Option B: Working Off-Platform

Going off-platform:

If you'd like to use your own environment instead of the pre-configured environment provided on the Coursera platform, you will have to prepare your environment and make sure your directory is set up for Jupyter notebook. Details on how to get started can be found below. We've also included additional resources to help you set up a Jupyter Notebook to clean and analyze tweets needed for this case study. 

Setting up your environment & Getting Started

1.   Check if you have Python installed on your system: Open the Command Prompt (Windows) or Terminal (Mac/Linux) and type python --version or python3 --version. If you see a version number, it means Python is installed. Otherwise, download and install Python from the official website: https://www.python.org/downloads/.

2.   Open the Command Prompt (Windows) or Terminal (Mac/Linux) and type pip install jupyter. This will install Jupyter Notebook and all its dependencies.

3.   Once the installation is complete, type jupyter notebook in the Command Prompt/Terminal and hit enter. This will open a web browser with the Jupyter Notebook dashboard.

4.   Create a new notebook by clicking the "New" button in the upper right corner of the dashboard and selecting "Python 3" or any other kernel of your choice.

Additional Resources

• Jupyter Notebook Quick Start Guide

• Jupyter Notebook: An Introduction

• Installing Jupyter Notebook

• Signing up for a new Github account

• Github: Getting Started

Feeling stuck? Here are some helpful resources

Below are a list of additional resources and some helpful advice in case you get stuck during your project. Feel free to refer back to this section later in your journey if you need support.

Debugging Tips + Helpful Resources

Feeling stuck? Try the following:

• View a task-by-task guide: we put together a task-by-task guide to help you through the multiple stages of this project. With each stage, we've provided additional resources you can explore to help you get unstuck.

• Google your question: oftentimes, someone has had the same question as you! Check out websites like StackOverflow to see how other folks have found solutions.

• Read the documentation: make sure to carefully read through the documentation for any languages and libraries that you are using. Oftentimes they’ll have examples of what you’re looking for! Pandas , Seaborn, and Matplotlib documentation may be especially helpful!

• Rubber ducking: try to explain a problem to a friend or co-worker. Oftentimes you’ll figure out the solution as you’re trying to explain it. And if not, getting another pair of eyes on your code can be helpful.

Check out these helpful resources:

• Matplotlib tutorial

• seaborn examples

• NumPy tutorial

• pandas user guide

Review Your Project Checklist

You might want to know if you're on the right track as you complete your project. Does your project contain everything it needs to solve the case and stand out to potential employers? To answer that question, take a look at the checklist  below.

Check Your Work

Here's a checklist for creating an exceptional an exploratory data analysis:

1. Data Understanding: The learner should demonstrate a deep understanding of the data and the problem being explored. The following should be included:

   • A clear description of the data, including its source and any relevant background information

   • A detailed exploration of the data, including summary statistics and visualizations

   • An explanation of any data cleaning or preprocessing techniques that were applied

2. Data Visualization: The learner should effectively use appropriate visualizations to explore the data and communicate insights. The following should be included:

   • Clear, well-designed visualizations that effectively communicate key insights

   • Appropriate visualizations for the type of data being analyzed

   • Thoughtful design choices, including appropriate labeling, color choices, and formatting

3. Analysis Techniques: The learner should use a variety of appropriate analysis techniques to explore the data and draw insights. The following should be included:

   • A clear explanation of the analysis techniques used and why they were chosen

   • Appropriate statistical tests, models, and algorithms to analyze the data

   • Thoughtful consideration of the limitations and assumptions of the analysis techniques used

4. Insights and Conclusions: The learner should draw insightful conclusions from the data and effectively communicate those conclusions. The following should be included:

   • Clear and well-supported conclusions that provide meaningful insights into the problem being explored

   • Appropriate recommendations or next steps based on the analysis

   • A clear explanation of any limitations or areas for further research