in mastering Python in order to tackle difficult tasks or projects, or spurring a career change, it will take you a lot longer.
In this article, being the top data science training institute in Hyderabad we are going to offer tips that are designed to help you build up your Python coding knowledge within a shorter period of time.
If you are wondering what learning Python costs, there is a similar “it depends” answer. There is a wide variety of free resources available on the web, not to mention various books, courses, and platforms published just for beginners.
Another question that you may have is, “How difficult will learning Python be?”. . If you have some programming experience with another language, like R, Java, or C++, then learning Python faster is likely easier than for people who have not programmed before.
Now Let us dig in. the following are the top 8 pro tips to help you learn Python fast.
Top 8 Pro Tips for Learning Python Fast!
At the very least, you (and your resources) should have covered the basics. Without understanding these, you will struggle to tackle difficult problems, projects, or use cases. Below are some Python fundamentals which include:
If you are really crunched for time, you can explore all these basics in quick bursts at many different websites: docs.python.org, RealPython.org, Stavros.io, developer.google.com, PythonForBeginners.org.
Before starting learning Python, set your goals for the learning. The challenges that you encounter as you begin learning are easier to overcome when you have a goal in mind.
Also, you will know which learning materials you should focus or review, based on the goals. For instance, if you are interested in learning Python to analyze data, then you want to do exercises, write functions, and explore Python libraries that will make it easier to analyze data.
The following are a few examples of Python goals that may pertain to you:
Python resources can be broken down into three major categories: interactive resources, non-interactive resources, and video resources. In-person courses are also an option, but they will not be covered in this post. Interactive resources have been becoming more prevalent over the past few years with the popularity of interactive online courses which offer practice and coding explanations.
If you seem to code, it is because you really do. Interactive resources are usually free or at a nominal cost, or you can sign up for a free trial before buying.
Non-interactive resources are your more traditional, time-tested ones; these are books (digital and print) and websites (“online textbooks”). Many Python newcomers choose these because of the familiar, easy-to-follow nature of these media. As you will see, there are plenty of non-interactive resources to choose from, and most are free.
Video resources have been popularized in the last 10 years through MOOCs (Massive Online Open Courses), and they are similar to college lectures captured on video. In fact, the video resources are frequently supported & promoted by prestigious universities.
Now, video resources for a variety of subjects, including Python programming, are plentiful. Some of these video resources are pre-recorded courses hosted by learning platforms, while others are live-streamed courses provided by online education providers.
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In addition to learning Python, it is helpful to know one or two Python libraries.
Libraries are collections of specialized functions which act as “accelerators”. Without them, you would need to write custom code to perform the specialized tasks.
Pandas, for instance, is a highly-regarded library to manage tabular data. Numpy helps with performing mathematical and logical operations on arrays.
You could take the pain of downloading a Python installer from the Python Software Foundation site, and then go through the hassle of source-sourcing and downloading the extra libraries; or, you could download an Anaconda installer, which comes already packaged with many packages that you will be using regularly, particularly if you are planning to use Python for data analytics or data science.
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You will want to install the Integrated Development Environment (IDE), which is the app that lets you write, test, and execute code in Python.
When it comes to IDEs, the right one is the one that you like using most. According to different sources, the most popular Python IDEs/text editors are PyCharm, Spyder, Jupyter Notebook, Visual Studio, Atom, and Sublime. First, the good news: they are all free, so give them a couple of try-outs before settling on one. Next, the “bad” news: Each IDE/text editor has a slightly different UI and feature set, so you need a little bit of time to get a hang of using each.
For first-time Python users, I recommend writing your code in a Jupyter Notebook. It has a straightforward design and a simplified feature set, which does not get in the way, and makes practicing and prototyping Python easier. It even comes with dedicated visualizations for Dataframes and charts. If you download Anaconda, the Jupyter Notebook comes preinstalled.
As you work through the exercises, examples, and projects in Python, one of the easiest ways to fix bugs is by learning from other Python developers. Simply do a quick Internet search, and enter keywords related to your bug.
For instance, “how to join two lists in python” or “how do you convert to dates in python” are totally acceptable searches to perform, and they will take you to some popular forum-based communities like StackOverFlow, Stack Exchange, Quora, Programiz, and GeeksforGeeks.
This is the part that most people skip, resulting in failures or delays. Now, all that is left is to create your timeline. I suggest setting a two-week minimum schedule, in order to spread your learning and make sure that you are giving yourself plenty of time to properly cover Python basics, practice writing code in your IDE, and fix problems with code.
Part of the challenge (and the fun) of learning Python, or really any programming language, is fixing bugs. After the first couple weeks, you will be surprised how far you have come, and have plenty of practice to keep learning more advanced stuff provided by the resources you have chosen.
By now, you have established your minimum learning time frame, know how to select your study goals, have a list of study resources and learning methods to choose from, and you know what other programming considerations you all have to make. We hope that you have made the most of these tips for speeding up your Python learning.
Go Forth & Learn!
All the Best.