ironhack-labs / data-labs

Ironhack Data Analytics Labs

Getting Started

1. Install grip with brew install grip (Mac) or pip install grip (Windows).

2. Start local Markdown server:

$ grip -b README.md 8080 --user <your-github-username> --pass <your-github-password>

💡 grip uses the GitHub Markdown API to render the files in localhost so that you'll see exactly how GitHub would render the Markdown files. Running grip with your Github username and password will allow you to make unrestricted requests to GitHub. If you see error when you run the problem that says GitHub Rate Limit Reached, it's because you didn't run grip with your GitHub credentials or the provided credentials are incorrect.

Working on the Assignments

To work on your first assignment, create a branch of your own with your name (change the branch name unless your name is John Doe):

$ git checkout -b john-doe

Each project/lab has its own directory in which you'll find a README.md file and a sub-directory named your-code. The descriptions and requirements of the assignment can be found in the README file. When you work on the assignment, create your code files in the your-code directory and save regularly while you work.

After you finish, add those files to git, commit, and push your branch to GitHub. In the commit message, specify which lab/project you are submitting. For example:

$ git add <files-to-add>
$ git commit -m "Module 1 MySQL project"
$ git push origin john-doe

The instructional team will review your branch and provide feedback.

To work on the subsequent assignments, keep using the same branch you created and push your new codes to GitHub.

❗ Update your branch regularly because the curriculum development team is developing new assignments for you as the course proceeds. Make sure you have committed all your codes then exectue git pull origin master to obtain the latest code from the master branch.

Happy coding!

data-labs's Issues

DF Calculation Lab Challenge 2 complicated/confusing

Students reported Challenge 2 of the DF Calculation and Transformation is too complicated/confusing.

In this lab it was intended to help students practice the iterative data analysis workflow. I guess the lab is not a good media to cover this type of knowledge.

[MAD 0ct. 2018] Student feedback on labs/projects

Use this ticket to record student feedback from the Madrid Oct. 2018 cohort

New Lab: Week 1 Day 5

ML Pipeline lab improvement

• Need to add the final step to combine all cells so that it's a "pipeline".

• Pickle needs to be explained

module-3/lab-supervised-learning-evaluation

This lab has no solution branch.

Improve Advanced MySQL lab

Add some specific practice questions (with explicit prompts) to let students practice advanced mysql before asking those open-ended questions.

Use screenshots to demo expected lab challenge outputs

Use screenshots to demo expected lab challenge outputs

[data-labs/module-2/lab-bayesian-statistics/your-code/main_solutions.ipynb] Monty Hall

shouldn't be?

priors = [1/3, 1/3, 1/3] # priori
likelihoods = [1/3, 0, 2/3] # posteriori

posteriors = bayes_rule(priors, likelihoods)

print('Probability of winning if you stick with Door A:', posteriors[0])
print('Probability of winning if you switch to Door C:', posteriors[2])