DEV Community: Education Ecosystem

Transforming from a beginner to an expert in Python/ Django: My Journey with Education Ecosystem

Education Ecosystem — Fri, 15 Dec 2023 05:20:03 +0000

I am Liam from Belgium, a graduate in Computer Science from South East Technological University, my journey towards mastering programming languages, particularly Python and Django, has been both challenging and rewarding. My initial fascination was with data science, but I soon realized the power of Python and Django in creating dynamic and powerful web applications.

My discovery of Education Ecosystem marked a turning point in my learning journey. The platform, with its user-friendly interface and well-organized content, made it easy for me to dive into Python and Django. The courses were expertly created by industry professionals, providing me with the latest and most accurate information in these programming languages.

Starting as a beginner in Python and Django, the vastness of these subjects initially seemed overwhelming. However, Education Ecosystem’s diverse range of courses, catering to all skill levels, proved to be a game-changer. The platform offered in-depth video tutorials, practical code examples, and source code, making the learning process both engaging and effective. The hands-on projects were particularly beneficial, allowing me to apply my learning in real-world scenarios. I was eager to learn web framework. I came across such a very well explained tutorial “Python Django Web Framework Tutorial For Beginners”. Similarly, there are many cool projects like How to create a Calorie Counter App using Django and the good thing about Education Ecosystem is you get source code for free.

What really stood out to me about Education Ecosystem was the sense of community it fostered. I had the opportunity to engage with other learners and communicate with instructors through forums and discussion boards. This aspect of community engagement was invaluable, particularly when I encountered challenging topics.

As I progressed through the courses, I noticed a remarkable improvement in my proficiency with Python and Django. The curriculum was very thoughtfully designed, introducing increasingly complex concepts in a manageable way. This approach was crucial in developing and strengthening my skills. In a short period, I transitioned from a beginner to a proficient developer, gaining confidence in web application development using Python and Django. Education Ecosystem’s comprehensive approach not only enhanced my technical skills but also prepared me to contribute meaningfully in the field of web development.

My transformation into a skilled Python and Django developer, thanks to Education Ecosystem, was remarkable. The platform’s user-friendly interface, top-quality content, and practical approach played a pivotal role in my journey.

In conclusion, my experience with Education Ecosystem has been nothing short of transformative. As a Computer Science graduate, the platform enabled me to master Python and Django, instilling a strong sense of confidence in my capabilities. I believe Education Ecosystem is an invaluable resource for anyone aspiring to learn or enhance their skills in programming languages, especially Python and Django, and realize their full potential in the field of web development.

How to use ChatGPT to create a web scraping tool in Python

Education Ecosystem — Mon, 27 Feb 2023 11:52:45 +0000

Introduction

Web scraping is the process of automatically gathering data from websites using software or scripts. The data can be text, links, images, and other information available on the site. This process is also sometimes referred to as web data extraction, web harvesting or web crawling. The most popular programming language used for creating web scraping scripts is Python. In Python, there are many libraries for web scraping, for example Beautiful Soup, Selenium, and Requests. Depending on the type of scrapping script you are trying to build, some libraries might be better suited.

ChatGPT is a large language model developed by OpenAI that has the ability to generate human-like text. As far as programming goes, ChatGPT has many use cases, including the ability to write simple scripts, such as web scraping scripts. Other use cases include checking code and creating simple static pages written in HTML and CSS. In this article, we are going to see how you can build a web scraping script in Python using ChatGPT.

Example #1: Web scraping for price monitoring

In the first example, we will use Beautiful Soup to create a simple web scraping script for monitoring the price of the Asus GeForce RTX 3060 listed on Newegg. Here is one example of a prompt you can write on the chat box of ChatGPT:

write a price monitoring script in python beautifulsoup for https://www.newegg.com/asus-geforce-rtx-3060-ti-dual-rtx3060ti-o8g-v2/p/N82E16814126521. Check every 5 minutes.

