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Ironhack logo

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!

Lab Index

Module 1

Project | Merge Resolving Conflicts

Lab | Entity Relationship Diagram

Project | MySQL

Lab | Numpy Deep Dive

Lab | Pandas Deep Dive

Lab | Data Cleaning

Project | Pandas Project

Lab | Python List Comprehension & Error Handling

Lab | Python String Operations & Functional Programming

Project | Web Data Pipeline

Lab | API Scavenger Game

Lab | Web Scraping

Project | API and Web Data Scraping

Module 2

Lab | Subsetting and descriptive stats

Lab | Dataframe Calculation and Transformation

Lab | Pivot Table and Correlations

Project | Data Analysis with Pandas

Lab | DataViz with Seaborn & Matplotlib

Project | Visualizing Real World Data

Project | Storytelling with Data Visualizations

Lab | Probability Distribution

Lab | Hypothesis Testing

Project | Calculating Game of Chances

Lab | Introduction to BI and Tableau

Lab | DataViz with Tableau

Lab | BI with Tableau

Project | Tableau Project

Module 3

Lab | Intro to Machine Learning

Lab | Feature Extraction and Introduction to Supervised Learning

Lab | Unsupervised Learning with Scikit-Learn

Lab | Supervised Learning with Scikit-Learn

Project | Supervised Learning

Project | Unsupervised Learning (Clustering)

Lab | Machine Learning Pipelines

Lab | Supervised Learning

Lab | Unsupervised Learning

Project | Machine Learning Pipeline

Advanced Topics: Network Analysis

Advanced Topics: Topic Modeling

Advanced Topics: Apache Spark