Why is Python Essential for Data Scientists?

Before getting started in the field, one question typically comes up for most budding data scientists: “Which programming language do I need to know the most for my new career?”

Get The Full Series in PDF

Get the entire 10-part series on Charlie Munger in PDF. Save it to your desktop, read it on your tablet, or email to your colleagues.

Q2 2020 hedge fund letters, conferences and more

From Java and Javascript to C++ and R, there are many different programming languages to choose from. However, there is one language that has become the go-to choice and favorite among experts in the industry of data science: Python. With its extensive libraries, tools for simplifying tasks, and a huge community base for getting questions answered, Python has become the top programming language for data scientists and analysts.

Relying On Old-Fashioned Stock Picking, Lee Ainslie Reports His “Strongest Quarter” Ever

Lee Ainslie's Maverick Fund USA enjoyed its "strongest quarter in the fund's history" during the three months to the end of June. According to a copy of the firm's second-quarter letter to investors, which ValueWalk has been able to review, Maverick Fund USA gained 18% in the second quarter. Following this performance, the fund was Read More


What is Python?

Python is a widely used high-level, general-purpose programming language that was initially designed in 1991 by Guido van Rossum and the Python Software Foundation. This language is used for everything from scripting, developing, generation, and software testing. It has a strong focus on code readability while providing programmers with the ability to express concepts in a reduced amount of required code.

This language is applicable to a variety of tasks and areas of the tech world. Python software development is popular among software development companies, such as BairesDev and has become a favorite language among some of the biggest names and organizations in the technology world. The list includes Google, Quora, Dropbox, IBM, Cisco, and Hewlett-Packard.

Python and Data Science

The field of data science is growing every day and gaining even more use cases as technology continues to advance in areas such as artificial intelligence, machine learning, and predictive analysis. It has quickly become one of the most in-demand areas of expertise and a popular career choice.

Data scientists must deal with complex problems on a daily basis and go through the four major steps necessary to solve these problems: data collection and cleaning, data exploration, data modeling, and data visualization. Python provides data scientists with the tools required to effectively carry out the problem-solving process. It also offers libraries that are dedicated to each step of this process.

Data scientists are not dealing with small bits of information either during the problem-solving process. They deal with huge amounts of data, known as Big Data, which require coding and data management capabilities to manipulate and understand.

The Benefits of Python for Data Scientists

Python has become the preferred programming language in the field of data science for a variety of reasons, including the following:

  • Easy to Understand and Use - As most data scientists are not technically developers by trade, they need to work with a programming language that is easy to both understand and use. Python is the ideal choice for this as the learning curve is fast and fairly smooth - especially when compared to other programming languages such as C#, Java, and Ruby.

Even the newest data scientists can easily understand its syntax, which is very simple and also improves readability. The language also provides many data mining tools to help with the handling and simplifying of large sets of complicated data. As Python requires fewer lines of code to accomplish tasks when compared to other languages, it is an ideal tool for simplification of projects for experienced data scientists and an easy way for new data scientists to fully learn the job.

  • Scalability and Flexibility - Unlike other programming languages, Python makes scaling a much easier task than it has been in the past. It does so by providing data scientists with flexibility in their problem solving and providing multiple ways to approach different issues. This flexibility also helps busy data scientists solve end-to-end problems at a much faster rate than they would have been able to do in a more complicated language. Python is flexible enough to help data scientists in nearly all aspects of their jobs as well, including web services, data mining, classification, development of machine learning models, and many other areas.
  • Libraries and Online Communities - One of Python’s biggest pros for data scientists is the enormous amount of resources available for it. The language’s large following and large span of use in both academic and industrial applications mean there are many analytics libraries available online. As it continues to grow even more popular, the number of libraries will only continue to expand.

Python’s popularity is a direct result of its incredible and vast online community. More and more users create additional libraries every day which drives the development of more modern tools and advanced techniques. If there is a problem within this language, chances are that someone else has already encountered it and put advice out there for other data scientists. The community is a tight-knit one and makes it easy for anyone to find the support.

As the field of data science is still fairly new, it will only continue to progress - and Python will progress along with it. This programming language enables both experts and newcomers to achieve more in less time while providing powerful, easy-to-use tools. These characteristics and more have made Python a top choice for data scientists of all skill levels.