Essential Python Libraries to build your career in Data Science ๐๐
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree
Python Project Ideas: https://t.iss.one/dsabooks/85
Best Resources to learn Python & Data Science ๐๐
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree
Python Project Ideas: https://t.iss.one/dsabooks/85
Best Resources to learn Python & Data Science ๐๐
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more โค๏ธ
ENJOY LEARNING๐๐
โค2
Python Game Development Roadmap
Stage 1 - Learn Python basics (syntax, OOP).
Stage 2 - Study game physics and logic fundamentals.
Stage 3 - Use Pygame to prototype 2D games.
Stage 4 - Add input systems (controllers, keyboard, mouse).
Stage 5 - Add sound effects with PyGame Mixer.
Stage 6 - Explore OpenGL or Panda3D for 3D games.
Stage 7 - Add visual effects (shaders, lighting).
Stage 8 - Package and distribute games with tools like cx_Freeze or PyInstaller.
๐ โ Python Game Developer
Stage 1 - Learn Python basics (syntax, OOP).
Stage 2 - Study game physics and logic fundamentals.
Stage 3 - Use Pygame to prototype 2D games.
Stage 4 - Add input systems (controllers, keyboard, mouse).
Stage 5 - Add sound effects with PyGame Mixer.
Stage 6 - Explore OpenGL or Panda3D for 3D games.
Stage 7 - Add visual effects (shaders, lighting).
Stage 8 - Package and distribute games with tools like cx_Freeze or PyInstaller.
๐ โ Python Game Developer
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ (๐ก๐ผ ๐ฆ๐๐ฟ๐ถ๐ป๐ด๐ ๐๐๐๐ฎ๐ฐ๐ต๐ฒ๐ฑ)
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
๐5โค1
Some useful PYTHON libraries for data science
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook โpylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Pythonโs usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook โpylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Pythonโs usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
โค3๐2๐1
Official Python Docs
https://docs.python.org/3/
Tools:
https://docs.python-guide.org/en/latest/dev/virtualenvs/
https://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
https://www.practicepython.org/
https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
https://docs.python.org/3/faq/gui.html
https://docs.python.org/3/
Tools:
https://docs.python-guide.org/en/latest/dev/virtualenvs/
https://www.pythonforbeginners.com/basics/python-pip-usage
Practice:
https://www.practicepython.org/
https://www.hackerrank.com
https://wiki.python.org/moin/PythonDecorators
Python GUI FAQ
https://docs.python.org/3/faq/gui.html