What tools do I need to learn data science?, What tools do I need to learn data science? |
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What tools do I need to learn data science?, What tools do I need to learn data science? |
Jun 27 2024, 02:07 AM
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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. |
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Szöveges verzió | A pontos idő: 15th October 2024 - 07:28 PM |