Here is the code snippet generated by ChatGPT:

import requests
from bs4 import BeautifulSoup
import time

url = 'https://www.newegg.com/asus-geforce-rtx-3060-ti-dual-rtx3060ti-o8g-v2/p/N82E16814126521'
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3'}

def check_price():
    page = requests.get(url, headers=headers)
    soup = BeautifulSoup(page.content, 'html.parser')
    price = soup.find('li', {'class': 'price-current'}).strong.text
    price = float(price.replace(',', ''))
    return price

while True:
    price = check_price()
    print(f'Current price: ${price:.2f}')
    time.sleep(300)  # wait 5 minutes before checking again

Here is the result when the code is run:

You can also change the requirements as you like depending on how you want to monitor it. For example, you can change the time frequency, or check if it has dropped below a certain range. In addition, you can also get notified via slack chatbot.

It's important to note that ChatGPT may not always generate fully functional code. In fact, when we initially asked ChatGPT to create this script, it produced non-functioning code that required troubleshooting. We analyzed the code to identify the problem, made necessary modifications to the prompt, and then re-ran ChatGPT to generate a working script. It's important to always review and test the code generated by ChatGPT, and make any necessary modifications to ensure it works correctly for your specific use case.

Example #2: write a web scraping for data mining

In this second example, we will ask chatGPT to scrape the land area of a city in the US:

write a web scraper script in python that gathers the land area listed here https://worldpopulationreview.com/us-cities/seattle-wa-population

Here is the code snippet generated by ChatGPT:

import requests
from bs4 import BeautifulSoup

# specify the url of the website
url = "https://worldpopulationreview.com/us-cities/seattle-wa-population"

# send an HTTP request to the website and get its HTML content
response = requests.get(url)
html_content = response.content

# create a Beautiful Soup object from the HTML content
soup = BeautifulSoup(html_content, "html.parser")

# find the element that contains the land area information
land_area_element = soup.find("div", {"class": "jsx-1487038792 col-md-6 col-lg-4"})

# extract the land area value from the element
land_area_value = land_area_element.find("span").text.strip()

# print the land area value
print("Seattle's land area is:", land_area_value)

Here is the result when the code is run:

Similarly, you can also specify your requirements in the prompt. You can also specify it to use a different library to scrape data, like Selenium. Data mining is usually done to obtain data needed for data analysis.

ChatGPT is a great tool to write simple scripts that gather data, but this might not be the case if your requirements are complex, like handling errors. This is because ChatGPT is not specifically designed for web scraping. In addition to that, it does not have direct access to the web and cannot directly extract data from websites.

Conclusion

We have now seen 2 examples of how ChatGPT can be used to create web scraping scripts in Python using Beautiful Soup. This is just one use case of ChatGPT. You can also use it to help you debug code or create test cases. It can also assist you in providing guidance on programming concepts, syntax, and algorithms. Additionally, it can help with brainstorming ideas for the types of data that can be scraped and how to structure the code. Overall, while ChatGPT can be a helpful tool for writing web scraping scripts, it is important to use it in conjunction with other programming tools and resources specifically designed for web scraping.

How to Deploy Django in Production With Nginx and Gunicorn

Education Ecosystem — Fri, 27 Aug 2021 18:13:45 +0000

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. However, Django, an inbuilt development server, is not suitable for deploying your Django application since it isn’t scalable enough for production use.

NGINX is open-source software for web serving, reverse proxying, caching, load balancing, media streaming, and more. NGINX is the best-in-class load‑balancing solution used by high‑traffic websites such as Dropbox, Netflix.

Gunicorn, coined from ‘Green Unicorn’, is a Python HTTP Server for UNIX. Gunicorn interfaces with both Nginx and your actual python web-app code to serve dynamic content, as Dev Zero demonstrates in his project on Education Ecosystem.

This article will explain how to deploy our Django web application via Nginx and Gunicorn.
Setting your Django project.
Once your code has moved to the server, make sure all required installations are present on the server.

