Coursearch 💻 📝 📚

[]() []() []() []() []() []() []() []()

A one stop solution to navigate the endless sea of online courses.

This is our submission for the HackChennai Hackathon 2020. It was made under 36 hours with no prior preparation.

The problem:

• In the past few years, the popularity of MOOC platforms has skyrocketed, prompting many other such platforms to crop up.
• However, not many users are satisfied with all the courses present.
• Sometimes the level of difficulty is too high, or the hands-on labs are outdated, or the instructor hides behind technical jargon, or the videos lack contextual clarity and so on.
• With so many courses coming up, it is essential for the learner to save time and money by choosing only the best course available.

The solution:

• Why not let the user select something they want to learn and leave the choosing courses to us?
• Why not gather data from multiple MOOC platforms?
• Why not use this data of ratings and reviews to rank courses with a unique method?
• Why not let the user view this combined information in one place where they can easily search, sort and visit these courses?

Features:

• Crawls information from Coursera, Udemy, Pluralsight and Udacity (in progress) to gather MOOC data for any search term.
• Ranks the courses using a weighted average of their number of reviews and the average rating (out of five.)
• Allows the user to further filter the results on any basis they want: difficulty, ratings, intructor, etc.
• Additional search option is provided in the table too.
• Users can find direct links to any MOOC.
• The /api endpoint can be used by anyone to query data using parameters defined in the documentation.

Tech stack:

• flask: web framework.
• python: backend routing and web crawlers.
• jinja2: templating and rendering frontend.
• scrapy, selenium and beautifulsoup: data mining.
• crochet: running the reactor for consecutive searches.
• pandas and json: formatting and cleaning the dataframe.
• Material Design and Bootstrap: styling the frontend.

Deployment:

The live project is deployed on https://coursearch.herokuapp.com/. You may use it to search MOOCS, use the API for your projects or run it locally using the instructions below.

Local installation:

You must have Python 3.6 or higher to run the file.

• Create a new virtual environment for running the application. You can follow the instructions here.
• Navigate to the virtual environment and activate it.
• Install the dependancies using pip install -r requirements.txt
• Run the app.py file with python app.py

Note: to run it locally, you must have a version of chromedriver.exe that matches the one installed on your device.

Future scope:

• Fix the Udacity web crawler so that it works.
• Convert the website into a PWA.
• Explore and mine data from other platforms too.

• Add a variety of parameters to the /api endpoint. We are open to enhancement and bug fixes: fork and clone this repository, submit a pull request and we'll test your changes before merging them!

  Made with ❤️ by Team Infinity.