Why You Should Use Python for Data Science

on September 27, 2019

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What is Python?


If you’ve heard of programming, you’ve probably heard of Python. By name alone it may trigger a fear of a certain snake, but Python is a powerful, general purpose programming language that has become THE language to learn if one wants to get into the world of data science (whereas JavaScript is great for web development). 

This is due to several reasons:

  • Python is beginner friendly. It promotes clean and readable code so beginners can not only learn quickly but read code quickly as well due to its easy-to-understand syntax. Even software like Jupyter Notebook gives immediate feedback on your code so when you run it, the output is shown instantly. And if you have any problems in your code, Jupyter Notebook will even tell you where the error is.
  • There are also a wide variety of libraries to choose from. This is due to the reason above; having an easy-to-learn language promotes a faster learning curve, which leads to having a greater number of Python experts quicker and therefore, having greater potential to have more libraries. And the more Python libraries, the more methods one has access to and therefore, leads to an easier time when coding.
  • These reasons are all supported by the fact that Python has a great community behind it. Any question can be answered by a quick search of your problem on Google with the keyword “Python” at the end of your query. Any difficulty you may have as a beginner has most probably been well-documented and answered online.

learn python


Whilst the online community is extremely dense and active, many of our students prefer to have a more focused learning journey in our offline Python Fundamentals course in Hong Kong.


How can it help with your career?

Data science, the process of extracting meaningful insights from data using a combination of domain knowledge, coding, math and statistics, is also one of the hottest tech trends of recent years and it is not going away anytime soon. This is because more and more companies are looking to analyse and extract insights from the huge amounts of data they currently have. 

We’ve even covered the current in-demand data science trends in Hong Kong.

Since every company has some form of data, you can be a huge asset to your company if you are able to analyse and generate insights from your company’s data. Are most of your colleagues still stuck using old Excel tactics to get things done in days? You can probably impress them (and your boss) by accomplishing these tasks in mere minutes with Python.

If you are passionate about data and are committed enough, acquiring data science skills can also be a great way to make a career change and cash in on the tremendous demand for data scientists/data analysts in Hong Kong. Grinding away in the banking world? You’d be surprised what you can do with Python for Finance.


How can Python be used for data science?

There is no better way to start diving into improving your data science skills than getting better at Python. Once you’ve learned the basics of Python, working with data would be much easier.

Because of its ease of use, you can start working with data quickly instead of being bogged down on little oddities that other programming languages have. Coupled with Python’s vast and powerful array of libraries, you can easily execute complex data science tasks with little effort. If you ever encounter any obstacles, the vibrant Python community is always there to help you out since most data science projects that you can find online are written in Python.

Even if during the process of learning Python that you realize data science isn’t for you, you have still learned something quite valuable as Python is a general-purpose language. You can use Python to create a host of other projects like games, calculators and even a microblog! 

If you find that Python is a powerful tool for you, our data science course in Hong Kong is a great next step that will equip you with even more practical projects and skills you can deploy at work.


python scraper


Here are some examples of what you can do with Python vs what you might be doing now:

  • Data visualization in Excel is suitable for quick, small visualizations but visualizing big data can be a big issue. However, Python can handle huge volumes of data easily and is able to visualize them in a clear, understandable way that fits your data. It’s faster as well.
  • Dealing with missing data in an Excel file is another hassle, having to click and press keys to get it done. Even with that, you must be careful not to damage formulas or ranges. Utilizing the powerful pandas library (Python likes its animals), you can replace all missing values in a dataset in milliseconds with a single line of code. Easy, right?

That was a lot of jabs at Excel. If you still aren’t convinced, here are more reasons why you should use Python instead of Excel. You can even start learning Python now with this helpful guide.


Learning tech skills can seem daunting, but it’s actually very easy to get started. Kickstart your learning and head to Xccelerate to find our data science and Python courses in Hong Kong.