Python is one of the fastest growing programming languages in the world. It is becoming equally popular among novice and experienced programmers and taking over rival languages such as C++ and Java. One of the biggest reasons why Python's popularity has skyrocketed is its simplicity and flexibility. Even people with no prior programming knowledge can learn Python within weeks. In fact, Python has all the characteristics to become the lingua franca of coding in the near future.
Providing various types of data in a unified way is one of the core concepts of web communication. In recent years, JSON text files have become extremely popular due to their simplicity and universality. Vertabelo Academy’s recent course—How to Read and Write JSON Files in Python—explains how to manipulate JSON files in Python. If you work with Python and want to learn a practical skill in high demand, you’re in the right place.
Do you know the feeling when you’ve read the theory behind some programming concepts but you’re at a loss when it comes to writing actual code? Vertabelo Academy’s new course—Python Data Structures in Practice—addresses this exact problem by explaining the practical aspects of basic data structures. The course discusses frequent use cases and teaches you optimal solutions to common problems. As a bonus, you’ll create a real Python game!
Python can help you get more insights from your data, make better-informed data-driven decisions, automate many routine activities, and increase the ROI from your marketing campaigns. Interested? Then let’s see how exactly Python can boost your marketing efforts. How Can Your Marketing Activities Benefit from Python? As a marketer, you probably face a number of challenges: Ads are getting more expensive. Previously profitable marketing channels, like content marketing, are becoming crowded.
If you have basic knowledge about Python and are looking for an IDE to work with on your own computer, consider the Jupyter Notebook. It is a good tool, especially for Data Science projects. What is Jupyter Notebook? Jupyter Notebook is an open-source server-client application used to create and run mainly Data Science projects. Where does its name come from? JUPYTER is an acronym of Julia, Python, and R, because they were the first programming languages supported by this editor.
The PyCharm IDE is one of the most popular editors used by professional Python developers and programmers. But if you are a beginner, don’t worry, it can help you too! Find out how to create Python projects using PyCharm and what basic features can help you write code more efficiently. The PyCharm IDE is one of the most popular editors used by professional Python developers and programmers.
Python was my love at first sight. It was at the very beginning of my interest in programming when I took my first Python course, and I instantly fell in love with this language. Unfortunately, I had few opportunities to work professionally in the Python environment, but I kept learning after hours, just for fun, until my new job became too demanding and I simply had no more time for my coding hobby.
This tutorial will show you how to install the PyCharm IDE on your own computer–whether you have Windows or Ubuntu. To start developing with the PyCharm IDE, you need to download and install it on your computer’s operating system. PyCharm is an editor developed by the JetBrains company. As of today, it is the most popular IDE used by Python developers. Knowing this, JetBrains delivers PyCharm in three versions: Professional (full-featured IDE), Community (lightweight IDE, not full-featured), and Educational (community IDE with added education features).
Ever wondered how you can use Python to create stunning data visualizations? In the first part of this series, we saw how to draw line plots and histograms using the matplotlib library. We also saw how to change the default size of a plot and how to add titles, axes, and legends to a plot. In this article, we’ll see a few more types of plots that can be drawn using the matplot library with pro tips on how to improve them.
At some point in your Python journey, you’ll definitely need to work with dates, times, or both. Learn all the basics of date and time in Python with this short tutorial. Need to calculate how long it’s been since a given date? Working with date and time objects in Python? In this beginner’s guide, we’ll take a look at how to write programs with these two key data types in Python.
Want to know how Python is used for plotting and data visualization? Interested in learning one of the most commonly used data visualization libraries in Python? If so, you’re in the right place. In this installment of a two-part tutorial, we’ll learn how to use matplotlib, one of the most commonly used data visualization libraries in Python. Over the course of both articles, we’ll create different types of graphs, including:
Even if you’re just starting your Python journey, you’ve most likely come across Python sets. But do you know how to use them effectively? This article aims to cover all the main Python set operations to give you a better feel for when and how to use this important data structure. What Are Python Sets? First, let’s start with the basics. A set is a built-in type in Python that has a number of important characteristics:
Python is one of the most frequently used programming languages for financial data analysis, with plenty of useful libraries and built-in functionality. In this article, you’ll see how Python’s machine learning libraries can be used for customer churn prediction. Customer churn is a financial term that refers to the loss of a client or customer—that is, when a customer ceases to interact with a company or business. Similarly, the churn rate is the rate at which customers or clients are leaving a company within a specific period of time.
You already have some foundational knowledge of Python for data science. But do you write your code efficiently? Check out these tips and tricks to supercharge your Python skills. How to Write Efficient Python Code In this article, we’ll take a look at some tricks that will help you write fast and efficient Python code. I’ll start with how to optimize code that involves the pandas library. If you want to refresh your knowledge of pandas, check out our Introduction to Python for Data Science course.
Another Python course is now available on Vertabelo Academy. “How to Read and Write CSV Files in Python” is an online course for anyone interested in working with files in Python—a must-have skill for any beginner software developer or data scientist! After the great success of our SQL courses, we began working hard on the Python learning path. So far, we’ve made five Python courses available, and we’re proud to announce the release of yet another one: How to Read and Write CSV Files in Python!
How can Python’s pandas library be used to analyze time series data? Let’s find out. The pandas library is frequently used to import, manage, and analyze datasets in a variety of formats. In this article, we’ll use it to analyze Microsoft’s stock prices for previous years. We’ll also see how to perform basic tasks, such as time resampling and time shifting, with pandas. What is Time Series Data? Time series data contains values dependent on some sort of time unit.
Regression analysis is one of the most fundamental tasks in data-oriented industries. In simple words, it involves finding a relationship between independent and dependent variables (attributes) in a given dataset. Consider the example of a house price prediction problem—given the size and number of bedrooms, we want to predict the price of a house. This is a simple regression problem where the size of the house and the number of bedrooms are the independent variables and the price of the house is the dependent variable.
Want to start learning Python but struggling to find quality resources? Well, they're out there—and we'll review them here. In no particular order, here are the best online resources to learn Python. Courses 1. Vertabelo Academy Vertabelo Academy is an interactive e-learning platform that offers six Python courses: Python Basics Part 1. This is the first of a two-part course that covers everything you need to know about Python so you can start writing your own scripts and applications.
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.
Looking for some advice to build a data science portfolio that will put you ahead of other aspiring data scientists? Don’t miss these useful tips. Why Have a Portfolio at All? Even though the demand for data scientists is high, the competition for entry-level positions in this field is tough. It should come as no surprise that companies prefer to hire people with at least some real-world experience in data science.
When you already have some experience with Python, building your own portfolio of data science projects is the best way to showcase your skills to potential employers. But where do you begin with developing your very first Python project? First, Why Develop a Data Science Project? There are a number of career development benefits to creating your own data science project in a language such as Python: Studying.
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.
Brush up on your data science and SQL skills with Vertabelo Academy’s interactive courses. Why Vertabelo Academy? You get instant access to lessonsthat teach various concepts of SQL, data science, and programming in R (soon also in Python!). Our courses are appropriate for people who have no prior knowledge of computer science or programming. The only requirement is a web browser. No need to install databases, download example tables, or spend time inventing exercises for yourself.
Data science is hot right now. If you want to learn more about it, where should you go? Online, of course! Check out our favorite data science sites. Whether you’re a beginner or a pro, these are sites you should know. Not so long ago, if you wanted information on a topic like data science, you had to look for it – either at your local library or at a university.