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Full-Stack Software Developer - 16w

Data Science and Machine Learning - 16 wks

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Weekly Coding Challenge

Every week, we pick a real-life project to build your portfolio and get ready for a job. All projects are built with ChatGPT as co-pilot!

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Podcast: Code Sets You Free

A tech-culture podcast where you learn to fight the enemies that blocks your way to become a successful professional in tech.

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Pick Your Capstone Project

Reasons for a Full-Stack Project to Fail

You're probably here because you're about to start building your final project, how exciting!

πŸ‘“ 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. πŸ’ 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

Reasons for a Full-Stack Project to Fail

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:

Don't Add Too Many Features

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 does Netflix do? It's a billion-dollar project where you can find a movie and play it.
  • What does Uber do? You request a taxi.
  • What does Instagram do? It has a wall with all the posts of the people you follow.

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.

Don't Build Another Social Network

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.

πŸ”₯ 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 can connect to the Ethereum network and build your own blockchain-based currency or launch an NFT in a few hours.
  • You can send and receive SMS in Python with just 20 lines of code.
  • You can use a QR code reader in a couple of hours.
  • You can program a drone to fly as your code dictates in a few hours.
  • You can use a Raspberry PI with minimal effort and have access to temperature sensors, magnetic field sensors, and more

You should get a lot of feedback from your mentors to make sure you choose an innovative project that you can complete.

Reasons for a Data Science and Machine Learning Project to Fail

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:

What Dataset Do You Have?

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.

Overfitting or Underfitting

Feature engineering is one of the most challenging practices. Before choosing a dataset, discuss with your teacher the challenges it may bring

Processing Capacity

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.

General Requirements for Capstone Projects

Depending on the program you are studying, you will find different requirements, but in general, all final projects must:

  • Be deployed online: Whether it's Heroku, Render, Vercel, Azure, AWS, etc. You must make your project available online under a URL and provide a link so you can include it on your resume as a sample of your work.
  • Be done in groups of 2 to 3 (recommended) people: If you work alone, you will miss out on learning how to collaborate, which is one of the most important requirements in companies. Many best practices won't be necessary either. In the end, your experience won't be like real life.

πŸ”₯ 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.

  • Be uploaded to When you enter your cohort's dashboard, you will find a section to upload information about your final project.
  • Be presented at a GeekTalk: The final presentation is a necessary step. It will allow you to have a video of your project and also force you to set a delivery date and work under pressure. Sometimes, we invite potential employers to GeekTalk who are looking for talent to hire.

Specific Requirements by Program:

For Full-Stack Development:

Note: Only read this if you are in a full-stack or web development bootcamp.

  • Someone should be in charge of making things look good in the project, a CSS, Bootstrap, and React specialist.
  • You should have at least 3 routes, use the Context API, and understand the concepts behind Model, View, and Action in Flux.
  • Integrate with a third-party API.
  • Have an authentication system with JWT or a similar method.
  • Try to keep the project simple with not too many views or features. Think about it: How many features does Netflix or Instagram have? The core set of features is small

πŸ”₯ Go here to see a list of the requirements for the final Full-Stack project.

For Data Science:

Note: Only read this if you are in a data science, machine learning, or AI bootcamp.

  • The most important thing is to choose the dataset. What data do you have?
  • To impress in data science, it's good to implement predictions in areas like health (e.g., detecting pneumonia) or finance (e.g., fraud detection, delinquency, etc.).
  • You should make predictions with real-life data.

πŸ”₯ Go here to see a list of the requirements for the final Machine Learning project.