You’ve spent hours studying Python, and you may even have several successful projects in your portfolio. But do you write your Python code like a pro? Let’s review some important guidelines to help you clean up your code. What Is the Pythonic Way of Writing Code? There are often several ways to do something in Python; naturally, some are better than others. But you should always prefer code that is not only syntactically correct but also in alignment with coding best practices and the way the language ought to be used.
Online Python courses help you acquire basic knowledge of working with Python. But how do you retain what you’ve learned and start writing Python code on your own? Nowadays, there are plenty of e-learning platforms for programming languages like Python. With these platforms, you can learn the fundamentals of the Python language: syntax, basic functions, and programming best practices. On platforms like Vertabelo Academy, for example, you don’t need others tools to work through the content and can get your hands dirty with a language in a sandbox environment.
If you have just started learning Python, now is a great time to develop your skills further. Nowadays it is not problem to find resources about Python on the internet, however, it can be difficult to find good materials to read for beginners. Therefore I decided to gather and share with you my “Lucky thirteen” articles on Python written in 2018. Top 3 Vertabelo Academy Python Articles for Beginners The Vertabelo Academy is an online learning platform that delivers courses on SQL, data science, and Python.
Python is a simple yet powerful programming language that’s a must for beginners and advanced programmers alike. Here’s why. High-level programming languages have one goal in mind: to make your life as a programmer easier. Messy syntax and obscure keywords? Forget about it. With languages like Python, you can get away with understanding just the basics of programming, enough to begin writing your own scripts and apps. And since Python developers are high in demand, Python is a great language to learn if you want to pursue a career in software development or big data.
In this comprehensive beginner’s guide, we’ll look at how to install Python on three major operating systems, choose a Python IDE, and run your code. Would you like to start coding in Python but don’t know where to begin? Maybe you’ve graduated from an online course like Python Basics and now are looking to continue your Python adventure on your own machine. But first, why Python? The answer is simple: Python is a very easy-to-learn and powerful programming language.
An increasing number of fintech companies are using Python for data analysis. But what makes Python so special? And why is it a better language for data analysis compared to traditional software? Python is quickly becoming the most popular coding language in the world. Currently, it’s perching comfortably in the fourth spot after Java, C, and C++ on the Tiobe Index of Language Popularity. And the Popularity of Programming Language Index ranks Python as the most popular programming language in the world in October 2018.
Python is a programming language frequently used by scientists and data analysts to build applications. Why? Because it’s easy to use and has few rules. But simply installing Python isn’t enough—you also need a good interactive development environment (IDE) to program in. So what are the best Python IDEs for data science? Let’s find out! (Note: all IDEs presented here support Windows, macOS, and Linux.) 1. Enthought Canopy Enthought Canopy is one of the best Python IDEs for scientists and engineers.
Heat maps are a great way to visualize patterns in data, but some businesses avoid them because creating them seems challenging and time consuming. Well, it’s not. Do you know what the most popular programming language currently is? According to the PYPL Index, it’s—you guessed it—Python. And our serpentine friend was also crowned the best programming language in 2018 by Linux Journal readers. Why all the buzz? Because Python is simple and easy to learn.
Information technology is one of the hottest industries in the world and offers thousands of job in each major city around the globe. If you’re a student or professional who wants to get IT skills and find your first job in the industry, Vertabelo Academy can help you get started. The range of IT jobs available is stunning—from software engineers to system administrators and data scientists, IT rules the job market.
The Vertabelo Academy Team has been working hard to release a brand-new course, and it’s finally here! Python Basics for Programming is a great place to start for anyone aspiring to become a software developer. Since our SQL courses, the Vertabelo Academy platform has been embraced by thousands of students who are eager to learn new technologies. After releasing Intro to Python for Data Science last month, we asked ourselves: why not create another introductory Python course, but this time from a software developer’s perspective?
One of the oldest jokes in the business world goes like this: The CFO asked the CEO, “What happens if we invest in developing people and they leave us?” The CEO answered, “What happens if we don’t and they stay?” If you’re like the CEO and want to help your employees grow, this article will explain how you can do so with Vertabelo Academy. Vertabelo Academy has been around since 2015.
Over the past three months, we’ve been working on something completely new. Please welcome our new course on Python data analysis! We got many emails from users like you with good feedback on our Introduction to R course. So first, I want to start off with a big thank you—reading your wonderful comments was like a burst of energy! We’re always looking to improve our offerings, and we greatly value your input.
R and Python are two of the most popular data science languages, but which one is better? And will Python replace R in the near future? Let’s find out! R vs. Python: the Basics First, some history. R first appeared in 1990; it was derived from the language S, a statistical programming language developed for statisticians. It was (and still is) commonly used in educational settings and is a favorite among biostatisticians.