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.

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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• Sometimes the level of difficulty is too high, or the hands-on labs are outdated, or the instructor hides behind technical jargon, or the videos lack contextual clarity and so on.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.

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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?

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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.

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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.

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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.

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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.

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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!

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Made with ❤️ by Team Infinity.