Top 10 Python Libraries for Data Science & Machine Learning
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
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1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
Data Science Resources for Beginners
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Share with credits: https://news.1rj.ru/str/datasciencefun
ENJOY LEARNING 👍👍
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Learn Data Science in 2025
𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
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𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚
Pareto's Law states that "that 80% of consequences come from 20% of the causes".
This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.
Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.
But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).
For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.
So, invest more time learning topics that provide immediate value now, not a year later.
𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡
There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.
Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.
So, find a mentor who can teach you practical knowledge in data science.
𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️
If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.
Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
ENJOY LEARNING 👍👍
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The New Data Scientist of 2025:
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
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- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
- Business background
- Self-educated
- Knows enough SQL
- Knows enough Python
- Knows enough machine learning
- Uses Microsoft Excel
- Uses AI to be productive
- Title isn't "Data Scientist"
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