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Data Science and Machine Learning - 16 wks
Full-Stack Software Developer - 16w
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You're probably here because you're about to start building your final project, how exciting!
👓 If you're not clear yet about why the final project is so important, we recommend reading this other article.
To help you choose, you should know that your final project is an effort that integrates and uses all the skills and knowledge that have been taught during the course. 🍒 The cherry 🍰 on top of the cake at the end of the bootcamp. The capstone project is a simulation of a real-life project, and your experience in developing it will likely be similar to your future work within a company.
Before we talk about the project requirements, we consider it more important to let you know how to avoid failure and ensure on-time delivery
If you're studying data science, skip to the next section. After 8 years of seeing final projects succeed or fail, we've compiled the following list of suggestions:
The most common mistake students make is thinking that the quality of your final project is determined by the number of features it has. Nothing could be further from the truth!! The more features you have, the lower the quality of your project. All great products have only a few features.
What will you do? Choose one thing that you want to do well, then you'll realize it will take a lot of work to achieve that functionality 100%. Remember that even the most basic project should include authentication, integration with third-party APIs, signup, login, etc.
To impress an employer, it's better to create innovative projects. Don't be intimidated; it's possible to create innovative projects with a relatively low level of technical difficulty.
🔥 Get inspired by previous projects: Check out this list for ideas and inspiration on how to make your project amazing.
These days, there are too many APIs, packages, and tools that make your work easier. For example:
You should get a lot of feedback from your mentors to make sure you choose an innovative project that you can complete.
If you're studying Full Stack Development, skip to the next section. We've compiled the following list of questions to help you choose a good project:
The most common reason for a prediction project to fail is low-quality or lack of data. We recommend checking Kaggle or HuggingFace to find interesting datasets. You can also ask our mentors for datasets that are well-known and can help you.
It's also highly recommended to have data from a company you work for or that is willing to provide you with the data. This would be very beneficial for your profile, as you would have a real-life dataset and a real-life prediction case on your resume.
Feature engineering is one of the most challenging practices. Before choosing a dataset, discuss with your teacher the challenges it may bring
Since we are in an educational environment, your processing resources will be limited. If you choose large datasets, you will have to wait for hours and even days before getting any useful results. This will happen repeatedly. We recommend validating the size of your dataset and other possible processing considerations with your mentors.
Depending on the program you are studying, you will find different requirements, but in general, all final projects must:
🔥 It's important to convince your peers to join your project, after all, projects are done in groups, and not all ideas will be realized; some students will have to give up their idea to join a teammate's project.
Note: Only read this if you are in a full-stack or web development bootcamp.
🔥 Go here to see a list of the requirements for the final Full-Stack project.
Note: Only read this if you are in a data science, machine learning, or AI bootcamp.
🔥 Go here to see a list of the requirements for the final Machine Learning project.