Invision Power Board -> Hozzászólás a következő témához: What tools do I need to learn data science?

[quote name='armne234' date='Jun 27 2024, 02:07 AM' post='185698']
To [b][url="https://www.sevenmentor.com/data-science-course-in-pune.php"]learn data science[/url][/b] 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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armne234	Hozzászólás Jun 27 2024, 02:07 AM
	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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