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Welcome to your Final Project!

Content

Project Description

In this project, you will pick a topic of your choosing and perform an end-to-end analysis using what you have learned. You will apply the statistical or machine learning techniques we have learned over the last few weeks and present your results to all of us as well as to the jury for the HackShow.

Project Goals

  • Ask interesting and thoughful questions and find the data to answer them.
  • Focus on improving in areas and/or tools that are hard for you or learning more about something with which you feel comfortable. You can also learn to use new tools related to data analysis, visualization, etc.
  • Apply the statistical and/or machine learning techniques we have learned.
  • Create useful and clear graphs.
  • Present your insights in a thoughtful, clear and accurate way.

Requirements

  • You must plan your project. That is why creating a Kanban or Trello Board is mandatory.
  • You CAN'T CODE until you project is planned.
  • Create a .gitignore file and include it in your repository.
  • You may be on team (3 people max) or work on your own.
  • You can include ML or statistics, but it is not required to do so, as long as you have a rigorous analysis.
  • You may use the data from your last project or from past projects.

Deliverables

  • A well-commented notebook with your analysis (Jupyter or Zeppelin).
  • A 5 minute presentation in the auditorium (+2 minutes of questions)
  • A 5 minute presentation for the jury (+5 minutes of questions)
  • Repository with your workflow + documentation + code. Even if you are working alone, you need to keep good practices!
  • The dataset where you have kept your data (if possible), as well as a description of it.

Schedule

Wednesday

  • Choose your topic and to work as a team or individually.

Thursday

  • Create your repository and README overview (template provided).
  • Choose the dataset you would like to use.
  • Teacher's validation

NO CODE UNTIL HERE

Monday

  • Data analysis/ model training.

Tuesday

  • Analysis validation/ model fine-tunning.

Wednesday - Thursday

  • Preparation of presentation/paper.

Thursday evening

  • Group rehersal!

Friday morning

  • Jury presentation: they will select their two prefered projects.

Friday evening. HACKSHOW!

  • Presentations at 18:00.
  • Vote for the winner of the HackShow.

Presentation

  • 5 minute presentation for the jury (+5 minutes of questions)
  • 5 minute presentation in Hackshow (+2 minutes of questions)

Tips & Tricks

  • Organize yourself (don't get lost!). Respect deadlines.
  • Ask for help vs Google is your friend.
  • Define a simple approach first. You never know how the data can betray you 😉.
  • Document yourself. Learn about the problem and what research has been done before you.
  • Before making a graph, think what you want to represent.
  • Don't force yourself to use tecniques if they are not helpful for your objective.
  • If using machine learning, remember:
    • This is an iterative process. Try your best to improve your model performance by:
      • Try different models and select one that is the simplest yet produce the best result.
      • Try different hyperparameters and see if they improve the result.