Applied Data Science Capstone Labs

This repository contains my completed laboratory assignments and projects for the IBM Applied Data Science Capstone course. The labs cover a range of essential topics in data science, from data collection and wrangling to exploratory analysis, machine learning, and interactive dashboard development.

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Repository Structure

The labs are organized into individual folders, each corresponding to a specific topic or assignment. This structure is designed to make it easy to navigate and find specific lab materials.

Here's a breakdown of the folder structure:

• Exploratory Analysis Using Pandas and Matplotlib/: Focuses on in-depth data exploration and visualization using Python's Pandas and Matplotlib libraries.
• Exploratory Analysis Using SQL/: Explores data analysis techniques using SQL queries, demonstrating data retrieval and manipulation.
• Hands on Lab Complete the Machine Learning Prediction lab/: Contains work related to building and evaluating machine learning models for prediction tasks.
• Hands-on Lab Build an Interactive Dashboard with Ploty Dash/: Showcases the development of interactive web dashboards using Plotly and Dash for data visualization.
• Hands-on Lab Complete the Data Collection API Lab/: Covers methods for collecting data using various APIs.
• Hands-on Lab Complete the Data Collection with Web Scraping lab/: Demonstrates techniques for extracting data from websites using web scraping.
• Hands-on Lab Data Wrangling/: Addresses the critical process of cleaning, transforming, and preparing raw data for analysis.
• Hands-on Lab Interactive Visual Analytics with Folium lab/: Explores the creation of interactive geospatial visualizations using the Folium library.

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Contents of Each Lab Folder

Each lab folder typically includes:

• Jupyter Notebooks (.ipynb): The primary workspace for code, analysis, and explanations.
• Data Files (.csv, etc.): Any datasets used or generated during the lab.
• Other relevant files: Such as Python scripts, reports, or supporting documentation.

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How to View/Run the Labs

To view and run the Jupyter Notebooks in this repository:

1. Clone the repository:

   git clone [https://github.com/FoxKid404/Applied-Data-Science-Capstone.git](https://github.com/FoxKid404/Applied-Data-Science-Capstone.git)

2. Navigate to the repository directory:

   cd Applied-Data-Science-Capstone

3. Install necessary libraries: Each lab might have specific dependencies. It's recommended to create a virtual environment. You'll likely need to install libraries like pandas, matplotlib, seaborn, scikit-learn, plotly, dash, requests, BeautifulSoup4, folium, and database connectors (e.g., sqlite3, psycopg2-binary).

   pip install pandas matplotlib seaborn scikit-learn plotly dash requests beautifulsoup4 folium

4. Launch Jupyter Lab or Jupyter Notebook:

   jupyter lab
   # OR
   jupyter notebook

5. Open the .ipynb files from the respective lab folders in your browser.

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Contact

Feel free to reach out if you have any questions or feedback.

• GitHub: FoxKid404

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License

This project is open-source and available under the MIT License.