This code will install Python, pip, and Nginx.

sudo apt install python3-pip python3-dev nginx

Next, install all the packages required by your application in your virtual environment next. Install gunicorn with pip.

Pip install gunicorn

Then, open the port where we want to run the application.

sudo ufw allow 8100

Configuring Gunicorn
A socket is a special file for interprocess communication, which enables two processes to communicate. You now need to create a socket file for gunicorn.

Use the following command to create your file;

sudo vim /etc/systemd/system/gunicorn.socket

Create a service file for Gunicorn and specify a user name, the working directory for our project, the virtual environment for our project, and a sock file. Gunicorn is the Django web server that needs to know the user. Gunicorn needs to know the working directory for the project and to know the virtual environment folder. You also need to create a sock file for the project. The sock file communicates with Nginx, and they are both able to communicate through this socket.

sudo vim /etc/systemd/system/gunicorn.service

Enable the Gunicorn socket using this command;

sudo systemctl start gunicorn.socket
sudo systemctl enable gunicorn.socket

Configuring Nginx
Now you need to create the configuration file for Nginx, using the following command;

sudo vim /etc/nginx/sites-available/Lorem_Test_Project

Activate your configuration, then finish by a restart of Nginx.

sudo ln -s /etc/nginx/sites-available/Lorem_Test_Project /etc/nginx/sites-enabled/
sudo systemctl restart nginx

Your application is up and running!

Summary
Running a local Django server is not recommended in production because it’s just a test server, not a production-ready server.

This article is a guide to show you how to run Django in production with Gunicorn. It uses Nginx as the reverse proxy to make your application more secure.

What is the Most Popular Programming Language in Each US State?

Education Ecosystem — Fri, 15 May 2020 19:13:25 +0000

Is your favorite programming language the most popular one in your state? Find out.

There is no universal programming language that can be used to develop all kinds of applications. Each programming language has its own pros and cons that make it either a good or bad choice for a particular application. However, some developers swear by a specific language. It is their bread and butter for writing new applications. And this belief may not be unique to individual developers, but to programmers in the same region.

According to Wikipedia, there are more than 700 programming languages. The US is made up of 50 states, five major territories, and several minor islands. This data-driven article gives you insights into the most popular programming language based on each US state. The data used for this article was compiled by Education Ecosystem after surveying 15,000 US-based users on its platform.

Popular programming languages in different US states
The most popular languages in the US, based on the survey, are C, C#, C++, Delphi/Object Pascal, Java, JavaScript, PHP, Python, R, Ruby, Swift, and Visual Basic.NET. Here is a breakdown of the most popular programming languages in different US states.

*C is popular in Nebraska, Iowa, Arizona, and Rhode Island
*C# has a foothold in Maine, Connecticut, New Mexico, and Texas
*C++ has a huge ‘programmer-fan base’ in South Carolina, Nevada, Kansas, and Arkansas
*Delphi/Object Pascal takes the cake in Idaho, Mississippi, Oklahoma, and West Virginia
*Java is popular in Alaska, Montana, Pennsylvania, and Indiana
*JavaScript is popular in more states than any other language. It is popular in *California, Illinois, Missouri, Alabama, Wyoming, Oregon, and Wisconsin
*PHP is mainly used by programmers in South Dakota, New Hampshire, and North Dakota
*Python is popular in Tennessee, Minnesota, New Jersey, Colorado, and Louisiana
*R is preferred in the states of Hawaii, Washington, and Ohio
*Ruby has its mark in Massachusetts, North Carolina, Florida, and Vermont
*Swift is popular in North Dakota, Georgia, Michigan, and Virginia
*Visual Basic.NET’s popularity lies in Utah, Maryland, New York, and Delaware

What determines the popularity of a programming language?
Perhaps what is even more interesting in this survey are the factors that determine the popularity of programming languages. Open-source programming languages are still popular. A good example is Java whose usage has been on the rise since 2008. Java is preferred when developing large scale software projects and Android Apps.

