Data Science with Python Training in Toronto

Why Python is the Best

Python has long been known as a simple programming language to pick up, from a syntax point of view, anyway. Python also has an active community with a vast selection of libraries and resources. You have a programming platform that makes sense to use with emerging technologies like machine learning and data science.Professionals working with data science applications don’t want to be bogged down with complicated programming requirements. They want to use programming languages like Python and Ruby to perform tasks hassle-free.

• Ruby is excellent for performing tasks such as data cleaning and data munging, along with other data pre-processing tasks. However, it doesn’t feature as many machine learning libraries as Python.
• This gives Python the edge when it comes to data science and machine learning.
• Python also enables developers to roll out programs and get prototypes running, making the development process much faster. Once a project is on its way to becoming an analytical tool or application, it can be ported to more sophisticated languages such as Java or C if necessary.
• Newer data scientists gravitate toward Python because of its ease of use, which makes it accessible. So popular in fact, a staggering 48 percent of data scientists with five or fewer years experience rated Python their preferred programming language.
• This number tapers off as the experience level increases and the analytics become more intensive. Python has proven itself to be an excellent starting point for data scientists.

Why is Python for data science preferred

• In many scenarios, Python is the programming language of choice for the daily tasks that data scientists tackle, and is one of the top data science tools used across industries.
• For data scientists who need to incorporate statistical code into production databases or integrate data with web-based applications, Python is often the ideal choice. It is also ideal for implementing algorithms, which is something that data scientists need to do often.
• There are also Python packages that are specifically tailored for certain functions, including pandas, NumPy, and SciPy. Data scientists working on various machine learning tasks find that Python’s scikit-learn is a useful and valuable tool.
• Matplotlib, another one of Python’s packages, is also a perfect solution for data science projects that require graphics and other visuals.
• It is called ‘Pythonic’ when the code is written in a fluent and natural style. Apart from that, Python is also known for other features that have captured the imaginations of data science community.

Why is Python important for Data Analysis?

It's Flexible

If you want to try something creative that’s never done before; then Python is perfect for you. It’s ideal for developers who want to script applications and websites.

It's Easy to Learn

Focus on simplicity and readability, it boasts a gradual and relatively low learning curve. This ease of learning makes Python an ideal tool for beginning programmers. Python offers programmers the advantage of using fewer lines of code to accomplish tasks than one needs when using older languages. In other words, you spend more time playing with it and less time dealing with code.

It's Open Source

Python is open-source, which means it’s free and uses a community-based model for development. Python is designed to run on Windows and Linux environments. Also, it can easily be ported to multiple platforms. There are many open-source Python libraries such as Data manipulation, Data Visualization, Statistics, Mathematics, Machine Learning, and Natural Language Processing, to name just a few (though see below for more about this).

It’s Well-Supported

Anything that can go wrong will go wrong, and if you're using something that you didn’t need to pay for, getting help can be quite a challenge. Fortunately, Python has a large following and is heavily used in academic and industrial circles, which means that there are plenty of useful analytics libraries available. Python users needing help can always turn to Stack Overflow, mailing lists, and user-contributed code and documentation. And the more popular Python becomes, the more users will contribute information on their user experience, and that means more support material is available at no cost. This creates a self-perpetuating spiral of acceptance by a growing number of data analysts and data scientists. No wonder Python’s popularity is increasing!