Prework

Ironhack Data Analytics Bootcamp

Introduction

This repository contains the prework for the Ironhack Data Analytics Bootcamp.

If you are an enrolled student in our upcoming bootcamp, you can receive support from our instructional staff while you are working on these challenges. Your Program Manager will send you information about how to reach out to our instructional support team.

If you are not an enrolled student but find our repository in GitHub, feel free to use it for personal, non-commcercial purposes. The codes are provided AS IS without support.

Getting Started

Before starting with the challenges, read the Prework lessons in LMS (Ironhack's student platform) and make sure you've installed all the required software. To complete the Prework exercises, you will mainly need Python 3 and Jupyter Notebook.

To get started, fork the prework-labs repository and clone it to your local file system. Navigate to the repository directory using the command line and then, start Jupyter Notebook.

In the repository, you will find two directories: Python and Statistics.

Python

The Python directory includes a bunch of folders. Each folder is a different challenge and it contains a Jupyter Notebook file (.ipynb). Open the notebook file in browser. Then, follow the step-by-step instructions to solve the challenge in the interactive coding environment.

Solve the challenges following the order suggested below.

Suggested Order Based on Difficulty

1. Snail and well
2. Duel of sorcerers
3. Bus
4. Robin Hood
5. Processor temperature
6. Rock Paper Scissors

Statistics

The statistics challenge consists on completing the Data Science Math Skills course in Coursera, offered by Duke University.

The course teaches the core math that data science is built upon. You'll learn about set theory, math notation, probability theory, etc. The expected duration of the course is 15 hours and you can enroll it for free.

The completion of this course is essential for you to be ready for the statistical module of Ironhack's bootcamp. That is the reason why you are asked to submit all the Practice Exercises and Quizes you will find in the Data Science Math Skills course.

Watch the lesson videos, read the recommended articles and when you feel ready, complete the practice exercises and quizes for each week. When you are done, take screenshots of all the solved exercises and save them for later submission.

Here's the complete list of exercises you need to deliver:

WEEK 1

• Building Blocks for Problem Solving - Practice quiz on Sets (3 questions)
• The infinite World of Real Numbers - Practice quiz on the Number Line, including Inequalities (8 questions)
• That Jagged S Symbol - Practice quiz on Simplification Rules and Sigma Notation (6 questions)
• That Jagged S Symbol - Graded quiz on Sets, Number Line, Inequalities, Simplification, and Sigma Notation (13 questions)

WEEK 2

• Descartes Was Really Smart - Practice quiz on the Cartesian Plane (5 questions)
• Input-Output Machines - Practice quiz on Types of Functions (6 questions)
• Input-Output Machines - Graded quiz on Cartesian Plane and Types of Function (13 questions)

WEEK 3

• This is about that derivative stuff - Practice quiz on Tangent Lines to Functions (2 questions)
• Fast Growth, Slow Growth - Practice quiz on Exponents and Logarithms (12 questions)
• Fast Growth, Slow Growth - Graded quiz on Tangent Lines to Functions, Exponents and Logarithms (13 questions)

WEEK 4

• Basic Probability Definitions - Practice quiz on Probability Concepts (9 questions)
• Problem Solving Methods - Practice quiz on Problem Solving (9 questions)
• Applying Bayes Theorem and the Binomial Theorem - Practice quiz on Bayes Theorem and the Binomial Theorem (9 questions)
• Applying Bayes Theorem and the Binomial Theorem - Probability (basic and Intermediate) Graded Quiz (12 questions)

Deliverables

The files you need to submit are:

• The .ipynb files of each Python challenge including the solutions.
• Screenshots of all the challenges in the Data Science Math Skills course in Coursera.

Submitting Your Work

If you are an enrolled student, you are required to submit your solutions before your course starts. Your Program Manager will send you information on how to submit your work.

Summary

Read the instructions of the prework challenges carefully. Remember that if you have any doubt, you can reach out to Ironhack's instructional support team.

Try your best and good luck!