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240701 ||| eng |
| 050 |
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4 |
|a QA76.73.P98
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| 245 |
0 |
0 |
|a Data analysis foundations with Python
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| 250 |
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|a First edition
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| 260 |
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|a Plano, TX
|b Cuantum Technologies LLC.
|c 2023
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| 300 |
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|a 551 pages
|b illustrations
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Programming languages (Electronic computers) / http://id.loc.gov/authorities/subjects/sh85107313
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| 653 |
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|a Data mining / http://id.loc.gov/authorities/subjects/sh97002073
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
|
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|a Exploration de données (Informatique)
|
| 710 |
2 |
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|a Cuantum Technologies LLC.
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
|
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|z 9781836209072
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781836209072/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 005.13/3
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| 520 |
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|a Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects
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