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My name is Frank Andrade. I studied engineering and specialized in Data Science. I’m currently navigating the world of freelancing by writing articles about Data Science on Medium and making videos on my YouTube channels Lean Languages with TV and Python en Español. I believe it’s a great idea to apply the knowledge you have in different fields you love. …
As a data scientist, you need to have a portfolio website that helps you showcase your projects and profile in one place. You might already have a Github and LinkedIn page, but don’t expect a potential employer to look through all your code and posts to know more about you.
Building a portfolio website can be as easy as using a WordPress or GitHub template; however, creating a website on your own will help you add more customization while learning new things you can do in Python.
Although building a website usually requires knowledge beyond Python, we don’t need to…
NumPy is a Python library on which most data science packages such as SciPy (Scientific Python), Matplotlib, and Scikit-learn depends to some extent. It adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.
Without a doubt, Numpy is a library that you should learn if you are a data science enthusiast. This is why, in this guide, I’ll show you all the things you can do with the Numpy library.
Below, you’ll find the topics covered in this guide:
Table of Contents
1. How to Create an Array…
If you’ve been studying data science for a while, you might know that in order to learn data science you need to learn math, statistics, and programming. This is a good start for anyone interested in data science, but do you know how to get even more exposure to data science?
It’s with projects! A project will help you put into practice all the knowledge you’ve acquired from math, statistics, and programming. So far you might’ve seen each of them individually, but after you finish a project, the concepts you’ve learned in each field will make more sense.
Data is everywhere. Every website you visit contains some data displayed in a readable format that you could use for a project. Although you could easily copy and paste that data, when it comes to large data, web scraping is the best solution.
Learning web scraping might be challenging at the beginning, but if you start with the right web scraping library, things will get a lot easier. This is why in this step-by-step guide, I’ll show you how to scrape multiple pages of a website using Python’s easiest web scraping library, Beautiful Soup.
This guide will be split into…
Think of all the things you do during the day. You might read the news, send emails, find the best deal for a product, or search jobs online. Most of these tasks can be automated with web scraping, so instead of spending hours looking through websites, a computer would do it for you in a couple of minutes.
Web Scraping is the process of extracting data from a website. …
One of the coolest things that you can do in Python without installing any third-party library is to perform file system operations such as creating a folder, renaming a file, and working with directories. Although these tasks can be easily done manually, you can automate them with Python code to save some time.
In this article, we’ll see 7 file system operations you can do in Python with the os and Pathlib modules. Each operation includes practical examples so you can understand the difference between these 2 modules.
Knowing the current working directory is fundamental when dealing with paths in…
Learning Python can be difficult. You might spend a lot of time watching videos and reading books; however, if you can’t put all the concepts learned into practice, that time will be wasted.
This is why you should get your hands dirty with Python projects. A project will help you bring together everything you’ve learned, stay motivated, build a portfolio and come up with ways of approaching problems and solving them with code.
In this article, I listed some projects that helped me level up my Python code and hopefully will help you too. The projects are listed by difficulty…
Natural Language Processing (NLP) is focused on enabling computers to understand and process human languages. Computers are great at working with structured data like spreadsheets; however, much information we write or speak is unstructured.
The goal of NLP is to make computers understand unstructured texts and retrieve meaningful pieces of information from it. We can implement many NLP techniques with just a few lines of code of Python thanks to open-source libraries such as spaCy and NLTK.
In this article, we’ll learn the core concepts of 7 NLP techniques and how to easily implement them in Python.
Jupyter Lab is known as Jupyter’s next-generation notebook interface. It integrates the notebook, console, text editor, and terminal into a single interactive and collaborative environment.
Apart from bringing the classical notebooks and text editor, Jupyter Lab also offers third-party extensions. As we’ve seen in a previous article, Jupyter Notebook only provides a limited number of native extensions; however, Jupyter Lab contains a robust and thriving third-party extension community. In this guide, I’ll show you 5 extensions that will make you think about switching to Jupyter Lab.
In case you’ve been using only Jupyter Notebooks and never heard of Jupyter Lab…