Autor:Codenga23.07.2024

The Most Important Libraries and Tools Used in Data Science

Data Science is a field that combines data collection and analysis, visualization, and drawing conclusions. Tools that enable data processing play an important role in it. In this article, we will discuss the key tools used in Data Science.

Programming Languages

When it comes to programming languages, two are most commonly used in Data Science: Python and R.

Python is a universal and very versatile language that has a large number of libraries facilitating data work. On the other hand, R is a specialized language focused on statistical analysis and chart visualization. In this article, you will find a more detailed comparison of Python and R.

Python Libraries

Since we mentioned Python libraries, it’s worth discussing them in more detail.

Pandas

An indispensable library for manipulation and analysis of structured data, such as tables and DataFrames.

NumPy

The fundamental library for numerical computations, offering tools for working with multidimensional arrays.

Matplotlib

A tool for creating basic charts and data visualizations.

Seaborn

An extension of Matplotlib, enabling more advanced and aesthetic visualizations.

Scikit-learn

A versatile machine learning library, offering algorithms for classification, regression, and clustering.

TensorFlow

A machine learning and deep learning framework developed by Google, used for building and training neural network models.

Keras

A high-level library based on TensorFlow, simplifying the creation and training of neural network models.

PyTorch

An alternative to TensorFlow, popular in academic and research environments, created by Facebook AI Research.

Data Analysis Tools

Now let’s move on to popular tools supporting data analysis. If you want to learn more about this topic, refer to this article.

Jupyter Notebook

An interactive environment for data analysis, enabling the creation of documents containing code, charts, and comments.

Google Colab

A free cloud-based Jupyter Notebook environment offering additional computational resources.

Apache Spark

An engine for processing large data sets, supporting distributed computations, ideal for working with Big Data.

Databases

Data needs to be stored somewhere. We need a system that allows easy saving and browsing of information. Databases fulfill this role. There are two basic types of databases:

• Relational databases, storing information in tables, usually based on the SQL language.
• Non-relational databases sometimes known as NoSQL. They are used to store unstructured data, which do not have an organized structure.

Visualization

Proper data visualization plays an important role. Good visualization allows us to present the results of our work in a clear way. Here are popular tools for data visualization.

Tableau

Data visualization software enabling the creation of interactive dashboards, widely used in business analysis.

Power BI

A Microsoft tool for business analysis and data visualization, integrating with other Microsoft products.

Plotly

A library for creating interactive charts in Python, enabling the creation of advanced and interactive visualizations.

Summary

Data Science is a very extensive field, covering various aspects of working with data. At each stage of working with data, you can use tools that support our work: from data collection to visualization.