Repository Overview

This repository contains two Jupyter notebooks that serve as educational resources for understanding different data analysis techniques. These notebooks focus on working with NoSQL databases and parallel computing with the Dask library for scalable data processing. Below is a detailed description of each notebook:

1. NoSQL_MongoDB_Spatial_Students.ipynb

This notebook is dedicated to Spatial NoSQL using MongoDB. It demonstrates how MongoDB's built-in functionalities can be leveraged to efficiently store, query, and manage spatial data (e.g., geographic information).

Key topics and features covered include:

• Introduction to MongoDB: Overview of NoSQL database concepts and the strengths of MongoDB for handling non-relational data.
• MongoDB for Spatial Data: Utilization of MongoDB’s spatial data types and geospatial indexing.
• Data Access and Querying: Practical examples of using MongoDB’s queries to extract and manipulate spatial data, such as finding points within a specific area or calculating distances between locations.

This notebook is a practical guide for anyone interested in working with geospatial data using NoSQL databases and specifically demonstrates how MongoDB can handle spatial data operations.

2. dask_exercise_EDA_students.ipynb

This notebook focuses on Exploratory Data Analysis (EDA) using the Dask library in Python. Dask is a parallel computing library that allows for the manipulation of large datasets in a distributed manner, enabling computations on data that doesn’t fit into memory.

Key aspects covered in this notebook include:

• Introduction to Dask: Understanding Dask and its use for scalable data analysis, along with a comparison to the traditional Pandas library.
• Dask DataFrame Operations: How to perform basic data manipulations (similar to Pandas) using Dask's dataframe module, such as loading, inspecting, filtering, and summarizing data.
• Exploratory Data Analysis (EDA): Conducting initial analysis of datasets, including computing basic statistics, and generating visualizations.

Before running this notebook, you will need to install the Dask library and its dependencies. You can install it using the following command:

pip install dask[dataframe]

This notebook is ideal for students and practitioners who want to explore large datasets efficiently without being limited by memory constraints.

Getting Started

To get started with either of these notebooks, make sure to have the necessary libraries installed in your Python environment. Both notebooks require Python 3.x to run properly. Clone the repository to your local machine using the following command:

git clone https://github.com/spaceie08/NoSQL-Dask.git