JavaScript has grown in popularity with the survey showing that it is favored in California, the tech capital of the United States. Its popularity can be traced to many factors. It is compatible with HTML and CSS. Beginner programmers argue that JavaScript is easy to learn, and its large collection of learning material is a major bonus point. JavaScript can be used for websites, mobile apps, and games.

Ruby, the dethroned king of programming languages, is considered an easy language to learn. The demand for Ruby programmers has increased resulting in a spike in its popularity. Its functions are said to be concise when compared with other verbose languages. C is a popular programming language because it allows programmers to work closely with the operating system. It has good flexibility in memory management. C is mainly used in language compilers, language interpreters, operating systems, and system utilities.

C# is popular among programmers working in the game development sector. It is mainly used for the creation of games using the Unity game engine, which is one of the most widely used game engines in existence. C++ is preferred by many programmers because it is faster and has better memory execution when compared with other languages.

Object Pascal is popular despite being an old programming language. It offers all the tools necessary for Object-Oriented Programming (OOP) although not on the same level as languages like Ruby or Smalltalk. It is fast, modular, verbose, and has good documentation and community.

PHP is a scripting language mainly used for creating websites. It is simple, open-source (free), has great documentation, works well with Content Management System (CMS), flexible, scalable, has a huge community and big standard library, exceptional performance, and accepts platform diversity.

Python is popular because it is productive, reliable, accessible, efficient and has amazing libraries, active community, corporate sponsors. R is the language of big data science. It is an open-source language and can lead programmers to good careers.

Swift was created in 2014 by Apple. This means it has the backing of one of the largest companies in the world. It is open-source (free), fast, and safe. Microsoft’s Visual Basic.NET is popular among developers working in office environments.

Education Ecosystem to Distribute $1 Million Grants to Developers

Education Ecosystem — Wed, 06 May 2020 19:37:39 +0000

Education Ecosystem has announced the allocation of $1million in grant funding to developers. Education Ecosystem (LEDU) is a project-based learning platform that teaches professional developers and students how to build real products in areas such as programming, game development, artificial intelligence, cybersecurity, data science, and blockchain.

The grant funding was unveiled to provide developers on Education Ecosystem’s network with the necessary resources to create new projects for the network viewers. The funding will be split across the categories mentioned above.

“We believe that the grant funding is the best way for us to support and reach our goal of becoming the largest project-based online learning platform in the world,” said Education Ecosystem Manager Dusan Kolic “This funding is available to all developers who have the skills to create high-quality projects for our millions of worldwide viewers.

The grant will be allocated to the six areas that Education Ecosystem caters to – artificial intelligence, blockchain, cybersecurity, data science, game development, and programming – over a three year period. The funding will be divided equally per category and the number of recipients per category will be determined by demand. The funding rounds will be held as follows:

· 2020 – ⅓ of the fund will be allocated to all six categories

· 2021 – ⅓ of the fund will be allocated to the six categories

· 2022 – ⅓ of the fund will be allocated to the top 3 categories (based on interest from the grant applications).

Since the launch of Education Ecosystem’s alpha product in 2015, the platform has been used by more than 1 million people from at least 194 countries. There are more than 200,000 pieces of video content on the platform created by at least 13,000 project creators. This funding will see more projects added to Education Ecosystem’s platform.

The company will use a transparent and tried-and-tested system to determine who qualifies for the grant. As the platform is meant to benefit tech intermediates and experts, the funding will go to experienced developers with a proven track record in their areas of expertise. This is to ensure that only high-quality projects will be created with the aim of providing value to the viewers. Applicants have up to six months to prove to Education Ecosystem that they have the necessary experience and will be committed to this project for the duration of the funding. Applicants interested in receiving the grants should fill out this typeform.

Media Contact:

Brianna Weth

London, PR Agency

Email: content@education-ecosystem.com