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Practical Data Science with Jupyter
This summary is talking about the Book "Practical Data Science with Jupyter".
The provided text is an excerpt from the book "Practical Data Science with Jupyter" by Prateek Gupta. The book is a comprehensive guide to data science using Python and Jupyter Notebooks. It covers essential concepts in data science, such as data cleaning, pre-processing, feature engineering, and machine learning. The book also includes practical examples and case studies to help readers apply their newfound knowledge to real-world problems. The excerpt provides a detailed overview of the book's content, including chapters on data science fundamentals, software installation, Python data structures, NumPy and Pandas libraries, interacting with databases, statistical concepts, data importing and cleaning, data visualization, supervised and unsupervised machine learning, time-series analysis, best practices for project structuring, and an advanced algorithm called CatBoost.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Practical-Data-Science-Jupyter-Pre-processing/dp/9389898064?&linkCode=ll1&tag=cvthunderx-20&linkId=095bb8e9350938b01b6ca4d76f875086&language=en_US&ref_=as_li_ss_tl
The provided text is an excerpt from the book "Practical Data Science with Jupyter" by Prateek Gupta. The book is a comprehensive guide to data science using Python and Jupyter Notebooks. It covers essential concepts in data science, such as data cleaning, pre-processing, feature engineering, and machine learning. The book also includes practical examples and case studies to help readers apply their newfound knowledge to real-world problems. The excerpt provides a detailed overview of the book's content, including chapters on data science fundamentals, software installation, Python data structures, NumPy and Pandas libraries, interacting with databases, statistical concepts, data importing and cleaning, data visualization, supervised and unsupervised machine learning, time-series analysis, best practices for project structuring, and an advanced algorithm called CatBoost.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Practical-Data-Science-Jupyter-Pre-processing/dp/9389898064?&linkCode=ll1&tag=cvthunderx-20&linkId=095bb8e9350938b01b6ca4d76f875086&language=en_US&ref_=as_li_ss_tl
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By CyberSecurity SummaryThis summary is talking about the Book "Practical Data Science with Jupyter".
The provided text is an excerpt from the book "Practical Data Science with Jupyter" by Prateek Gupta. The book is a comprehensive guide to data science using Python and Jupyter Notebooks. It covers essential concepts in data science, such as data cleaning, pre-processing, feature engineering, and machine learning. The book also includes practical examples and case studies to help readers apply their newfound knowledge to real-world problems. The excerpt provides a detailed overview of the book's content, including chapters on data science fundamentals, software installation, Python data structures, NumPy and Pandas libraries, interacting with databases, statistical concepts, data importing and cleaning, data visualization, supervised and unsupervised machine learning, time-series analysis, best practices for project structuring, and an advanced algorithm called CatBoost.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Practical-Data-Science-Jupyter-Pre-processing/dp/9389898064?&linkCode=ll1&tag=cvthunderx-20&linkId=095bb8e9350938b01b6ca4d76f875086&language=en_US&ref_=as_li_ss_tl
The provided text is an excerpt from the book "Practical Data Science with Jupyter" by Prateek Gupta. The book is a comprehensive guide to data science using Python and Jupyter Notebooks. It covers essential concepts in data science, such as data cleaning, pre-processing, feature engineering, and machine learning. The book also includes practical examples and case studies to help readers apply their newfound knowledge to real-world problems. The excerpt provides a detailed overview of the book's content, including chapters on data science fundamentals, software installation, Python data structures, NumPy and Pandas libraries, interacting with databases, statistical concepts, data importing and cleaning, data visualization, supervised and unsupervised machine learning, time-series analysis, best practices for project structuring, and an advanced algorithm called CatBoost.
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cyber_security_summary
Get the Book now from Amazon:
https://www.amazon.com/Practical-Data-Science-Jupyter-Pre-processing/dp/9389898064?&linkCode=ll1&tag=cvthunderx-20&linkId=095bb8e9350938b01b6ca4d76f875086&language=en_US&ref_=as_li_ss_tl