What tools do I need to learn data science?, What tools do I need to learn data science? Beállítások

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To learn data science effectively, you'll need to become proficient with a variety of tools and technologies that span different aspects of the field. Here are some essential tools to consider:

Programming Languages
Python: Widely used for data analysis, machine learning, and general-purpose programming.
R: Excellent for statistical analysis and visualization.
Data Analysis and Manipulation
Pandas: A Python library for data manipulation and analysis.
NumPy: A Python library for numerical computations.
Data Visualization
Matplotlib: A Python library for creating static, interactive, and animated visualizations.
Seaborn: Based on Matplotlib, this library provides a high-level interface for drawing attractive statistical graphics.
Tableau: A powerful data visualization tool that can connect to a wide variety of data sources.
Machine Learning and AI
Scikit-learn: A Python library for machine learning that includes simple and efficient tools for data mining and data analysis.
TensorFlow: An open-source library for deep learning and machine learning.
Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow.
Big Data Technologies
Apache Hadoop: A framework for distributed storage and processing of large data sets.
Apache Spark: A unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning, and graph processing.