Author: Kateryna Koidan

Want to become proficient with Python operators? In this comprehensive guide, you’ll learn everything you need to know about the types of operators in Python, their differences, and syntax and explore cases with easy-to-follow code examples. What Are Operators in Python? Operators are special symbols used to perform different operations on variables and values. The variable or value that the operator operates on is called the operand. In Python, operators are divided into the following groups:

Wondering how to get the most out of Python dictionaries? In this comprehensive guide, we’ll cover all you need to know to effectively use dictionaries in your studies and in real-world projects. What Is a Python Dictionary? A Python dictionary is yet another very useful and important data type. Basically, Python dictionaries are an unordered collection of data values. However, in contrast to other data types, a dictionary’s elements are key-value pairs instead of single data values.

Both lists and arrays are used to store data in Python. Moreover, both data structures allow indexing, slicing, and iterating. So what’s the difference between an array and a list in Python? In this article, we’ll explain in detail when to use a Python array vs. a list. Python has lots of different data structures with different features and functions. Its built-in data structures include lists, tuples, sets, and dictionaries.

Let’s clarify first. This article is not about snakes, it’s about coding in the fastest-growing programming language. That language happens to be named Python. In this article, we’ll go through a number of inspiring ways to use Python. So, what is Python used for? Why Code in Python? You have probably heard about many different programming languages, including JavaScript, Java, C++, Ruby, and others. Why should you choose Python?

In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if the available data is scarce. Big data and data science are concepts often heard together. It is believed that nowadays there are large amounts of data and that data science can draw valuable insights from all these terabytes of information.

There is so much hype around the data scientist role these days that when a company needs a specialist to get some insights from data, the first idea is to look for a data scientist. But is it really the best option? Let's see how the roles of data scientists and data analysts differ and why you may want to hire an analyst before any other role. Data Scientist or Data Analyst?

Are you a geek? Today being a geek means being smart and passionate about certain complicated subjects like mathematics, programming, science fiction films, and fantasy books. That sounds cool. So why not join the celebration of Geek Pride Day on May 25th? What’s a Geek? Let’s first define who can be called a geek. According to Wikipedia, this term was originally used to describe "eccentric or non-mainstream people". Today the word typically refers to an expert or a person who is passionate about some hobby or intellectual pursuit.

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.

It looks like everybody in the tech world is using GitHub. Wondering if you need it too? Let’s have a brief beginner-friendly overview to see what Git and GitHub are and how you can start working with them right away. What is Git? We are going to start with defining what Git is and how you can benefit from it. Git is an open-source distributed version control system. What does this mean in simple terms?

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.

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:

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.

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.

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.

Looking for a data science job? Then you’ve probably noticed that most positions require applicants to have some level of Python programming skills. But how are they going to test this? What are they going to ask? Let’s prepare you for some interview questions! Why Do Data Scientists Need Python? Data science goes beyond simple data analysis and requires that you be able to work with more advanced tools. Thus, if you work with big data and need to perform complex computations or create aesthetically pleasing and interactive plots, Python is one of the most efficient solutions out there.

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.