# Data Science Cheatsheet

## Overview
Data science is an interdisciplinary field that involves the extraction, analysis, and interpretation of data. Here are some fundamental concepts in data science:

- **Data collection:** Data collection is the process of gathering data from various sources. Data can be collected from databases, websites, and other sources.
- **Data cleaning:** Data cleaning is the process of identifying and correcting errors in the data. Data cleaning can be used to ensure that the data is accurate and consistent.
- **Data analysis:** Data analysis is the process of using statistical and computational methods to extract insights from the data. Data analysis can be used to identify patterns and trends in the data.

## Machine Learning
Machine learning is a subset of artificial intelligence that involves the development of algorithms that can learn from data. Here are some fundamental concepts in machine learning:

- **Supervised learning:** Supervised learning is a type of machine learning where the algorithm is trained on labeled data. Supervised learning can be used for tasks such as classification and regression.
- **Unsupervised learning:** Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data. Unsupervised learning can be used for tasks such as clustering and dimensionality reduction.
- **Deep learning:** Deep learning is a type of machine learning that involves the use of neural networks with multiple layers. Deep learning can be used for tasks such as image recognition and natural language processing.

## Data Visualization
Data visualization is the process of representing data graphically. Here are some fundamental concepts in data visualization:

- **Charts and graphs:** Charts and graphs can be used to represent data in a visual format. Examples of charts and graphs include bar charts, line charts, and scatter plots.
- **Dashboards:** Dashboards are visual displays of data that provide an overview of key metrics. Dashboards can be used to monitor performance and identify trends.
- **Interactive visualization:** Interactive visualization allows users to explore data by interacting with visualizations. Interactive visualization can be used to gain insights from data.

## Big Data
Big data refers to datasets that are too large to be processed using traditional methods. Here are some fundamental concepts in big data:

- **Hadoop:** Hadoop is an open-source software framework for processing large datasets. Hadoop can be used to distribute data processing across multiple computers.
- **Spark:** Spark is an open-source software framework for processing large datasets. Spark can be used to process data in memory, making it faster than Hadoop.
- **NoSQL:** NoSQL databases are non-relational databases that can handle large amounts of unstructured data. NoSQL databases can be used for tasks such as real-time analytics and machine learning.

## Resources
- [Kaggle](https://www.kaggle.com/)
- [DataCamp](https://www.datacamp.com/)
- [Towards Data Science](https://towardsdatascience.com/)